Set-based comprehensive learning and particle swarm optimization with memory for discrete optimization problem

Yuta Chikuba; Takahiro Hino; Michiharu Maeda

doi:10.1587/nolta.13.452

Special Section on Nonlinear Science Workshop on the Journal

Set-based comprehensive learning and particle swarm optimization with memory for discrete optimization problem

Yuta Chikuba, Takahiro Hino, Michiharu Maeda

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Keywords: comprehensive learning, particle swarm optimization, discrete space, optimization problem

JOURNAL FREE ACCESS

2022 Volume 13 Issue 2 Pages 452-458

DOI https://doi.org/10.1587/nolta.13.452

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Abstract

This paper describes a novel algorithm of set-based comprehensive learning and particle swarm optimization with memory for discrete optimization problem. Our algorithm is an approach of searching for the best position in a set by referring to the current position when updating the solution. In order to show the validity of our algorithm, we examine numerical experiments compared to existing algorithms.

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