Abstract
This paper elaborates on new ideas of a scatter search algorithm for solving multiobjective problems. Our approach adapts the well-known scatter search template for single objective optimization to the multiobjective field. The result is a simple and new metaheuristic called SSMO, which incorporates typical concepts from the multiobjective optimization domain such as Pareto dominance, crowding, and Pareto ranking. We evaluate SSMO with both constrained and unconstrained problems and compare it against NSGA-II. Preliminary results indicate that scatter search is a promising approach for multiobjective optimization.
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
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Knowles, J., Corne, D.: The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, NJ, pp. 9–105. IEEE Computer Society Press, Los Alamitos (1999)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)
Coello, C.A., Toscano, G.: Multiobjective Optimization Using a Micro-Genetic Algorithm. In: GECCO-2001, pp. 274–282 (2001)
Glover, F., Laguna, M., Martí, R.: Fundamentals of Scatter Search and Path Relinking. Control and Cybernetics 29, 653–684 (2000)
Beausoleil, R.P.: MOSS: Multiobjective Scatter Search Applied to Nonlinear Multiple Criteria Optimization. To appear in the European Journal of Operational Research (2004)
Caballero, R., Laguna, M., Molina, J., Martí, R.: SSPMO: A Scatter Search Procedure for Non-Linear Multiobjective Optimization. Submitted to INFORMS Journal on Computing (2004)
da Silva, C.G., Clímaco, J., Figueira, J.: A Scatter Search Method for the Bi-Criteria Multi-Dimensional {0,1}-Knapsack Problem using Surrogate Relaxation. Journal of Mathematical Modelling and Algorithms 3, 183–208 (2004)
Glover, F.: A Template for Scatter Search and Path Relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) AE 1997. LNCS, vol. 1363. Springer, Heidelberg (1998)
Glover, F., Laguna, M., Martí, R.: Scatter Search. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing: Theory and Applications. Springer, Heidelberg (2003)
Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Grefensttete, J. (ed.) First International Conference on Genetic Algorithms, Hillsdale, NJ, pp. 93–100 (1987)
Fonseca, C.M., Flemming, P.J.: Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms - Part II: Application Example. IEEE Transactions on System, Man, and Cybernetics 28, 38–47 (1998)
Kursawe, F.: A Variant of Evolution Strategies for Vector Optimization. In: Schwefel, H., Männer, R. (eds.) Parallel Problem Solving for Nature, pp. 193–197. Springer, Berlin (1990)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. IEEE Transactions on Evolutionary Computation 8, 173–195 (2000)
Osyczka, A., Kundo, S.: A New Method to Solve Generalized Multicriteria Optimization Problems Using a Simple Genetic Algorithm. Structural Optimization 10, 94–99 (1995)
Tanaka, M., Watanabe, H., Furukawa, Y., Tanino, T.: GA-Based Decision Support System for Multicriteria Optimization. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1556–1561 (1995)
Srinivas, N., Deb, K.: Multiobjective Function Optimization Using Nondominated Sorting Genetic Algorithms. Evolutionary Computation 2, 221–248 (1995)
Kurpati, A., Azarm, S., Wu, J.: Constraint Handling Improvements for Multi-Objective Genetic Algorithms. Structural and Multidisciplinary Optimization 23, 204–213 (2002)
Montgomery, D.C.: Design and Analysis of Experiments, 3rd edn. John Wiley, New York (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nebro, A.J., Luna, F., Alba, E. (2005). New Ideas in Applying Scatter Search to Multiobjective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-31880-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24983-2
Online ISBN: 978-3-540-31880-4
eBook Packages: Computer ScienceComputer Science (R0)