Abstract
This paper introduces a novel parallel way to combine different meta-heuristic algorithms by using the island model, which is called Hybrid Island Metaheuristic Algorithm (HIMA). This parallel hybridization structure can improve the diversity of the whole algorithm and combine the features of different algorithms together. In this paper, three traditional algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Fireworks Algorithm (FWA) have been used to build three different HIMA algorithms, PSO-GAs (HIMA-PGA), FWA-GAs (HIMA-FGA) and FWA-PSO-GAs (HIMA-FPGA). The performance of the proposed algorithms is compared to that of the traditional Island GA and to that of the others as well. All three HIMA algorithms show a better result quality compared to the island GA. Moreover, the experiment results comfirm that the sub-PSO improves the convergence speed of the algorithm while the sub-FWA can improve the result quality on some proposed functions.
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Li, J., Gonsalves, T. (2022). A Hybrid Approach for Metaheuristic Algorithms Using Island Model. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 3. FTC 2021. Lecture Notes in Networks and Systems, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-030-89912-7_24
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DOI: https://doi.org/10.1007/978-3-030-89912-7_24
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