Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909715744686080 |
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| author | Urbańczyk, Piotr Urbańczyk, Aleksandra Król, Magdalena Rutkowski, Leszek Kisiel-Dorohinicki, Marek |
| author_facet | Urbańczyk, Piotr Urbańczyk, Aleksandra Król, Magdalena Rutkowski, Leszek Kisiel-Dorohinicki, Marek |
| contents | The goal of this paper is twofold. First, it explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive manner in comparison with their standard basic form: Genetic Algorithm and Particle Swarm Optimization. The algorithms were tested on a set of benchmark functions, including Ackley, Griewank, Levy, Michalewicz, Rastrigin, Schwefel, and Shifted Rotated Weierstrass, across multiple dimensions. The experimental results demonstrate that the hybrid approaches achieve superior convergence and consistency, especially in higher-dimensional search spaces. The second goal of this paper is to introduce a novel consecutive hybrid PSO-GA evolutionary algorithm that ensures continuity between PSO and GA steps through explicit information transfer mechanisms, specifically by modifying GA's variation operators to inherit velocity and personal best information. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_00229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics Urbańczyk, Piotr Urbańczyk, Aleksandra Król, Magdalena Rutkowski, Leszek Kisiel-Dorohinicki, Marek Neural and Evolutionary Computing Optimization and Control 90C59 (Primary), 90C27, 68T20, 68W10 (Secondary) I.2.8; I.2.6; G.1.6; F.2.1; I.6.6 The goal of this paper is twofold. First, it explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive manner in comparison with their standard basic form: Genetic Algorithm and Particle Swarm Optimization. The algorithms were tested on a set of benchmark functions, including Ackley, Griewank, Levy, Michalewicz, Rastrigin, Schwefel, and Shifted Rotated Weierstrass, across multiple dimensions. The experimental results demonstrate that the hybrid approaches achieve superior convergence and consistency, especially in higher-dimensional search spaces. The second goal of this paper is to introduce a novel consecutive hybrid PSO-GA evolutionary algorithm that ensures continuity between PSO and GA steps through explicit information transfer mechanisms, specifically by modifying GA's variation operators to inherit velocity and personal best information. |
| title | Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics |
| topic | Neural and Evolutionary Computing Optimization and Control 90C59 (Primary), 90C27, 68T20, 68W10 (Secondary) I.2.8; I.2.6; G.1.6; F.2.1; I.6.6 |
| url | https://arxiv.org/abs/2508.00229 |