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In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrained Multi-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high. Other features of SMPSO include the use of polynomial mutation as a turbulence factor and an external archive to store the nondominated solutions found during the search. Our proposed approach is compared with respect to five multi-objective metaheuristics representative of the state-of-the-art in the area. For the comparison, two different criteria are adopted: the quality of the resulting approximation sets and the convergence speed to the Pareto front. The experiments carried out indicate that SMPSO obtains remarkable results in terms of both, accuracy and speed.

Abstract. This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store non-dominated solutions and a feedback mechanism in which solutions from this archive randomly replaces existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been evaluated with both constrained and unconstrained problems and compared against NSGA-II and SPEA2, two state-of-theart evolutionary multiobjective optimizers. For the used benchmark, preliminary experiments indicate that MOCell obtains competitive results in terms of convergence, and it clearly outperforms the other two compared algorithms concerning the diversity of solutions along the Pareto front.

In this paper we propose a new algorithm for solving multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called AbYSS, which follows the scatter search structure but using mutation and crossover operators coming from the field of evolutionary algorithms. AbYSS incorporates typical concepts from the multiobjective field, such as Pareto dominance, density estimation, and an external archive to store the nondominated solutions. We evaluate AbYSS with a standard benchmark including both unconstrained and constrained problems, and it is compared against two state-ofthe-art multiobjective optimizers, NSGA-II and SPEA2. The obtained results indicate that AbYSS produces very competitive Pareto fronts according to the applied convergence metric, and it clearly outperforms the other two algorithms concerning the diversity of the solutions and the hypervolume metric.

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