A hybrid approach of dimension partition and velocity control to enhance performance of particle swarm optimization
Particle swarm optimization (PSO) is a population-based optimization technique and it has been used to solve many optimization problems successfully. However, more efficient strategies are still needed to control the trade-off between exploitation exploration in the search process for solving tasks with high complexity. In this work, we present a new hybrid PSO approach to overcome the search difficulties. Our approach focuses on two search strategies. One is a two-swarm cooperative strategy that controls search region and integrates full and single dimension PSO search. The other strategy is to control the velocity of the particles in an adaptive way, according to how they move in the space. To evaluate the performance and generality of our hybrid approach, extensive experiments have been conducted and the results confirm the effectiveness of the proposed approach.
dimension partition particle swarm optimization cooperative strategy swarm intelligence adaptive velocity control