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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(More)
This paper presents a new multi-objective technique which consists of a hybrid between a particle swarm optimization approach (PSO) and tabu search (TS) technique. The main idea of the approach is to combine the high convergence rate of PSO with a local search technique based on Tabu Search. Besides, in our study, we proposed to apply local search to(More)
In this paper, we propose to introduce inheritance and approximation techniques for the evaluation of the objective function. The main idea of the approaches is to reduce MO-TRIBES complexity. Besides, in our study, we incorporate at the beginning, an inheritance technique then an approximation technique (Approximation 1: to consider the whole swarm,(More)
The aim of this paper is to present an improvement of the multiobjective TRIBES (MO-TRIBES). The main idea of this improvement is to propose two new operators: a mutation, which is applied to good particles and four processes of resets, which are applied to bad particles. The aim of the integration of those mechanisms is to insure a good exploration and/or(More)
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(More)
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