A Study of the Efficiency of the Hybridization of a Particle Swarm Optimizer and Tabu Search

In this paper, we propose a hybrid Particle Swarm Optimization (PSO) called TS-Tribes which combine Tribes, a PSO algorithm free of parameters and Tabu Search (TS) technique. The main idea behind this hybridization is to combine the high convergence rate of Tribes with a local search technique based on TS. In addition, we study the impact of the place where we apply local search on the performance of the obtained algorithm which leads us to three different versions: applying TS on the archive’s particles, applying TS only on the best particle among each tribe and applying TS on each particle of the swarm. The mechanisms proposed are validated using ten different functions from 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.