New Proposal for a Multi-objective Technique using Tribes and Tabu Search

The aim of this paper is to present a new multi-objective technique which consists on a hybridization between a particle swarm optimization approach (Tribes) and tabu search technique. The main idea of the approach is to combine the high convergence rate of Tribes with a local search technique based on Tabu Search. Besides, in our study, we proposed different places to apply local search: the archive, the best particle among each tribe and each particle of the swarm. As a result of our study, we present three versions of our hybridized algorithm. The mechanisms proposed are validated using twelve 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.

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