A study of the efficiency of hybridized approaches based on Particle Swarm Optimization technique

Particle Swarm Optimization (PSO) is a continuous optimization metaheuristic in which the PSO's convergence is ensured, but its solution is considered neither as a global solution nor as a local solution. The convergence is guaranteed only to the best visited position by the whole swarm. In this paper, we propose a couple of hybrid methods for multi-objective particle swarm optimization. In fact, we combined these methods in the following two cases: in the first case, we proposed to hybridize it with a local search technique based on Tabu Search (TS). In the second case, we proposed to hybridize it with a global search technique based on PESAII. The proposed mechanisms are validated using fifteen different functions from the specialized literature of multi-objective optimization. The obtained results show that using this kind of hybridization is justified as it is able to improve the quality of the solutions in the majority of cases.