Solving Three-Objective Optimization Problems Using a New Hybrid Cellular Genetic Algorithm

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Abstract

In this work we present a new hybrid cellular genetic algorithm. We take MOCell as starting point, a multi-objective cellular genetic algorithm, and, instead of using the typical genetic crossover and mutation operators, they are replaced by the reproductive operators used in differential evolution. An external archive is used to store the nondominated solutions found during the search process and the SPEA2 density estimator is applied when the archive becomes full. We evaluate the resulting hybrid algorithm using a benchmark composed of three-objective test problems, and we compare the results with several state of the art multi-objective metaheuristics. The obtained results show that our proposal outperforms the other algorithms according to the two considered quality indicators.