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Coevolutionary Optimization Algorithm: With Ecological Competition Model

  • Conference paper
Emerging Research in Artificial Intelligence and Computational Intelligence (AICI 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 237))

Abstract

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.

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Author information

Authors and Affiliations

  1. Congqing Business and Technology University, 400067, Chongqing, China

    Jianguo Liu & Weiping Wu

Authors
  1. Jianguo Liu
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  2. Weiping Wu
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Editor information

Editors and Affiliations

  1. School of Business Information technology, RMIT University, GPO Box 2476V, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  2. Department of Computer Science and Technology, Tongji University, Shanghai, China

    Duoqian Miao

  3. Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan, Hong Kong, SAR, China

    Fu Lee Wang

  4. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

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© 2011 Springer-Verlag Berlin Heidelberg

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Liu, J., Wu, W. (2011). Coevolutionary Optimization Algorithm: With Ecological Competition Model. In: Deng, H., Miao, D., Wang, F.L., Lei, J. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2011. Communications in Computer and Information Science, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24282-3_10

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  • DOI: https://doi.org/10.1007/978-3-642-24282-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24281-6

  • Online ISBN: 978-3-642-24282-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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