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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Zitzler, E.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. on Evolutionary Computation 13(4), 257–271 (1999)
Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 23(4), 1–16 (1995)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)
Ficici, S.G.: Solution concepts in coevolutionary algorithms, Ph.d. dissertation, Brandeis University (May 2004)
Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. In: Artificial Life II, pp. 313–324. Addison-Wesley, Reading (1991)
Paredis, J.: Coevolutionary computation. Artificial Life 21(4), 355–379 (1995)
Cao, X.-b., Li, J.-l., Wang, X.-f.: Research on Multiobjective Optimization based on Ecological cooperation. Journal of Software 12(4), 521–528 (2001)
Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002); ISBN 0-3064-6762-3
Coello Coello, C.A., Toscano Pulido, G.: Multiobjective Optimization using a Micro-Genetic Algorithm. In: Spector, L., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 274–282. Morgan Kaufmann Publishers, San Francisco (2001)
Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Van Veldhuizen, D.A., Lamont, G.B.: On Measuring Multiobjective Evolutionary Algorithm Performance. In: 2000 Congress on Evolutionary Computation, vol. 1, pp. 204–211. IEEE Service Center, Piscataway (2000)
Van Veldhuizen, D.A., Lamont, G.B.: On Measuring Multiobjective Evolutionary Algorithm Performance. In: 2000 Congress on Evolutionary Computation, vol. 1, pp. 204–211. IEEE Service Center, Piscataway (2000)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)
De Jong, E.D., Pollack, J.B.: Idea evaluation from coevolution. Evolutionary Computation 12(4), 25–28 (2004)
Xing, Z.-Y., Zhang, Y., Hou, Y.-L., Jia, L.-M.: On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm. International Journal of Control, Automation, and Systems 5(4), 444–455 (2007)
Horn, J., Goldberg, D.: A Niched Pareto genetic algorithm for multiobjective optimization. In: Proc. of the First IEEE Conference on Evolutionary Computation, Piscataway, pp. 82–87 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)