A New Proposal for a Multi-objective Technique using Tribes and Simulated Annealing

This paper proposes a new hybrid multi-objective particle swarm optimizer which incorporates a particle swarm optimization approach (Tribes) and Simulated Annealing (SA). The main idea of the approach is to propose a skilled combination of Tribes with a local search technique based on Simulated Annealing technique. Besides, we are studying 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 SA on the archive’s particles, applying SA only on the best particle among each tribe and applying SA on each particle of the swarm. In order to validate our approach, we use ten well-known test functions proposed in 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.