Fusing Exploration and Exploitation: Hybrid Fireworks–Whale Optimization for Multi-Objective Independent Task Scheduling in Cloud Environments

Fusing Exploration and Exploitation: Hybrid Fireworks–Whale Optimization for Multi-Objective Independent Task Scheduling in Cloud Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fusing Exploration and Exploitation: Hybrid Fireworks–Whale Optimization for Multi-Objective Independent Task Scheduling in Cloud Environments Abdullah Nayem Wasi Emran, Majisha Jahan Disha, Rezwana Reaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6807131/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Task scheduling in cloud computing environments is a complex, multi-objective optimization problem that requires balancing conflicting goals such as minimizing execution time and cost while maximizing resource utilization and throughput. Metaheuristic algorithms have proven effective for tackling such problems due to their adaptability and global search capabilities. In this work, we propose a novel family of hybrid scheduling algorithms that combine the strengths of two well-established metaheuristics: the Fireworks Algorithm (FWA)—known for its diverse exploration of the search space—and the Whale Optimization Algorithm (WOA)—recognized for its effective local exploitation. Uniquely, we design four distinct hybridization strategies: Sequential Hybrid, Parallel Hybrid, FWA-Encircling Hybrid, and WOA-Spark Hybrid, each integrating FWA and WOA in a fundamentally different manner. To the best of our knowledge, this is the first systematic study to explore multiple hybridization pathways between two metaheuristic algorithms for cloud task scheduling. Comprehensive experiments on synthetic benchmark workloads as well as the real-world GoCJ trace demonstrate that our hybrid algorithms outperform recent advanced FWA (OBDFWA), modified WOA (MWOA), and the high-performing hybrid GA–GWO methods in key metrics, such as makespan, resource utilization, and cost. Our methods achieve 2.5% to 7.5% improvements in fitness scores. Statistical validation using Friedman's rank test and the Nemenyi post-hoc test further confirms the superiority and robustness of our hybrids. Overall, this work introduces a comprehensive and pioneering hybridization framework for multi-objective cloud task scheduling. Cloud computing Fireworks Algorithm Hybrid Optimization Metaheuristic algorithms Task scheduling Whale Optimization Algorithm Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviews received at journal 02 Oct, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Submission checks completed at journal 03 Jun, 2025 First submitted to journal 03 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6807131","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472535915,"identity":"fbd36992-7f94-45c3-8ce8-a83a148a3cdc","order_by":0,"name":"Abdullah Nayem Wasi 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