Abstract
Multi-objective genetic algorithm is proved to be suitable for solving multi-objective optimization problems. However, it is usually very hard to balance the convergence and diversity of a multi-objective genetic algorithm. This paper introduces a new algorithm, with both good convergence and diversity based on clustering method and multi-parent crossover operator. Meanwhile, an initial population is generated by orthogonal design to enhance the search effort of the algorithm. The experimental results on a number of test problems indicate the good performance of the Cluster-Based Orthogonal Multi-Objective Genetic Algorithm.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithm. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. Lawrence Erlbaum, Mahwah (1985)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. Danpur Genetic Algorit hms Laboratory (kanGAL), Indian Institute of Technology, Kanpur: Technical Report 6(2), 182–197 (2002)
Knowles, J.D., Corne, D.W.: The Pareto archived evolution strategy: A new baseline algorithm for Pareto for multiobjective optimization. In: Congress on Evolutionary Computation (CEC 1999), Piscataway, NJ, pp. 38–48 (2001)
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)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. ETH Zent rum, Zurich, Switzerland: TIK2Report 103, 200–210 (2001)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: 1st IEEE Trans. Evol. Comput., Piscataway, NJ, vol. 1, pp. 82–87 (1994)
Yen, G.G., Lu, H.: Dynamic multiobjective evolutionary algorithm: Adaptive cell-based rank and density estimation. IEEE Trans. Evol. Comput. 7, 253–274 (2003)
Jinghua, Z., Zhongzhi, S., Yong, X.: A Fast Multi-objective Genetic Algorithm Based on Clustering. Journal of computer research and development 41(7), 1081–1087 (2004)
Chan, K.P., Ray, T.: An evolutionary algorithm to maintain diversity in the parametric and the objective space. In: Third International Conference on Computational Intelligence, Robotics and Autonomous Systems, CIRAS 2005 (2005)
Zhou, A., Zhang, Q., Jin, Y.: Approximating the Set of Pareto Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm. Technical Report CES-485
Montgomery, D.C.: Design and Analysis of Experiments (3rd). Wiley, New York (1991)
Leung, Y.W., Wang, Y.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation 5(1), 41–53 (2001)
Dai, G., Zheng, W., Xie, B.: An Orthogonal and Model Based Multiobjective Genetic Algorithm for LEO Regional Satellite Constellation Optimization. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 652–660. Springer, Heidelberg (2007)
Zheng, S., Wei, W., Kang, L., et al.: A multi-objective algorithm based on orthogonal design. Chinese Journal of Computers 28(7), 1153–1162 (2005)
Cai, Z., Gong, W., Huang, Y.: A Novel Differential Evolution Algorithm based on epsilon-domination and Orthogonal Design Method for Multiobjective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 286–301. Springer, Heidelberg (2007)
Gong, W., Cai, Z.: An Improved Multiobjective Differential Evolution based on Pareto-adaptive epsilon-dominance and Orthogonal Design. European Journal of Operational Research 198(2), 576–601 (2009)
Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10(5), 477–506 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, J., Dai, G., Mo, L. (2009). A Cluster-Based Orthogonal Multi-Objective Genetic Algorithm. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-04962-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04961-3
Online ISBN: 978-3-642-04962-0
eBook Packages: Computer ScienceComputer Science (R0)