Special Section on Nonlinear Science Workshop on the Journal
Set-based comprehensive learning and particle swarm optimization with memory for discrete optimization problem
2022 年 13 巻 2 号 p. 452-458
詳細
2022 年 13 巻 2 号 p. 452-458
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.
