Title: Particle swarm optimisation with dynamic search strategies based on rank correlation

Authors: Toshiki Nishio; Jun-ichi Kushida; Akira Hara; Tetsuyuki Takahama

Addresses: Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima, 731-3194 , Japan ' Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima, 731-3194 , Japan ' Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima, 731-3194 , Japan ' Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima, 731-3194 , Japan

Abstract: The paper presents particle swarm optimisation with dynamic search strategies (DSS-PSO). In order to control search strategies, we introduce landscape modality estimation method using correlation coefficients between rankings of search points to PSO. This estimation method utilises relationship between fitness and distance to a reference points. By using estimated result, we stochastically classify whether the landscape modality is uni-modal or multi-modal and population is moving toward outside or not. Our proposal method can switch the strategies properly according to the landscape modality of an objective function. If the landscape modality is estimated as multi-modal, elitist learning strategy is performed. If the landscape modality is estimated as ridge, multi-dimensional mutation and mutation by differential vector are performed. Furthermore, inertia weight for velocity vector is controlled according to the estimated landscape modality. To confirm the search ability of the proposal method, we conducted experiments using standard benchmark functions. The experimental results show that the proposal method outperforms other PSO variants.

Keywords: particle swarm optimisation; PSO; landscape modality; Spearman's rank correlation.

DOI: 10.1504/IJCISTUDIES.2017.089521

International Journal of Computational Intelligence Studies, 2017 Vol.6 No.4, pp.311 - 332

Available online: 23 Jan 2018 *

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