Particle Swarm Optimizer for constrained optimization

Recently, Particle Swarm Optimizer (PSO) has become a popular tool for solving constrained optimization problems. However, there is no guarantee that PSO will perform consistently well for all problems and will not be trapped in local optima. In this paper, a PSO algorithm is introduced that uses two new mechanisms, the first one to maintain a better balance between intensification and diversification and the second one to escape from local solutions. Furthermore, all the basic parameters are determined self-adaptively. The performance of the proposed algorithm is analyzed by solving the CEC2010 constrained optimization problems. The algorithm shows consistent performance, and is superior to other state-of-the-art algorithms.