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An Improved Particle Swarm Algorithm for Solving Nonlinear Constrained Optimization Problems

This paper proposes an improved particle swarm optimization algorithm(IPSO). IPSO adopts a new mutation operator and a new method that congregates some neighboring individuals to form multiple sub- populations in order to lead particles to explore new search space. Additionally, our algorithm incorporates a mechanism with a simple and easy penalty function to handle constraint. Thus, our algorithm has strong global exploratory capability and efficiency while being applied to solve nonlinear constrained optimization problems. Experimental results indicate that our IPSO is robust and efficient in solving nonlinear constrained optimization problems.