Summary
International Symposium on Nonlinear Theory and its Applications
2017
Session Number:C0L-C
Session:
Number:C0L-C-3
A Co-Evolutional Particle Swarm Optimizer with Dynamic Re-Grouping Schemes
Ryosuke Kikkawa, Hidehiro Nakano, Arata Miyauchi,
pp.584-587
Publication Date:2017/12/4
Online ISSN:2188-5079
Summary:
Particle Swarm Optimizer (PSO) is a kind of metaheuristic algorithms for solving optimization problems with continuous objective functions. PSO can be executed based on the simple dynamics of search particles. For solving high-dimensional optimization problems with the large number of design variables, Cooperative Particle Swarm Optimizer (CPSO) has been proposed. In CPSO, each sub-swarm searches partial solutions in each sub-space given by the division of search space. Integrating the partial solutions, CPSO can obtain solution candidates for the optimization problems. This paper proposes dynamical and deterministic grouping methods for the sub-swarms in CPSO. In the simulation experiments, the results for some benchmark problems are shown.