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An n-state switching PSO algorithm for scalable optimization

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Abstract

Particle swarm optimization (PSO) is an optimization method that is most widely used to solve a number of problems in various fields such as engineering, economics and computer systems. However, due to its scalability and unsatisfying performance particularly for large-scale optimization problems; numerous PSO variants have been suggested so far, in the literature. This paper also proposes a new variant of the canonical PSO algorithm (‘N-state switching PSO—NS-SPSO’) that uses the evolutionary factor information to update particles velocities and, therefore, further enhance its performance. The evolutionary factor is derived by using the population distribution and the mean distance of each particle from the global best. The population distribution and the mean distance are determined through Euclidean distance. Moreover, algorithmic parameters such as inertia weight, and acceleration coefficients are assigned appropriate values at N stages (derived from exploration, exploitation, convergence and jumping out states) that improves the search efficiency and convergence speed. The proposed algorithm is applied to 12 widely used mathematical benchmark functions that demonstrate its best performance in terms of minimum evaluation error, fast convergence and low computational time. Besides these, seven high-dimensional functions and few other algorithms for large-scale optimization were considered to test the scalability of NS-SPSO algorithm. Our comparative results show that NS-SPSO performs well on low-dimensional problems and is promising for solving large-scale optimization problems. Furthermore, the proposed NS-PSO algorithm almost outperforms its closest rivals for various benchmarks.

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Acknowledgements

This work is supported by Abdul Wali Khan University, Mardan (AWKUM). The research was conducted as part of the Ph.D. program, at Brunel University London, UK, under the supervision of Prof. Zidong Wang (Fellow IEEE). The implementation code of the proposed NS-SPSO algorithm is available on the GitHub repository (https://github.com/izazhere/Research_Project).

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Authors and Affiliations

  1. Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan

    Izaz Ur Rahman, Muhammad Zakarya & Rahim Khan

  2. Faculty of Engineering, University of Porto, Porto, Portugal

    Mushtaq Raza

Authors
  1. Izaz Ur Rahman
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  2. Muhammad Zakarya
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  3. Mushtaq Raza
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  4. Rahim Khan
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Correspondence to Izaz Ur Rahman or Muhammad Zakarya.

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Communicated by A. Di Nola.

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Rahman, I.U., Zakarya, M., Raza, M. et al. An n-state switching PSO algorithm for scalable optimization. Soft Comput 24, 11297–11314 (2020). https://doi.org/10.1007/s00500-020-05069-2

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