Abstract
In this paper, a novel multi-objective orthogonal simulated annealing algorithm MOOSA using a generalized Pareto-based scale-independent fitness function and multi-objective intelligent generation mechanism (MOIGM) is proposed to efficiently solve multi-objective optimization problems with large parameters. Instead of generate-and-test methods, MOIGM makes use of a systematic reasoning ability of orthogonal experimental design to efficiently search for a set of Pareto solutions. It is shown empirically that MOOSA is comparable to some existing population-based algorithms in solving some multi-objective test functions with a large number of parameters.
This is a preview of subscription content, log in via an institution to check access.
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
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjecctive evolutionary algorithms: empirical results. Evolutionary Computation 8(2), 173–195 (2000)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proc. 1st IEEE Conf. Evol. Comput., Orlando, FL, June 27-19, pp. 82–87 (1994)
Zitzler, E., Deb, K., Thiele, L.: Comparsion of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8(2), 173–195 (2000)
Knowles, J.D., Corne, D.W.: The Pareto archived evolution strategy: A new basedline algorithm for Pareto multiobjecitve optimization. In: Proc. 1999 Congress on Evol. Comput., Washington, DC, July 6-9, pp. 98–105 (1999)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and strength Pareto approach. IEEE trans. Evol. Comput. 3, 257–271 (1999)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective algorithms: NSGA-II. IEEE trans. Evol. Comput. 6, 182–197 (2002)
Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE trans. Evol. Comput. 7(2), 204–223 (2003)
Shu, L.-S., Ho, S.-J., Ho, S.-Y.: OSA: Orthogonal Simulated Annealing Algorithm and Its Application to Designing Mixed H2/H∞ Optimal Controllers. IEEE Trans. Systems, Man, and Cybernetics-Part A to appear
Bagchi, T.-P.: Taguchi Methods T.-P. Bagchi, Taguchi Methods Explained: Practical Steps to Robust Design. Prentice-Hall, Englewood Cliffs (1993)
Phadke, M.-S.: Quality Engineering Using Robust Design. Prentice-Hall, Englewood Cliffs
Leung, Y.-W., Wang, Y.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans. Evol. Comput. 5, 41–53 (2001)
J Schaffer, D.: Multi-objective optimization with vector evaluated genetic algorithms. In: Grefenstette, J.J. (ed.) Proc. 1st Int. Conference Genetic Algorithms, pp. 93–100. Lawrence Erlbaum, Hillsdale (1985)
Hajela, P., Lin, C.-Y.: Genetic search strategies in multicriterion optimal design. Structural Optimization (4), 99–107 (1992)
Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. SMC-Part C: Applications and Reviews 28(3), 392–403 (1998)
Osyczka, A., Kundu, S.: A modified distance method for multicriteria optimization, using genetic algorithms. Computers and Industrial Engineering 30(4), 871–882 (1996)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)
Deb, K.: Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol. Comput. 7(3), 205–230 (1999)
Coello, C.A.C.: A comprehensive survey of evolutionary-based multiobjective optimization techniqures. International Journal of Knowledge and Information System 1(3), 269–308 (1999)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Szu, H., Hartley, R.: Fast simulated annealing. Physics Letters 122, 157–162 (1987)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest, S. (ed.) Proc. fifth Int. Conference Genetic Algorithms, pp. 416–423. Morgan-Kaufmann, San Mateo (1993)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
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
Shu, LS., Ho, SJ., Ho, SY., Chen, JH., Hung, MH. (2004). A Novel Multi-objective Orthogonal Simulated Annealing Algorithm for Solving Multi-objective Optimization Problems with a Large Number of Parameters. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-24854-5_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22344-3
Online ISBN: 978-3-540-24854-5
eBook Packages: Springer Book Archive