Holistic swarm optimization: A novel metaphor-less algorithm guided by whole population information for addressing exploration-exploitation dilemma

•Introduces Holistic Swarm Optimization (HSO), a novel metaphorless optimization algorithm.•HSO utilizes entire population data for robust and informed search processes.•Balances exploration and exploitation dynamically using adaptive mutation and selection.•Demonstrates superior performance across...

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Vydané v:	Computer methods in applied mechanics and engineering Ročník 445; s. 118208
Hlavní autori:	Akbari, Ebrahim, Rahimnejad, Abolfazl, Gadsden, Stephen Andrew
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.10.2025
Predmet:	Adaptive mutation Metaphor-less optimization algorithms Population-based algorithms Simulated annealing-based selection Swarm optimization Swarm optimization Population-based algorithms Metaphor-less optimization algorithms Adaptive mutation Simulated annealing-based selection
ISSN:	0045-7825
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Popis
Shrnutí:	•Introduces Holistic Swarm Optimization (HSO), a novel metaphorless optimization algorithm.•HSO utilizes entire population data for robust and informed search processes.•Balances exploration and exploitation dynamically using adaptive mutation and selection.•Demonstrates superior performance across diverse benchmark and real-world mechanical design problems.•Promotes development of straightforward optimization methods over metaphor-based algorithms. [Display omitted] This paper introduces a novel metaphor-less optimization algorithm called Holistic Swarm Optimization (HSO), designed to enhance the search process by utilizing data from the entire population. Unlike conventional algorithms that rely on partial or local information, HSO adopts a comprehensive approach, ensuring that each decision is informed by the overall distribution and fitness landscape of the population. The algorithm dynamically balances exploration and exploitation through an adaptive framework that integrates root-mean-squared (RMS) fitness-based displacement coefficients, simulated annealing-based selection, and adaptive mutation. This structure enables HSO to efficiently navigate complex, multimodal optimization problems while avoiding local optima. The performance of HSO is evaluated on two widely used benchmark test suites–CEC 2005 and CEC 2014–and a series of real-world engineering design problems. Results show that HSO delivers competitive and stable performance when compared to several state-of-the-art metaphor-based and metaphor-less algorithms. These findings demonstrate the effectiveness of a holistic population-guided approach in achieving robust optimization outcomes, making HSO a promising alternative for solving diverse and challenging problems without reliance on metaphorical inspirations. The source codes and implementation guidance for the HSO algorithm are available for public access on the https://github.com/ebrahimakbary/HSO.
ISSN:	0045-7825
DOI:	10.1016/j.cma.2025.118208