A new cooperative particle swarm optimizer with dimension partition and adaptive velocity control

Particle swarm optimization (PSO) has been proposed as an alternative to traditional evolutionary algorithms. In this work, we present a new PSO approach to control the trade-off between exploitation and exploration in the search process for solving multimodal functions. Our approach focuses on two search strategies for multimodal functions. One is a two-swarm cooperative strategy that controls search region and integrates partial and full dimension PSO search. The other strategy is to control the velocity of the particles in an adaptive way. To evaluate the proposed approach, experiments have been conducted and comparisons to several popular PSO variants have been made. The results prove that our approach can have better performance than others in most of the test cases.