Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement

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While written tests assess academic achievement, they do not fully capture skills like critical thinking, creativity, public speaking, and physical fitness. The proposed paper suggests a holistic optimization model involving a Hybrid PSO-GA (Particle Swarm Optimization - Genetic Algorithm) metaheuristic algorithm that would generate a more holistic student assessment platform that constitutes a fitness function that would evaluate students not only on exams but also on academic, co-curricular and extracurricular facets. The model formulates a constrained optimization problem where cognitive effort allocation is optimized under individual workload limits derived from factors such as cognitive capacity, time availability, fatigue, and task complexity. Proposed model evaluated in C++ and analyzed statistically in Python across three setups with population sizes of 25, 50, and 100, and up to 200 iterations. A previous survey-based study was considered in formulating datasets, and normalized cognitive workload scores were applied to five categories of tasks. The hybrid algorithm outperformed standalone Genetic Algorithm and Particle Swarm Optimization methods, achieving the highest mean fitness of 1.273 and the lowest standard deviation of 0.004. Statistical significance was confirmed using the Friedman and Nemenyi tests with p-values less than 0.0001, demonstrating the robustness and superior convergence of the hybrid model. The proposed approach provides a computationally intelligent and statistically robust framework for holistic student performance assessment, with implications for adaptive academic workload management and policy-driven educational enhancement." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1344", "name": "Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student..." } } ] } Home Browse Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article P N G PhaniKumar P, Jena JJ, Srinadhraju S and Sagiraju S. Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.12688/f1000research.171008.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] Pitchika P N G PhaniKumar https://orcid.org/0009-0000-7882-6345 1 , Junali Jasmine Jena 1 , Sagiraju Srinadhraju 2 , Sudhirvarma Sagiraju 2 Pitchika P N G PhaniKumar https://orcid.org/0009-0000-7882-6345 1 , Junali Jasmine Jena 1 , Sagiraju Srinadhraju 2 , Sudhirvarma Sagiraju 2 PUBLISHED 01 Dec 2025 Author details Author details 1 School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India 2 Department of Computer Science & Engineering, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, India Pitchika P N G PhaniKumar Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – Original Draft Preparation Junali Jasmine Jena Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing Sagiraju Srinadhraju Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation Sudhirvarma Sagiraju Roles: Conceptualization, Formal Analysis, Methodology, Resources, Validation, Writing – Original Draft Preparation OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract Students aim for well-rounded performance, and higher education institutions strive to support them. While written tests assess academic achievement, they do not fully capture skills like critical thinking, creativity, public speaking, and physical fitness. The proposed paper suggests a holistic optimization model involving a Hybrid PSO-GA (Particle Swarm Optimization - Genetic Algorithm) metaheuristic algorithm that would generate a more holistic student assessment platform that constitutes a fitness function that would evaluate students not only on exams but also on academic, co-curricular and extracurricular facets. The model formulates a constrained optimization problem where cognitive effort allocation is optimized under individual workload limits derived from factors such as cognitive capacity, time availability, fatigue, and task complexity. Proposed model evaluated in C++ and analyzed statistically in Python across three setups with population sizes of 25, 50, and 100, and up to 200 iterations. A previous survey-based study was considered in formulating datasets, and normalized cognitive workload scores were applied to five categories of tasks. The hybrid algorithm outperformed standalone Genetic Algorithm and Particle Swarm Optimization methods, achieving the highest mean fitness of 1.273 and the lowest standard deviation of 0.004. Statistical significance was confirmed using the Friedman and Nemenyi tests with p-values less than 0.0001, demonstrating the robustness and superior convergence of the hybrid model. The proposed approach provides a computationally intelligent and statistically robust framework for holistic student performance assessment, with implications for adaptive academic workload management and policy-driven educational enhancement. READ ALL READ LESS Keywords student performance, survey, optimization, metaheuristic, hybrid Corresponding Author(s) Junali Jasmine Jena ( [email protected] ) Close Corresponding author: Junali Jasmine Jena Competing interests: No competing interests were disclosed. Grant information: The authors acknowledge that this publication received partial funding support from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 P N G PhaniKumar P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: P N G PhaniKumar P, Jena JJ, Srinadhraju S and Sagiraju S. Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.12688/f1000research.171008.1 ) First published: 01 Dec 2025, 14 :1344 ( https://doi.org/10.12688/f1000research.171008.1 ) Latest published: 01 Dec 2025, 14 :1344 ( https://doi.org/10.12688/f1000research.171008.1 ) 1. Introduction Education is widely recognized as a fundamental human right essential for personal and national development. Over the years, education systems have faced numerous challenges and changes in their approaches. Today, institutions are increasingly focused on retaining students and providing engaging, effective teaching. However, assessing students’ abilities in various areas remains a challenge, as many institutions still heavily rely on written exams for evaluation. This narrow focus can overlook important skills, leaving students unprepared in areas beyond academics. A potential solution is to adopt smarter, data-driven methods for evaluating students’ strengths and weaknesses. This would enable faster and more personalized assessments through computational intelligence. Constrained optimization problems often transform subjective human judgments into structured mathematical models. Classic NP-hard problems ( Karp, 1972 ), such as the Traveling Salesman Problem (TSP) ( Sharma, 2024 ), optimize routing for scheduling and deliveries, while the Knapsack Problem ( He et al., 2024 ) determines optimal selections under constraints. Boolean satisfiability (SAT) ( Boulebnane & Montanaro, 2024 ) deals with binary variable conditions, and MAX-SAT ( Das et al., 2025 ) focuses on maximizing satisfied clauses. These mathematical frameworks enable the systematic evaluation of subjective tasks, such as student assessments, for objective decision-making. Metaheuristic algorithms are powerful optimization techniques inspired by natural processes, designed to efficiently tackle complex problems where traditional methods are inadequate. They balance exploration and exploitation to achieve near-optimal solutions. For instance, the Genetic Algorithm ( Tang et al., 1996 ) simulates natural selection, while Particle Swarm Optimization ( Kennedy & Eberhart, 1995 ) models collective behaviour. Teaching-Learning-Based Optimization (TLBO) ( Rao et al., 2012 ) is based on the teacher-student knowledge transfer mechanism, Ant Colony Optimization ( Dorigo et al., 1996 ) replicates how ants use pheromone trails to find optimal paths, and Light Spectrum Optimization ( Abdel-Basset et al., 2022 ) is inspired by light dispersion. Due to their adaptability and effectiveness, these methods are widely applied across various domains. The structure of the paper is as follows: Introduction section and Literature Review section examines prior research on the topic, providing a critical evaluation of different perspectives while highlighting their strengths and limitations. Problem Definition section establishes the problem framework and presents a mathematical formulation, including an objective function, a constraint equation, and equations for evaluating effectiveness and stress levels. Datasets section details the datasets compiled from various studies and survey papers. Hybrid PSO-GA approach section introduces a novel metaheuristic algorithm designed to address the problem. In Results and Discussion section, multiple simulations are conducted to assess the algorithm’s performance in terms of optimization accuracy and computational efficiency, with results ranked using standard statistical techniques. Finally, Conclusion summarizes the key contributions and outlines potential directions for future research. 2. Literature review This section will review several recent papers published addressing the student evaluation and prediction problem through the application of computational intelligence. To begin with, Lakshmi et al. ( Miranda Lakshmi et al., 2013 ) conducted a study that explored the use of genetic algorithms as an effective method for analyzing complex educational data ( Batool et al., 2023 ). By applying principles of natural evolution, their model identified significant factors affecting student performance through a quantitative evaluation of various academic metrics, such as theoretical, mathematical, and practical scores. This approach not only assisted educational institutions in improving teaching quality by analyzing student marks but also served as a valuable tool for classifying and examining performance determinants throughout the academic year. The findings indicated that even minor adjustments to the genetic algorithm’s parameters could yield significant insights into key performance indicators. Ultimately, the model provided students with a self-assessment mechanism to better understand their academic standing and identify areas for improvement, underscoring the effectiveness of genetic algorithms in predicting student performance and enhancing educational development across various contexts. Another similar study, done by Hamsa et al. ( Hamsa et al., 2016 ) developed a hybrid model that combines decision tree algorithms with a fuzzy genetic algorithm to predict student academic performance ( Yağcı, 2022 ) in bachelor’s and master’s programs. By analyzing various parameters such as internal marks, sessional scores, and admission scores, the model evaluates each student’s subject-specific outcomes and provides educators with a valuable tool to promote academic success ( Jin, 2023 ). The decision tree ( “Predictive Analytics in Business Analytics,” 2022 ) component effectively identifies at-risk students, enabling instructors to offer additional support to improve final exam results, while the fuzzy genetic algorithm classifies more students as passing by accommodating borderline cases, reassuring them and allowing for indirect monitoring of their progress. This balanced approach fosters a supportive learning environment ( Niu et al., 2022 ) and helps high-performing students attract early recruitment from reputable companies, ultimately benefiting both the students and the institution’s reputation. Gomede et al. ( Gomede et al., 2018 ) developed a computational intelligence model ( Ikegwu et al., 2024 ) to improve education quality and support decision-making, particularly in regions with limited access to quality education ( Timotheou et al., 2023 ). Using data science and mining techniques ( Shu & Ye, 2023 ), the model generated personalized knowledge profiles, helping teachers monitor key performance indicators (KPIs) ( Joppen et al., 2019 ) and make informed decisions. Based on real K–9 student data from a private school in Brazil, it utilized graph-based visualization, recommendation systems, and random forest algorithms for classification ( Shaik & Srinivasan, 2019 ) and prediction ( Schonlau & Zou, 2020 ). This approach enabled performance forecasting, identified key links, and provided tailored recommendations to enhance learning outcomes. Operating within a PDCA (Plan, Do, Check, Act) cycle, the model improved prediction accuracy and aligned indicators with educational system goals. Furthermore, in a paper by Taylan et al. ( Taylan et al., 2017 ), the authors analyzed learning objectives for graduate and program-level industrial engineering students by developing the “house of cognitive learning,” which emphasizes cognitive depth, outcome components, and conceptual understanding. To address challenges in assessment and measurement, they proposed a “learning index” derived using an integrated integer programming model. This index was refined through statistical methods and quality control charts to provide a comprehensive and longitudinal view of student progress ( Ifenthaler & Yau, 2020b , 2020a ). Their findings underscored the importance of clearly defined learning objectives and shared instructor-student expectations in driving effective curriculum outcomes ( Ifenthaler et al., 2019 ). The study reinforced the growing role of data-driven course design in enhancing learning impact, while also promoting self-directed learning and personalized feedback mechanisms to foster continuous student engagement and responsibility. Agaarna et al. ( Michael & Amenawon, 2015 ) analyzed factors influencing academic performance in world-class universities using a linear programming model ( Mayer, 2022 ). Key elements included entry points, student-to-staff ratio, library spending, accommodation quality, teaching assessments, research ratings, and international student presence. Using the simplex method ( Huiberts et al., 2022 ) and MAPLE14 software, the model standardized these factors to assess their significance. Results showed teaching assessment as the most critical factor, followed by entry points. The study underscored the importance of high teaching quality, library engagement, and proper admission criteria. A comprehensive understanding of these variables was found to be essential for optimizing student outcomes in higher education. 3. Problem definition We consider a set of task categories indexed by i ∊ {1,2, …, n }, each associated with a cognitive load value c i . The objective is to optimize the allocation of effort x i to maximize the total cognitive benefit, formulated as: (1) max ∑ i = 1 n x i . c i Subject to the following constraint: (2) ∑ i = 1 n x i ≤ λ ∑ i = 1 n c i where λ represents an individual’s personalized cognitive load limit. This parameter is determined based on multiple factors to ensure a balanced cognitive workload. Specifically, λ is a function of: • Cognitive Capacity (Baseline) Assessed through historical academic performance or aptitude scores. • Time Availability Measured in terms of structured task hours available per day. • Fatigue & Stress Levels Evaluated through self-reported metrics, including stress surveys, sleep quality, and mental fatigue levels. • Task Complexity & Adaptability Determined via problem-solving assessments reflecting adaptability to cognitive challenges. For this study, we focus on task categories that have quantifiable cognitive workload based on survey-derived satisfaction metrics. These categories are: • Academic Performance Mapped to: Memory & Learning, Analytical & Problem-Solving • Assignments & Projects Mapped to: Memory & Learning, Analytical & Problem-Solving • Co-curricular & Extra-curricular Engagement Mapped to: Social & Communication, Multitasking • Clubs & Societies Mapped to: Social & Communication • Sports & Cultural Participation Mapped to: Physical & Sensorimotor, Social & Communication These categories were selected based on strong representation in the dataset and clear association with distinct cognitive domains, enabling an evidence-based approach to modeling student workload and performance. 