AN INFEASIBLE ELITIST BASED PARTICLE SWARM OPTIMIZATION FOR CONSTRAINED MULTIOBJECTIVE OPTIMIZATION AND ITS CONVERGENCE

In this paper, an infeasible elitist based particle swarm optimization is proposed for solving constrained optimization problems. Firstly, an infeasible elitist preservation strategy is proposed, which keeps some infeasible solutions with smaller rank values at the early stage of evolution regardless of how large the constraint violations are, and keep some infeasible solutions with smaller constraint violations and rank values at the later stage of evolution. In this manner, the true Pareto front will be found easier. Secondly, in order to find a set of diversity and uniformly distributed Pareto optimal solutions, a new crowding distance function is designed. It can assign large function values not only for the particles located in the sparse regions of the objective space but also for the crowded particles located near to the boundary of the Pareto front as well. Thirdly, a new mutation operator with two phases is proposed. In the first phase, the particles whose constraint violations are less than the threshold value will be used to compute the total force, then the force will be used as a mutation direction, being helpful to find the better solutions along this direction. In order to guarantee the convergence of the algorithm, the second phase of mutation is proposed. Finally, the convergence of the algorithm is proved. The comparative study shows that the proposed algorithm can generate widespread and uniformly distributed Pareto fronts and outperforms those compared algorithms.