Abstract

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.

Citations