4. Materials and methods 4.1 Datasets Cognitive load value (c i ): The dataset utilized in this study was extracted from Table 2 , Table 3 , and Table 4 of a survey-based study conducted by Yangdon et al. ( Karma et al., 2021 ). The mean satisfaction scores for each category, based on Likert-scale responses, were normalized using Equation 3 to map the values onto a scale from 0 to 1. Here, M denotes the average satisfaction rating for a given category. To represent cognitive workload instead of satisfaction, the normalized values were inverted by subtracting them from 1. (3) C i = 1 − M − 1 4 Five academic task categories were considered in this study, denoted as C 1 to C 5 . C 1 corresponds to Academic Performance, while C 2 represents Assignments & Projects. Co-curricular & Extra-curricular Engagement is categorized under C 3 , whereas C 4 refers to Clubs & Societies. Finally, C 5 is assigned to Sports & Cultural Participation. Each task category is mapped to one or more cognitive domains, forming the basis for workload estimation and cognitive effort distribution in this study. The values taken from the survey paper are shown in Table 1 and the resulting workload scores are summarized in Table 2 . Table 1. Mapped academic task categories with associated cognitive domains, source reference, and computed workload values. Category Cognitive domain(s) Source Table/Section Workload (0–1) Academic Performance Memory & Learning, Analytical & Problem-Solving Table 4 (M = 3.42) 0.57 Assignments & Projects Memory & Learning, Analytical & Problem-Solving Table 4 (M = 2.83) 0.71 Co-curricular & Extra-curricular Engagement Social & Communication, Multitasking Table 2 (Recreation M = 3.08) 0.65 Clubs & Societies Social & Communication Table 3 (Tutor Support M = 3.16) 0.68 Sports & Cultural Participation Physical & Sensorimotor, Social & Communication Table 2 (Sports M = 3.25) 0.65 Table 2. Cognitive workload values (c i ) for each category (C 1 to C 5 ). C 1 C 2 C 3 C 4 C 5 0.57 0.71 0.65 0.68 0.65 Personal cognitive limit (λ): The personal cognitive limit λ was assumed to be influenced by four key factors: cognitive capacity, time availability, fatigue & stress levels, and task complexity & adaptability. These factors were represented as l 1, l 2, l 3 , and l 4 , respectively. Since precise values for these factors were unavailable, they were randomly chosen within a reasonable range for the scope of this study. The cognitive limit λ was computed using the following equation: (4) λ = ∑ α i l i where α i represents the weighting coefficients assigned to each factor which in this study is considered to be 0.25. For this study, 3 student’s data have been randomly generated as shown in Table 3 and will be used in experiments. Table 3. Limit values of student S1, S2 and S3 generated at random. Student l 1 l 2 l 3 l 4 S1 0.36 0.73 0.45 0.71 S2 0.25 0.45 0.61 0.63 S3 0.23 0.32 0.12 0.87 4.2 Hybrid PSO-GA approach The Hybrid PSO-GA algorithm synergizes the exploration capabilities of Genetic Algorithms (GA) with the exploitation strengths of Particle Swarm Optimization (PSO) to achieve more robust optimization. Each particle in the population acts as a self-contained agent that holds several key pieces of memory throughout the search process. These include its current position in the solution space, its velocity , its personal best position (the most optimal solution it has found so far), the fitness value of that best position, and crucially, the iteration index at which it achieved this personal best. This extra memory component allows the algorithm to keep track of stagnation at an individual level. The entire structure of a single particle in the whole population can be seen in Figure 1 . A hyperparameter called the iteration threshold determines how long a particle can go without improving before triggering an alternative search behaviour. Figure 1. Structure of a particle in Hybrid PSO-GA. When a particle exceeds this stagnation threshold, it is subjected to Genetic Algorithm operations instead of standard PSO updates. Specifically, the particle is selected for crossover with another randomly chosen particle from the population. A subset of genes (position values) is swapped between the two using a defined crossover probability and window size. After crossover, each gene has a chance to undergo mutation, governed by a mutation probability. Once the new candidate solutions are formed, they are passed through a repair function r(x) to ensure that all variables remain within the problem’s constraints of [0,1], which basically is a slightly modified sigmoid function as shown in Equation 5 . The fitness of these modified solutions is then evaluated. This GA-based route reinjects diversity into the population and allows the algorithm to escape local optima. (5) r ( x ) = 1 1 + e − ( x − 0.5 ) . c If a particle has not yet stagnated, it continues to evolve using the conventional PSO dynamics. The velocity is updated based on its inertia and the difference vectors between its current position and both its personal best and the global best found in the population, with some randomness injected to encourage exploration. The new position is computed, repaired if needed, and evaluated for fitness. If the new fitness surpasses its personal best, the personal best is updated along with the iteration in which the improvement occurred. Simultaneously, the global best is also updated whenever a particle exceeds the previous best. This hybrid framework ensures a balance between local refinement and global exploration, leading to higher resilience against premature convergence and better performance in complex, multimodal search landscapes. The whole flowchart of the algorithm can be seen in Figure 2 . Figure 2. Flowchart displaying the functionality of Hybrid PSO-GA. Algorithm 1. Hybrid PSO-GA Optimization (Condensed). 1: Init particle population: positions, velocities 2: Evaluate fitness, set personal and global bests 3: for each generation do 4: for each particle p do 5: if no improv. for p in threshold iterations then 6: Select particle q randomly 7: Crossover p and q with P c 8: Mutate p’ s genes with P m 9: Repair invalid genes using r(x) 10: Evaluate fitness of p, q 11: else 12: Update p’ s velocity and position (PSO) 13: Repair and evaluate fitness 14: end if 15: if fitness( p ) > personal best then 16: Update personal best, note iteration 17: end if 18: if fitness( p ) > global best then 19: Update global best 20: end if 21: end for 22: end for 5. Results and discussion The simulations were implemented in C++ with GCC version 14.2.1, while Python 3.12 was employed for the statistical analysis of the results. The tests were conducted on a system with an Intel Core i7-1255U processor and 16GB of RAM, running Fedora Linux 40. The final fitness values were ranked using a standard ranking method, preserving the initial values for each algorithm to ensure a fair comparison. This approach allowed for an objective assessment of each algorithm’s performance, providing a detailed overview of their efficiency and convergence. Each sub-experiment was executed 50 times to ensure a statistically valid comparison between all the algorithms, resulting in a total of 3 × 3 × 50 = 450 simulation runs across all configurations. 