Abstract
A novel multi-population evolutionary algorithm (MPEA) is presented, which can solve the constrained function optimization problems rather efficiently. The MPEA adopts three populations with different multi-parent crossover operators. So each population emphasizes particularly on different searching regions and the complementarity of these three crossover operators can enhances the diversity of individuals, which improves the search ability of the MPEA dramatically. And during the MPEA runs, the three populations exchange the best solution in each generation to adjust its search direction to the possible optimum solution. Experiments have been carried on several benchmark functions to test the performance of the presented MPEA. Numerical results show that MPEA is highly competitive with other algorithms in effectiveness and generality.
Chapter PDF
Similar content being viewed by others
Key words
References
Storn R And Price K., Minimizing the real functions of the ICEC’96 contest by differntial evolution. IEEE Conf.on Evolutionary Computation. Nagoya. 1997, 842–844.
Chiou J P and Wang, P S. A hybid method of differential evolution with application to optimal control problems of a bioprocess system. IEEE Conf. On Evolutionary Computation. Anchorage. 1998, 627–631
Yun-Chien Lin, Feng-Shen Wang, and Kao-shing Hwang. a hybrid method of evolutionary algorithms for mixed-integer nonlinear optimization problems. Proceedings of the 1999 congress on Evolutionary Computation. Washington, D.C. OSA: IEEE Service Center, 1999, 3:2159–2169
Michalewicz Z, Nazhiyath G. Genecop III: A Co-evolutionary Algorithm for Numerical Optimization Problems with Nonlinear Constraints. Proc. of 1995 IEEE Int’l. Conf. on Evolutionary Computation (ICEC’95). Perth, Australia, IEEE Press, 1995, 647–651
Renders, J.-M. and H. Bersini (1994). Hybridizing genetic algorithms with hill-climbing methods for global optimization: Two possible ways. In Z. Michalewicz, J.D. Schaffer, H.-P. Schwefel, D.B. Fogel, and H. Kitano (Eds.), Proceedings of the First IEEE International Conference on Evolutionary Computation, pp, IEEE Press, 1994. 312–317.
Runarsson TP, Yao X. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 2000, 4(3):284–294.
Deb K, Joshi D, Anand A. Real-Coded evolutionary algorithms with parent-centric recombination. Technical Report, KanGAL Report No.2001003, Kanpur: Indian Institute of Technology, 2001.
Efrén Mezura-Montes & Carlos A. Coello Coello:Adding a Diversity echanism to a Simple Evolution Strategy to Solve Constrained Optimization Problems. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC’03), IEEE Press, ADFA, UNSW, Canberra, Australia, 8–12 December 2003. pp. 6–13
Tanese R. Distributed Genetic Algorithms. Proc. of the 3rd Int’l. Conf. on Genetic Algorithms. Morgan Kaufmann, Los Altos, 1989
Potts J C, Giddens T P, Yadav S B. The Development and Evaluation of an Improved Genetic Algorithm Based on Migration and Artificial Selection. IEEE Trans.on SMC, 24(1), 1994.73–86
K. Deb and R.B. Agrawal: Simulated Binary Crossover for Continuous Search Space, Complex Systems, 9, 115–148(1995).
Guo T, Kang L S: A new Evolutionary Algorithm for Function Optimization. Wuhan University Journal of Natural Sciences, 1999, 4(4) (In Chinese)
K. Deb. An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering, 2000.186(2/4):311–338
Z. Michalewicz: Genetic Algorithms, Numerical Optimization, and Constraints, In L. Eshelman (Ed.), Proceedings of the 6th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, 1995.151–158
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 International Federation for Information Processing
About this paper
Cite this paper
Chen, Z., Kang, L. (2005). Multi-Population Evolutionary Algorithm for Solving Constrained Optimization Problems. In: Li, D., Wang, B. (eds) Artificial Intelligence Applications and Innovations. AIAI 2005. IFIP — The International Federation for Information Processing, vol 187. Springer, Boston, MA. https://doi.org/10.1007/0-387-29295-0_41
Download citation
DOI: https://doi.org/10.1007/0-387-29295-0_41
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-28318-0
Online ISBN: 978-0-387-29295-3
eBook Packages: Computer ScienceComputer Science (R0)