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An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization

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Abstract

Particle Swarm Optimization (PSO) has recently emerged as a nature-inspired algorithm for real parameter optimization. This article describes a method for improving the final accuracy and the convergence speed of PSO by firstly adding a new coefficient (called mobility factor) to the position updating equation and secondly modulating the inertia weight according to the distance between a particle and the globally best position found so far. The two-fold modification tries to balance between the explorative and exploitative tendencies of the swarm with an objective of achieving better search performance. We also mathematically analyze the effect of the modifications on the dynamics of the PSO algorithm. The new algorithm has been shown to be statistically significantly better than the basic PSO and four of its state-of-the-art variants on a twelve-function test-suite in terms of speed, accuracy, and robustness.

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Acknowledgments

This work is supported by the Key Project of Chinese Ministry of Education under Grant 209021.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India

    Sayan Ghosh, Swagatam Das, Debarati Kundu & Kaushik Suresh

  2. Department of Electrical Engineering, IIT, Delhi, India

    B. K. Panigrahi

  3. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

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  1. Sayan Ghosh
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  2. Swagatam Das
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  3. Debarati Kundu
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  4. Kaushik Suresh
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  5. B. K. Panigrahi
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  6. Zhihua Cui
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Corresponding author

Correspondence to B. K. Panigrahi.

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Ghosh, S., Das, S., Kundu, D. et al. An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization. Neural Comput & Applic 21, 237–250 (2012). https://doi.org/10.1007/s00521-010-0356-x

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