Comparative Study of Heuristic vs. Metaheuristic Optimization in Pension Fund Management

Comparative Study of Heuristic vs. Metaheuristic Optimization in Pension Fund Management | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 September 2025 V1 Latest version Share on Comparative Study of Heuristic vs. Metaheuristic Optimization in Pension Fund Management Author : Muhammad Abubakar 0009-0000-6005-6549 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175795272.25097599/v1 163 views 112 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Pension fund management requires balancing long-term security with dynamic market uncertainties, making optimization techniques essential for investment allocation and risk control. Traditional heuristic approaches, such as rule-based allocation and greedy algorithms, offer simplicity and computational efficiency but often struggle to adapt to nonlinear financial environments. In contrast, metaheuristic methods, including Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, provide more robust search capabilities, enabling the exploration of complex solution spaces with higher accuracy. This paper presents a comparative study of heuristic and metaheuristic optimization methods in pension fund management, evaluating their effectiveness across parameters such as risk-return trade-offs, convergence speed, scalability, and adaptability to market volatility. Experimental results highlight that while heuristics remain valuable for real-time decision-making with limited data, metaheuristics outperform them in delivering optimized portfolio allocations under uncertain conditions. The study contributes to financial engineering by offering insights into how hybrid models can leverage the strengths of both approaches to enhance fund sustainability and ensure more reliable retirement outcomes. Supplementary Material File (comparative study of heuristic vs. metaheuristic optimization in pension fund management.pdf) Download 194.24 KB Information & Authors Information Version history V1 Version 1 15 September 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords financial engineering genetic algorithm heuristic optimization metaheuristic algorithms particle swarm optimization pension fund management portfolio allocation Authors Affiliations Muhammad Abubakar 0009-0000-6005-6549 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 163 views 112 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Muhammad Abubakar. Comparative Study of Heuristic vs. Metaheuristic Optimization in Pension Fund Management. Authorea . 15 September 2025. DOI: https://doi.org/10.22541/au.175795272.25097599/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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