A Novel Particle Swarm Optimization for Constrained Optimization Problems.

This paper proposes a novel particle swarm optimization (PSO) for solving constrained optimization problems. Based upon the acceptable assumption that any feasible solution is better than any infeasible solution, a new mechanism for constraints handling is incorporated in the standard particle swarm optimization. In addition to the mechanism of constraints handling, a mutation strategy to increase population diversity is added to the proposed algorithm to improve convergence. Experimental results compared with genetic algorithm and a standard PSO show that the proposed algorithm is a desirable and competitive algorithm for solving constrained optimization problems. [ABSTRACT FROM AUTHOR]