5.1 Experiment set S-1 Experiment A (Population: 25, Iterations: 50): The first experiment in Set S-1 evaluates algorithmic performance under a limited computational budget, using a population size of 25 and 50 iterations. The results show a clear advantage for the Hybrid PSO-GA algorithm over the standalone GA and PSO variants. It achieves the highest average fitness value of 1.254 and maintains the lowest standard deviation of 0.015, highlighting both its superior optimization capability and enhanced stability across five problem cases. To validate these observations statistically, a non-parametric Friedman test was conducted. The test yielded a Chi-Square value of 75.9192 with a p-value of 0.0000, indicating that the observed performance differences among the three algorithms are statistically significant at the 95% confidence level ( p < 0.05). Subsequently, the Nemenyi posthoc test was applied to evaluate pairwise significance. The resulting p-values demonstrated that Hybrid PSO-GA significantly outperforms both GA and PSO individually, with p-values of 4.07 × 10 −14 and 8.63 × 10 −13 respectively. However, no significant difference was observed between GA and PSO ( p = 0.9156). This highlights the strength of the hybrid strategy, especially in lower-resource environments. The Nemenyi matrix and the convergence trend are shown in Figures 3 and 4 , respectively. Detailed fitness results are reported in Table 4 . Figure 3. Experiment S1-A Nemenyi test matrix. Figure 4. Experiment S1-A convergence graph. Table 4. Fitness values for experiment A (S-1). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.071 0.678 0.341 0.457 0.251 1.217 0.025 3 PSO 0.046 0.649 0.369 0.488 0.246 1.219 0.023 2 Hybrid PSO-GA 0.013 0.914 0.136 0.605 0.150 1.254 0.015 1 Experiment B (Population: 50, Iterations: 100): In the second experiment, the population and iteration counts were doubled to examine scalability and robustness in a more resource-rich environment. With a population of 50 and 100 iterations, the Hybrid PSO-GA continued to outperform the standalone GA and PSO, achieving the highest mean fitness of 1.265 and the lowest standard deviation of 0.009, further reinforcing its robustness. The Friedman test for this setup produced a Chi-Square value of 81.5758 and a p-value of 0.0000, indicating statistically significant differences in algorithm performance. The Nemenyi test showed that Hybrid PSO-GA significantly surpassed GA ( p = 2.88 × 10 −13 ) and PSO ( p = 5.66 × 10 −11 ), while the difference between GA and PSO remained statistically insignificant ( p = 0.6079). These results demonstrate the hybrid algorithm’s consistent advantage, even when more iterations and population diversity are available. The Nemenyi significance matrix and the convergence curve are shown in Figures 5 and 6 , while the numeric results are reported in Table 5 . Figure 5. Experiment S1-B Nemenyi test matrix. Figure 6. Experiment S1-B convergence graph. Table 5. Fitness values for experiment B (S-1). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.021 0.885 0.218 0.570 0.113 1.243 0.016 3 PSO 0.005 0.910 0.218 0.551 0.133 1.251 0.013 2 Hybrid PSO-GA 0.006 0.929 0.029 0.785 0.076 1.265 0.009 1 Experiment C (Population: 100, Iterations: 200): The third and final experiment in Set S-1 investigates performance under the maximum resource allocation—100 individuals over 200 iterations. Once again, Hybrid PSO-GA achieved the best fitness results, with a mean of 1.273 and a remarkably low standard deviation of 0.004. This emphasizes the algorithm’s convergence efficiency and reliability under extended search conditions. The Friedman test produced a Chi-Square value of 85.2400 and a p-value of 0.0000, confirming the significance of the observed differences. The Nemenyi test revealed that Hybrid PSO-GA significantly outperforms GA ( p = 6.43 × 10 −12 ) and PSO ( p = 2.74 × 10 −10 ), while GA vs PSO remained statistically non-significant ( p = 0.4863). Figures 7 and 8 display the statistical and convergence analyses. Table 6 presents the corresponding quantitative results. Figure 7. Experiment S1-C Nemenyi test matrix. Figure 8. Experiment S1-C convergence graph. Table 6. Fitness values for experiment C (S-1). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.009 0.925 0.111 0.755 0.018 1.259 0.013 3 PSO 0.002 0.928 0.005 0.859 0.035 1.270 0.005 2 Hybrid PSO-GA 0.003 0.981 0.007 0.832 0.008 1.273 0.004 1 Summary of experiment set S-1: Across all three configurations in Experiment Set S-1, the Hybrid PSO-GA consistently outperforms both GA and PSO in terms of mean fitness and stability (as measured by standard deviation). Table 7 summarizes the average ranks, with Hybrid PSO-GA maintaining a perfect harmonic rank of 1.0 across all experiments. Table 7. Rank summary for experiment set S-1. Algorithm A Rank B Rank C Rank Harmonic Mean Rank GA 3 3 3 3.00 PSO 2 2 2 2.00 Hybrid PSO-GA 1 1 1 1.00 5.2 Experiment set S-2 Experiment A (Population: 25, Iterations: 50): The first experiment in Set S-2 assesses algorithmic efficiency under constrained computational conditions, using a small population of 25 over 50 iterations. The Hybrid PSO-GA again demonstrates superior performance, yielding the highest average fitness of 1.074 and the lowest standard deviation of 0.019. This suggests a strong capability for both exploration and exploitation even with limited resources. The Friedman test reported a Chi-Square value of 79.8071 and a p-value of 0.0000, affirming statistically significant differences between the three algorithms. The Nemenyi posthoc analysis further validated these differences, with Hybrid PSO-GA showing significant superiority over GA ( p = 1.11 × 10 −16 ) and PSO ( p = 1.73 × 10 −10 ). However, the difference between GA and PSO was not statistically significant ( p = 0.1386). Figures 9 and 10 present the Nemenyi matrix and the convergence trend respectively. The corresponding fitness values are listed in Table 8 . Figure 9. Experiment S2-A Nemenyi test matrix. Figure 10. Experiment S2-A convergence graph. Table 8. Fitness values for experiment A (S-2). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.107 0.545 0.268 0.389 0.204 1.019 0.045 3 PSO 0.037 0.547 0.291 0.324 0.339 1.039 0.027 2 Hybrid PSO-GA 0.013 0.782 0.148 0.428 0.190 1.074 0.019 1 Experiment B (Population: 50, Iterations: 100): The second experiment expands the search horizon with a population of 50 and 100 iterations. Hybrid PSO-GA maintained its lead with a mean fitness of 1.089 and a minimal standard deviation of 0.011. These results further indicate its ability to adapt and scale effectively with larger computational budgets. The Friedman test yielded a Chi-Square value of 77.5600 and a p-value of 0.0000, confirming statistically significant performance variation. The Nemenyi posthoc analysis confirmed that Hybrid PSO-GA significantly outperformed GA ( p = 3.33 × 10 −16 ) and PSO ( p = 6.25 × 10 −11 ), while the GA-PSO comparison remained statistically non-significant ( p = 0.2456). Figures 11 and 12 illustrate the significance matrix and convergence behavior. Table 9 details the numerical results. Figure 11. Experiment S2-B Nemenyi test matrix. Figure 12. Experiment S2-B convergence graph. Table 9. Fitness values for experiment B (S-2). