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Modified particle swarm optimization for multimodal functions and its application

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Abstract

In this paper, a Modified variant of Particle Swarm Optimization named MPSOPR is proposed. The velocity update in MPSOPR follows a neighbourhood-based learning strategy based on PageRank (PR) algorithm and a scale-free network is proposed for the interaction among particles in the population. This is in contrast to the basic PSO which has a fully connected topology or regular topology. The inclusion of these two modifications helps in enhancing the diversity and the information dissemination ability of the algorithm. Performance of MPSOPR is validated on a set of 17 benchmark problems divided into 3 groups, on basis of level of difficulties. Comparative analysis of the results obtained through MPSOPR with 9 other variants of PSO, indicate that the proposed scheme can help in improving the performance of PSO significantly. The performance of MPSOPR is further validated by employing it for solving problems related to recommender system.

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  1. Department of ASE, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Neetu Kushwaha & Millie Pant

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Kushwaha, N., Pant, M. Modified particle swarm optimization for multimodal functions and its application. Multimed Tools Appl 78, 23917–23947 (2019). https://doi.org/10.1007/s11042-018-6324-7

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