MOCell: A cellular genetic algorithm for multiobjective optimization

Authors:
Antonio J. Nebro;Juan J. Durillo;Francisco Luna;Bernabé Dorronsoro;Enrique Alba
Affiliations:
Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain;Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain;Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain;Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain;Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain
Venue:
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
Year:
2009

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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 nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace 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-the-art evolutionary multiobjective optimizers. For the studied benchmark, our experiments indicate that MOCell obtains competitive results in terms of convergence and hypervolume, and it clearly outperforms the other two compared algorithms concerning the diversity of the solutions along the Pareto front. © 2009 Wiley Periodicals, Inc.