A Bio-Inspired Memetic Particle Swarm Optimization Algorithm for Multi-objective Optimization Problems

This paper proposes a bio-inspired particle swarm optimization algorithm that incorporates random walk for local search techniques in the non-dominated sorting Particle Swarm Optimization (PSO) algorithm in addition to the mechanism of crowding distance, resulting in an efficient and effective optimization method. The proposed algorithm was implemented and evaluated using different benchmark test problems including unconstrained and constrained problems. The obtained results were compared with published ones. The results showed that the proposed bio-inspired algorithm generates a precise well distributed set of non-dominated solutions justifying the superiority of the random walk method.