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Published in:
2011 | OriginalPaper | Chapter
Coevolutionary Optimization Algorithm: With Ecological Competition Model
Authors : Jianguo Liu, Weiping Wu
Published in: Emerging Research in Artificial Intelligence and Computational Intelligence
Publisher: Springer Berlin Heidelberg
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Premature convergence and low converging speed are the distinct weaknesses of the genetic algorithms. a new algorithm called ECCA (ecological competition coevolutionary algorithm) is proposed for multiobjective optimization problems, in which the competition is considered to be in important position. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. An ecological population density competition equation is used for reference to describe the relation between multiple objectives and to direct the adjustment over the relation at individual and population levels. The proposed approach store the Pareto optimal point obtained along the evolutionary process into external set, enforcing a more uniform distribution of such vectors along the Pareto front. The experiment results show the high efficiency of the improved Genetic Algorithms based on this model in solving premature convergence and accelerating the convergence.