Abstract
Particle swarm optimization (PSO) has been actively studied as an effective search method using populations. The basis of search algorithms using particle swarms is efficient convergence from exploiting the ability to move toward the best individual (Gbest) within the current population. Therefore, PSO has the property of quickly converging to the optimal solution, especially for unimodal search problems. For multimodal search problems, there are also reported methods, such as Standard PSO (SPSO), which divides a population into several subgroups, and research examples in which several particles are used to improve the local search capability. However, using a part of the particles in populations for a local search increases the search time in environments with fewer particles because the force toward Gbest of the entire population tends to be relatively weaker than that of the original PSO. To solve this problem, this paper proposes a search method in which all particles toward to Gbest while conducting a local search using their respective past best solutions, instead of dividing some particles into local searches. Using typical benchmark functions and existing algorithms, we show that the proposed method works well even with a small number of particles.
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Acknowledgment
This work was supported by JSPS KAKENHI Grant Numbers JP19K12162, JP22K12185, and the Education Department Scientific Research Program Project of Hubei Province of China (Q20222208).
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Sato, Y., Yamashita, Y., Guo, J. (2023). PSO with Local Search Using Personal Best Solution for Environments with Small Number of Particles. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_10
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DOI: https://doi.org/10.1007/978-3-031-36622-2_10
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