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.050 0.826 0.231 0.295 0.150 1.064 0.020 3 PSO 0.015 0.856 0.174 0.354 0.159 1.074 0.016 2 Hybrid PSO-GA 0.008 0.910 0.074 0.476 0.102 1.089 0.011 1 Experiment C (Population: 100, Iterations: 200): The final experiment in Set S-2 evaluates algorithmic performance under the highest computational allowance: 100 individuals across 200 iterations. Here, Hybrid PSO-GA again achieved the top performance, reporting a mean fitness of 1.098 and an impressively low standard deviation of 0.005, reflecting highly stable convergence. The Friedman test indicated a Chi-Square value of 89.4400 with a p-value of 0.0000, highlighting statistically significant differences. The Nemenyi test showed Hybrid PSO-GA to be significantly better than both GA ( p = 0.0000) and PSO ( p = 6.42 × 10 −8 ). Even the GA vs PSO comparison reached significance ( p = 4.25 × 10 −4 ), suggesting improved resolution of differences at higher resources. Figures 13 and 14 depict the statistical and convergence visuals, and the fitness scores are shown in Table 10 . Figure 13. Experiment S2-C Nemenyi test matrix. Figure 14. Experiment S2-C convergence graph. Table 10. Fitness values for experiment C (S-2). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.030 0.838 0.146 0.483 0.069 1.080 0.012 3 PSO 0.001 0.928 0.040 0.529 0.074 1.093 0.006 2 Hybrid PSO-GA 0.005 0.980 0.048 0.525 0.017 1.098 0.005 1 Summary of experiment set S-2: Across the entire Experiment Set S-2, the Hybrid PSO-GA consistently demonstrated the highest average fitness and the lowest standard deviation, confirming both its optimization strength and reliability under varying resource conditions. Table 11 presents the rank summary, where the hybrid method once again maintained an optimal harmonic mean rank of 1.0. Table 11. Rank summary for experiment set S-2. Algorithm A Rank B Rank C Rank Harmonic Mean Rank GA 3 3 3 3.00 PSO 2 2 2 2.00 Hybrid PSO-GA 1 1 1 1.00 5.3 Experiment set S-3 Experiment A (Population: 25, Iterations: 50): Experiment 3-A evaluates algorithmic efficiency in low-resource conditions, using a population size of 25 and 50 iterations. Hybrid PSO-GA again demonstrated superior optimization, achieving the highest mean fitness value of 0.863 with the lowest standard deviation of 0.014, indicating stable and effective performance under constraints. The Friedman test reported a Chi-Square value of 77.6382 and a p-value of 0.0000, confirming statistically significant performance differences. The Nemenyi posthoc test established that Hybrid PSO-GA significantly outperformed GA ( p = 5.55 × 10 −16 ) and PSO ( p = 4.44 × 10 −11 ), while GA and PSO showed no statistically significant difference ( p = 0.2909). Figures 15 and 16 show the significance heatmap and the convergence graph respectively. Detailed fitness values are provided in Table 12 . Figure 15. Experiment S3-A Nemenyi test matrix. Figure 16. Experiment S3-A convergence graph. Table 12. Fitness values for experiment A (S-3). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.039 0.618 0.224 0.210 0.093 0.810 0.034 3 PSO 0.029 0.446 0.190 0.374 0.177 0.826 0.023 2 Hybrid PSO-GA 0.034 0.881 0.110 0.154 0.064 0.863 0.014 1 Experiment B (Population: 50, Iterations: 100): In Experiment 3-B, the population size and iterations were doubled, increasing the search horizon. Hybrid PSO-GA remained the top performer, achieving a mean fitness of 0.874 and a standard deviation of 0.008, indicating reliable convergence with higher resource allocation. The Friedman test produced a Chi-Square value of 79.8400 and a p-value of 0.0000, revealing statistically significant differences among the algorithms. According to the Nemenyi posthoc analysis, Hybrid PSO-GA significantly outperformed GA ( p = 0.0000) and PSO ( p = 4.66 × 10 −10 ), while GA and PSO remained statistically similar ( p = 0.0712). Figures 17 and 18 depict the statistical and convergence results. Corresponding numerical results are shown in Table 13 . Figure 17. Experiment S3-B Nemenyi test matrix. Figure 18. Experiment S3-B Convergence Graph. Table 13. Fitness values for experiment B (S-3). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.021 0.753 0.116 0.269 0.053 0.839 0.028 3 PSO 0.007 0.816 0.078 0.262 0.075 0.861 0.012 2 Hybrid PSO-GA 0.005 0.960 0.042 0.183 0.058 0.874 0.008 1 Experiment C (Population: 100, Iterations: 200): The final experiment in Set S-3 tests scalability under maximum computational allowance. Hybrid PSO-GA once again led with a mean fitness of 0.880 and a very low standard deviation of 0.003, reflecting strong optimization capabilities and stability at scale. The Friedman test yielded a Chi-Square value of 91.0000 with a p-value of 0.0000, confirming significance. The Nemenyi test validated Hybrid PSO-GA’s advantage over both GA ( p = 0.0000) and PSO ( p = 1.14×10 −7 ). Interestingly, even GA and PSO showed significant differences in this configuration ( p = 1.87 × 10 −4 ). Figures 19 and 20 show the test results, with the raw performance scores summarized in Table 14 . Figure 19. Experiment S3-C Nemenyi test matrix. Figure 20. Experiment S3-C convergence graph. Table 14. Fitness values for experiment C (S-3). Algorithm C1 C2 C3 C4 C5 Mean Standard deviation Rank GA 0.015 0.937 0.097 0.143 0.041 0.861 0.017 3 PSO 0.001 0.949 0.061 0.191 0.049 0.875 0.006 2 Hybrid PSO-GA 0.004 0.998 0.016 0.219 0.014 0.880 0.003 1 Summary of experiment set S-3: Throughout Experiment Set S-3, Hybrid PSO-GA outperformed both GA and PSO in all configurations, maintaining the highest mean fitness and lowest variance. The harmonic mean rank across the experiments as shown in Table 15 further emphasizes its consistent superiority. Table 15. Rank Summary for experiment Set S-3. Algorithm A Rank B Rank C Rank Harmonic Mean Rank GA 3 3 3 3.00 PSO 2 2 2 2.00 Hybrid PSO-GA 1 1 1 1.00 6. Conclusion This paper explores the use of metaheuristic algorithms for constrained decision-making, focusing on student performance optimization. Six key areas are identified for student workload optimization: Cognitive Capacity, Time Availability, Fatigue & Stress Levels and Task Complexity & Adaptability. These adaptable areas provide a flexible framework for optimization across various contexts. A Hybrid PSO-GA (Particle Swarm Optimization - Genetic Algorithm) approach was employed to balance effort allocation among these areas. Multiple metaheuristic algorithms were tested and compared, with final results presented as the harmonic mean of multiple simulation runs to ensure reliability. The statistical analysis in this study was conducted across three experimental sets (S-1, S-2, and S-3), each executed under three configurations with varying population sizes (25, 50, 100) and iteration counts (50, 100, 200). Each set compared the performance of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the proposed Hybrid PSO-GA. The evaluation metrics included mean fitness value, standard deviation, and algorithmic rank, while statistical significance was assessed using the Friedman test and Nemenyi posthoc test. In all experiments, the Hybrid PSO-GA consistently achieved the highest mean fitness and the lowest standard deviation, maintaining a harmonic mean rank of 1.0 throughout. In Experiment Set S-1, the hybrid algorithm attained a peak mean fitness of 1.273 with a standard deviation of 0.004. Similar patterns were observed in Sets S-2 and S-3, where the maximum fitness values reached 1.098 and 0.880, respectively. The Friedman test results indicated statistically significant differences in performance (p-value = 0.0000) across all configurations. The Nemenyi test further confirmed that the Hybrid PSO-GA significantly outperformed both GA and PSO. At the same time, the performance gap between GA and