This work presents a simple but effective modification of the velocity updating formula in the Particle Swarm Optimization algorithm to improve the performance of the algorithm on multi-modal problems. The well-known issue of premature swarm convergence is addressed by a repulsive mechanism implemented on a single-particle level where each particle in the population is partially repulsed from a different particle. This mechanism manages to prolong the exploration phase and helps to avoid many local optima. The method is tested on well-known and typically used benchmark functions, and the results are further tested for statistical significance.
CITATION STYLE
Pluhacek, M., Senkerik, R., Viktorin, A., & Kadavy, T. (2018). Particle swarm optimization with single particle repulsivity for multi-modal optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10841 LNAI, pp. 486–494). Springer Verlag. https://doi.org/10.1007/978-3-319-91253-0_45
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