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Improving Distribution-Based Discrete Particle Swarm Optimization Using Lévy Flight

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Abstract

Some metaheuristic algorithms such as particle swarm optimization (PSO) are extended and have been shown to perform very well in a wide range of optimization domains though they are originally designed for continuous optimization. In discrete optimization, some extended algorithms handle continuous parameters of a probability distribution, which assumes variable values of a candidate solution instead of directly handling discrete variables. These distribution-based discrete PSOs (DDPSO) sample a variable value from a distribution for every variable to generate a candidate solution. This procedure can be considered as a kind of local search centered on anintended solution, which has the highest probability to be generated. Step length from the intended solution increases proportionally and the probability of producing an intended solution decreases exponentially in high-dimensional problems. We propose a novel sampling method to control the step size for DDPSO. In this paper, we describe our new sampling method to control the step size with Lévy distribution in a similar way to Lévy flight. The proposed method is applied to three representative methods of DDPSOs and performance is compared with original algorithms. In our discrete optimization experiments, we demonstrate that our algorithm increases DDPSO’s search performance and robustness to dimensionality.

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Acknowledgment

This work was supported in part by the Ministry of Education, Culture, Sports, Science and Technology-Japan, Grant–in–Aid for Scientific Research under grant #JP19H01137, #JP19H04025, and #JP20H04018.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan

    Koya Ihara & Shohei Kato

  2. Frontier Research Institute for Information Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan

    Koya Ihara & Shohei Kato

Authors
  1. Koya Ihara

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  2. Shohei Kato

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Correspondence toShohei Kato.

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Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia

    Marcus Gallagher

  2. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

    Nour Moustafa

  3. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

    Erandi Lakshika

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Ihara, K., Kato, S. (2020). Improving Distribution-Based Discrete Particle Swarm Optimization Using Lévy Flight. In: Gallagher, M., Moustafa, N., Lakshika, E. (eds) AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science(), vol 12576. Springer, Cham. https://doi.org/10.1007/978-3-030-64984-5_15

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