PSO remained statistically insignificant in most cases, except under maximum computational resources. These findings collectively demonstrated the hybrid algorithm’s robustness, scalability, and superior optimization performance across various student workload evaluation scenarios. Future research should focus on empirical validation with diverse datasets, refining weight calibration, and comparing traditional assessment models. Enhancing computational efficiency through selective optimization and parameter tuning could improve scalability. Additionally, integrating machine learning for automated calibration and analyzing longitudinal data may further refine dynamic constraints. Cross-domain applications in employee assessment and personalized healthcare could extend this framework’s utility. Ethical considerations This research was conducted in accordance with the highest standards of academic integrity and ethical responsibility. The study utilized only anonymized secondary data, with no direct involvement of human participants. As such, issues of informed consent or potential risk to individuals do not arise, and formal ethical approval was not required. All datasets were handled responsibly, ensuring confidentiality, fairness, and compliance with institutional and international research ethics guidelines. The reporting of this study maintains transparency, accuracy, and integrity, in line with best practices for responsible research conduct. The authors declare adherence to ethical guidelines for responsible research conduct, including avoidance of plagiarism, proper attribution of intellectual property, and accurate reporting of results. Data availability The authors confirm that the data supporting the findings of this study are available within the article and have also been uploaded to the Figshare data repository. • Student Cognitive Workload Dataset: This dataset contains the cognitive load capacities of students and is available at DOI: https://doi.org/10.6084/m9.figshare.30172204.v2 ( PhaniKumar et al., 2025a ). • Students’ Maximum Cognitive Capacity Dataset: This dataset contains the maximum cognitive capacities of three students, which were generated randomly for this study, and is available at DOI: https://doi.org/10.6084/m9.figshare.30172207.v2 ( PhaniKumar et al., 2025b ). Data are available under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). References Abdel-Basset M, Mohamed R, Sallam KM, et al. : Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm. Mathematics. 2022; 10 (19): 3466. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 01 Dec 2025 ADD YOUR COMMENT Comment Author details Author details 1 School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India 2 Department of Computer Science & Engineering, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, India Pitchika P N G PhaniKumar Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – Original Draft Preparation Junali Jasmine Jena Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing Sagiraju Srinadhraju Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation Sudhirvarma Sagiraju Roles: Conceptualization, Formal Analysis, Methodology, Resources, Validation, Writing – Original Draft Preparation Competing interests No competing interests were disclosed. Grant information The authors acknowledge that this publication received partial funding support from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 01 Dec 2025, 14:1344 https://doi.org/10.12688/f1000research.171008.1 Copyright © 2025 P N G PhaniKumar P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article P N G PhaniKumar P, Jena JJ, Srinadhraju S and Sagiraju S. Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.12688/f1000research.171008.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 01 Dec 2025 Views 0 Cite How to cite this report: Nachouki M. Reviewer Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r452140 ) The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-452140 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Feb 2026 Mirna Nachouki , Ajman University, Ajman, Ajman, United Arab Emirates Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.188543.r452140 The manuscript proposes a hybrid Particle Swarm Optimization–Genetic Algorithm (PSO–GA) approach to optimize a student's “effort allocation” model, intended to support a more holistic assessment beyond written exams. The paper is generally readable and includes a broad literature ... Continue reading READ ALL The manuscript proposes a hybrid Particle Swarm Optimization–Genetic Algorithm (PSO–GA) approach to optimize a student's “effort allocation” model, intended to support a more holistic assessment beyond written exams. The paper is generally readable and includes a broad literature review of metaheuristics and educational data mining. However, validation is required for core educational constructs such as “student performance enhancement,” “cognitive workload,” and “comprehensive assessment.” The experimental design relies on very small simulated cases (three “students” with randomly generated limits), which makes it difficult to support claims about improving real-world student performance. The authors describe the implementation environment (C++/Python versions and hardware), the experimental settings (population sizes, iterations, and 50 runs), and provide a concise description of the algorithm. The interpretation is overly p-value-centered, with limited discussion of effect size and practical significance, and the statistical conclusions do not resolve construct validity issues. The results support the narrow claim that the proposed hybrid strategy achieves higher mean fitness than standalone GA/PSO across the authors’ simulated setups. They do not adequately support stronger claims about real-world “comprehensive student performance enhancement” or policy implications because the dataset and variables, along with the validation, are insufficiently grounded in real-world educational outcome measures. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Education Data Mining I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Nachouki M. Reviewer Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r452140 ) The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-452140 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Nguyen TL. Reviewer Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r444483 ) The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-444483 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 02 Feb 2026 Tung Linh Nguyen , Electric Power University, Ha Noi, Vietnam Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.188543.r444483 Dear Author, anh Editor The topic is potentially interesting, and the paper makes an effort to use statistical comparisons. However, in its current form the study is not yet scientifically sound because (i) the optimization model and variables are ... Continue reading READ ALL Dear Author, anh Editor The topic is potentially interesting, and the paper makes an effort to use statistical comparisons. However, in its current form the study is not yet scientifically sound because (i) the optimization model and variables are not convincingly linked to the claimed educational outcome, (ii) key inputs (notably λ) are generated randomly and the evaluation is based on very small simulated cases, (iii) the “hybrid” contribution and novelty are not sufficiently established, and (iv) reproducibility is incomplete. Problem formulation lacks construct validity The objective function is essentially linear in the decision variables, and the meaning of the decision vector (effort/time/priority) is not operationalized