Memetic Particle Swarm Optimization algorithm for multi-objective optimization problems

In this paper, we propose a new Memetic Particle Swarm Optimization scheme that Incorporates random walk for local search techniques In the non-dominated sorting Particle Swarm Optimization algorithm In addition to the mechanism of crowding distance computation, resulting in an efficient and effective optimization method. The proposed algorithm has been applied to different unconstrained and constrained programming problems and the obtained results are compared to that of the published ones. The results showed that the proposed approach generates a precise well distributed set of non-dominated solutions Justifying the superiority of the random walk method Memetic approach.