Abstract
Many engineering optimization problems can be state as function optimization with constrained, intelligence optimization algorithm can solve these problems well. Particle Swarm Optimization (PSO) algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. In this paper, aim at the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, we improve the standard PSO and propose a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solution when compared to other heuristic methods and is a powerful optimization algorithm for constrained engineering optimization problems.
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