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MOCell: A cellular genetic algorithm for multiobjective optimization
Author(s) -
Nebro Antonio J.,
Durillo Juan J.,
Luna Francisco,
Dorronsoro Bernabé,
Alba Enrique
Publication year - 2009
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20358
Subject(s) - benchmark (surveying) , multi objective optimization , convergence (economics) , mathematical optimization , computer science , evolutionary algorithm , genetic algorithm , pareto principle , population , mathematics , algorithm , demography , geodesy , sociology , geography , economics , economic growth
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.