A memetic particle swarm optimization for constrained multi-objective optimization problems

In this paper, a new memetic algorithm for constrained multi-objective optimization problems is proposed, which combines the global search ability of particle swarm optimization with an attraction based local search operator for directed local fine-tuning. Firstly, a new particle updating strategy is proposed based on the concept of uncertain personal-best to deal with the problem of premature convergence. Secondly, an attraction based local search operator is proposed to find good local search direction for the particles. Finally, the convergence of the algorithm is proved. The proposed algorithm is examined and compared with two well known existing algorithms on five benchmark test functions. The results suggest that the new algorithm can evolve more good solutions, and the solutions are more widely spread and uniformly distributed along the Pareto front than the two existing methods. The proposed two developments are effective individually, but the combined effect is much better for these constrained multi-objective optimization problems.