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MOCell: A Cellular Genetic Algorithm for Multiobjective Optimization
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DOI：10.1002/int.20358.png)
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Abstract
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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 it feedback mechanism in which solutions front 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. (C) 2009 Wiley Periodicals, Inc.
Keywords:
EVOLUTIONARY ALGORITHMS
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