with units, measurement procedure, or evidence that maximizing the chosen utility corresponds to “performance enhancement.” Moreover, mapping satisfaction scores into cognitive workload by inversion/normalization is a strong assumption that is not validated. Required actions: Clearly define what each decision variable represents (time, credits, effort units), justify the functional form of the utility, and provide evidence/validation for the transformation from satisfaction to workload (or replace it with a validated workload/performance construct). Consider a multi-objective formulation (e.g., performance gain vs. overload/health constraints) if the educational claim is “comprehensive.” Study design is undermined by randomly generated key parameters and too-small evaluation. λ is central to feasibility and outcomes, yet the manuscript states that the underlying factors used to compute λ are assigned randomly for the simulated students due to lack of accurate data. As a result, the conclusions about “student performance enhancement” cannot be supported with credible evidence. Required actions: Use real participant-level data for λ and inputs (ideally a reasonably sized cohort), or at minimum provide a rigorous sensitivity analysis (λ distribution, factor weights, ranges) and demonstrate robustness. Expand beyond a toy case (e.g., 3 students) to multiple realistic instances and report variability. “Hybrid PSO–GA” novelty and technical contribution are not sufficiently established. The method appears to be a switching strategy (PSO with occasional GA operations upon stagnation) rather than a tightly integrated cooperative hybrid. Similar stagnation-triggered mutation/crossover strategies exist in the metaheuristics literature. Required actions: Precisely define the hybridization scheme, position it relative to prior hybrid PSO–GA/memetic PSO work, and articulate the distinct novelty. Include stronger baselines (e.g., DE, CMA-ES, advanced PSO variants) and ensure fair hyperparameter tuning across methods. Reproducibility and transparency are insufficient. To allow full replication, readers need complete access to source data, preprocessing steps, random seeds, parameter settings, and ideally the code (or detailed pseudocode sufficient to reimplement). With key inputs generated randomly, the pipeline is currently not reproducible in a meaningful sense. Required actions: Provide the exact dataset used for all results (including survey-to-parameter mapping), full algorithm parameters (population size, inertia schedule, stagnation threshold, crossover/mutation probabilities, repair operator details), random seeds, and a runnable implementation or a fully specified algorithmic description. Statistical analysis: partially appropriate, but interpretation needs strengthening. Using Friedman/Nemenyi across repeated runs is a reasonable start. However, interpretation should go beyond p-values; effect sizes, confidence intervals, and practical significance should be reported. Also, statistical significance does not compensate for weak experimental design (randomized inputs). Required actions: Report effect sizes (e.g., median differences, Cliff’s delta where applicable), confidence intervals via bootstrapping, and discuss practical significance. Clearly describe how rankings are computed and ensure assumptions are met. Improve clarity of key figures (e.g., particle/solution structure) and rewrite any ambiguous mathematical expressions (repair operator, constraints) with consistent notation. Add a notation table for all symbols and parameters. Tighten the literature review to focus on educational modeling/measurement validity and state-of-the-art metaheuristics relevant to the proposed hybrid. I recommend major revision . The manuscript could become publishable if the authors (1) redesign/validate the modeling assumptions, (2) replace random key inputs with real data or robust sensitivity/robustness analyses, (3) strengthen novelty claims and benchmarking, and (4) provide full reproducibility materials. Thanks Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Metaheuristic optimization (PSO/GA), computational intelligence, optimization modeling and benchmarking, applied statistics for algorithm evaluation. AI algorithms are applied to multi-objective optimization problems with many uncertain components, such as in energy systems, transportation systems, etc. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Nguyen TL. Reviewer Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r444483 ) The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-444483 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Beligiannis GN. Reviewer Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r438565 ) The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-438565 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Dec 2025 Grigorios N Beligiannis , University of Patras - Agrinio Campus, Agrinio, Peloponnisos Dytiki Ellada ke Ionio, Greece Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.188543.r438565 The Figure presenting the particle is confusing. It is not clear whta the structure of the particle is. The algorithm is not really hybrid, since PSO and GA are used alone and not in a cooperation scheme. Results ... Continue reading READ ALL The Figure presenting the particle is confusing. It is not clear whta the structure of the particle is. The algorithm is not really hybrid, since PSO and GA are used alone and not in a cooperation scheme. Results show that using both algorithms achieves better results than using them alone. This is not a surprise! Point out the advantages of the proposed method and compare it with other methods (different from GA and PSO). Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Intelligent Optimization Algorithms I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Beligiannis GN. Reviewer Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r438565 ) The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-438565 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 01 Dec 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 1 01 Dec 25 read read read Grigorios N Beligiannis , University of Patras - Agrinio Campus, Agrinio, Greece Tung Linh Nguyen , Electric Power University, Ha Noi, Vietnam Mirna Nachouki , Ajman University, Ajman, United Arab Emirates Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Nachouki M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Feb 2026 | for Version 1 Mirna Nachouki , Ajman University, Ajman, Ajman, United Arab Emirates 0 Views copyright © 2026 Nachouki M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript proposes a hybrid Particle Swarm Optimization–Genetic Algorithm (PSO–GA) approach to optimize a student's “effort allocation” model, intended to support a more holistic assessment beyond written exams. The paper is generally readable and includes a broad literature review of metaheuristics and educational data mining. However, validation is required for core educational constructs such as “student performance enhancement,” “cognitive workload,” and “comprehensive assessment.” The experimental design relies on very small simulated cases (three “students” with randomly generated limits), which makes it difficult to support claims about improving real-world student performance. The authors describe the implementation environment (C++/Python versions and hardware), the experimental settings (population sizes, iterations, and 50 runs), and provide a concise description of the algorithm. The interpretation is overly p-value-centered, with limited discussion of effect size and practical significance, and the statistical conclusions do not resolve construct validity issues. The results support the narrow claim that the proposed hybrid strategy achieves higher mean fitness than standalone GA/PSO across the authors’ simulated setups. They do not adequately support stronger claims about real-world “comprehensive student performance enhancement” or policy implications because the dataset and variables, along with the validation, are insufficiently grounded in real-world educational outcome measures. