Improving TCPSO Solution Search Performance Using Gaussian Process Regression

Abstract

Particle Swarm Optimization tends to converge prematurely to local solutions. To improve such a problem, TCPSO (Two-swarm Cooperative PSO) has been proposed. TCPSO employs a group of slave particles for intensive solution search and a group of master particles for global solution search. If the solution search process of TCPSO results in a small update range of solutions, the approximate shape of the function can be estimated based on known input data. This estimation can be done using Gaussian process regression to model the distribution with mean and variance, and improves the solution search process of TCPSO.