Abstract
This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multi-objective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a non-dominated sorting genetic algorithm (NSGA) and a controlled elitist non-dominated sorting genetic algorithm (CNSGA). The resulting algorithms can be referred to as co-operative co-evolutionary multi-objective optimisation algorithms or CCMOAs. The CCMOAs are benchmarked against the EMOAs in seven test problems. The first six problems cover different characteristics of multi-objective optimisation problems, namely convex Pareto front, non-convex Pareto front, discrete Pareto front, multi-modality, deceptive Pareto front and non-uniformity of solution distribution. In contrast, the last problem is a two-objective real-world problem, which is generally referred to as the continuum topology design. The results indicate that the CCMOAs are superior to the EMOAs in terms of the solution set coverage, the average distance from the non-dominated solutions to the true Pareto front, the distribution of the non-dominated solutions and the extent of the front described by the non-dominated solutions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hajela, P., Lin, C.Y.: Genetic search strategies in multicriterion optimal design. Structural Optimization 4, 99–107 (1992)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, Urbana-Champaign, IL, pp. 416–423 (1993)
Horn, J., Nafpliotis, N.: Multiobjective optimization using the niched Pareto genetic algorithm. IlliGAL Report No. 93005, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL (1993)
Srinivas, N., Deb, K.: Multi-objective function optimization using nondominated sorting genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms–Part 1: A unified formulation. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans 28(1), 26–37 (1998)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)
Deb, K., Goel, T.: Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 67–81. Springer, Heidelberg (2001)
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)
Sangkawelert, N., Chaiyaratana, N.: Diversity control in a multi-objective genetic algorithm. In: Proceedings of the 2003 Congress on Evolutionary Computation, Canberra, Australia, pp. 2704–2711 (2003)
Keerativuttitumrong, N., Chaiyaratana, N., Varavithya, V.: Multi-objective co-operative co-evolutionary genetic algorithm. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 288–297. Springer, Heidelberg (2002)
Lohn, J.D., Kraus, W.F., Haith, G.L.: Comparing coevolutionary genetic algorithm for multiobjective optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, HI, pp. 1157–1162 (2002)
Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2000)
Boonlong, K., Maneeratana, K.: A preliminary study on the multi-objective topology design by genetic algorithm and finite volume method. In: Proceedings of the 17th Conference of the Mechanical Engineering Network of Thailand, Prachinburi, Thailand (2003) CS024
Shimodaira, H.: DCGA: A diversity control oriented genetic algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Glasgow, UK, pp. 444–449 (1997)
Shimodaira, H.: A diversity-control-oriented genetic algorithm (DCGA): Performance in function optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea, pp. 44–51 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maneeratana, K., Boonlong, K., Chaiyaratana, N. (2004). Multi-objective Optimisation by Co-operative Co-evolution. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-30217-9_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
Online ISBN: 978-3-540-30217-9
eBook Packages: Springer Book Archive