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Education Data Mining I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Nachouki M. Peer Review Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r452140) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-452140 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Nguyen T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Feb 2026 | for Version 1 Tung Linh Nguyen , Electric Power University, Ha Noi, Vietnam 0 Views copyright © 2026 Nguyen T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Author, anh Editor The topic is potentially interesting, and the paper makes an effort to use statistical comparisons. However, in its current form the study is not yet scientifically sound because (i) the optimization model and variables are not convincingly linked to the claimed educational outcome, (ii) key inputs (notably λ) are generated randomly and the evaluation is based on very small simulated cases, (iii) the “hybrid” contribution and novelty are not sufficiently established, and (iv) reproducibility is incomplete. Problem formulation lacks construct validity The objective function is essentially linear in the decision variables, and the meaning of the decision vector (effort/time/priority) is not operationalized with units, measurement procedure, or evidence that maximizing the chosen utility corresponds to “performance enhancement.” Moreover, mapping satisfaction scores into cognitive workload by inversion/normalization is a strong assumption that is not validated. Required actions: Clearly define what each decision variable represents (time, credits, effort units), justify the functional form of the utility, and provide evidence/validation for the transformation from satisfaction to workload (or replace it with a validated workload/performance construct). Consider a multi-objective formulation (e.g., performance gain vs. overload/health constraints) if the educational claim is “comprehensive.” Study design is undermined by randomly generated key parameters and too-small evaluation. λ is central to feasibility and outcomes, yet the manuscript states that the underlying factors used to compute λ are assigned randomly for the simulated students due to lack of accurate data. As a result, the conclusions about “student performance enhancement” cannot be supported with credible evidence. Required actions: Use real participant-level data for λ and inputs (ideally a reasonably sized cohort), or at minimum provide a rigorous sensitivity analysis (λ distribution, factor weights, ranges) and demonstrate robustness. Expand beyond a toy case (e.g., 3 students) to multiple realistic instances and report variability. “Hybrid PSO–GA” novelty and technical contribution are not sufficiently established. The method appears to be a switching strategy (PSO with occasional GA operations upon stagnation) rather than a tightly integrated cooperative hybrid. Similar stagnation-triggered mutation/crossover strategies exist in the metaheuristics literature. Required actions: Precisely define the hybridization scheme, position it relative to prior hybrid PSO–GA/memetic PSO work, and articulate the distinct novelty. Include stronger baselines (e.g., DE, CMA-ES, advanced PSO variants) and ensure fair hyperparameter tuning across methods. Reproducibility and transparency are insufficient. To allow full replication, readers need complete access to source data, preprocessing steps, random seeds, parameter settings, and ideally the code (or detailed pseudocode sufficient to reimplement). With key inputs generated randomly, the pipeline is currently not reproducible in a meaningful sense. Required actions: Provide the exact dataset used for all results (including survey-to-parameter mapping), full algorithm parameters (population size, inertia schedule, stagnation threshold, crossover/mutation probabilities, repair operator details), random seeds, and a runnable implementation or a fully specified algorithmic description. Statistical analysis: partially appropriate, but interpretation needs strengthening. Using Friedman/Nemenyi across repeated runs is a reasonable start. However, interpretation should go beyond p-values; effect sizes, confidence intervals, and practical significance should be reported. Also, statistical significance does not compensate for weak experimental design (randomized inputs). Required actions: Report effect sizes (e.g., median differences, Cliff’s delta where applicable), confidence intervals via bootstrapping, and discuss practical significance. Clearly describe how rankings are computed and ensure assumptions are met. Improve clarity of key figures (e.g., particle/solution structure) and rewrite any ambiguous mathematical expressions (repair operator, constraints) with consistent notation. Add a notation table for all symbols and parameters. Tighten the literature review to focus on educational modeling/measurement validity and state-of-the-art metaheuristics relevant to the proposed hybrid. I recommend major revision . The manuscript could become publishable if the authors (1) redesign/validate the modeling assumptions, (2) replace random key inputs with real data or robust sensitivity/robustness analyses, (3) strengthen novelty claims and benchmarking, and (4) provide full reproducibility materials. Thanks Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Metaheuristic optimization (PSO/GA), computational intelligence, optimization modeling and benchmarking, applied statistics for algorithm evaluation. AI algorithms are applied to multi-objective optimization problems with many uncertain components, such as in energy systems, transportation systems, etc. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Nguyen TL. Peer Review Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r444483) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-444483 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Beligiannis G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Dec 2025 | for Version 1 Grigorios N Beligiannis , University of Patras - Agrinio Campus, Agrinio, Peloponnisos Dytiki Ellada ke Ionio, Greece 0 Views copyright © 2025 Beligiannis G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The Figure presenting the particle is confusing. It is not clear whta the structure of the particle is. The algorithm is not really hybrid, since PSO and GA are used alone and not in a cooperation scheme. Results show that using both algorithms achieves better results than using them alone. This is not a surprise! Point out the advantages of the proposed method and compare it with other methods (different from GA and PSO). Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Intelligent Optimization Algorithms I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Beligiannis GN. Peer Review Report For: Hybrid PSO-GA Metaheuristic Optimization for Comprehensive Student Performance Enhancement [version 1; peer review: 3 not approved] . F1000Research 2025, 14 :1344 ( https://doi.org/10.5256/f1000research.188543.r438565) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1344/v1#referee-response-438565 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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