Abstract
In this paper, we propose a framework using local models for multi-objective optimization to guide the search heuristic in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Pareto front using the guided dominance technique in the objective space. With this dual guidance, we can easily guide spheres towards different parts of the Pareto front while also exploring the decision space efficiently.
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
Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto frontier differential evolution approach for multiobjective optimization problems. In: Proceedings of CEC-2001, vol. 2, pp. 971–978. IEEE Press, Los Alamitos (2001)
Branke, J., Kaufler, T., Schmeck, H.: Guiding multi-objective evolutionary algorithms towards interesting regions. technical report no. 399. Technical report, Institute AIFB, University of Karlsruhe, Germany (2000)
Bui, L.T., Abbass, H.A., Essam, D.: Local models: An approach to disibuted multi-objective optimization. technical report no. 200601002. Technical report, ALAR, ITEE,UNSW@ADFA, Australia (2006)
Coello, C.A.C., Veldhuizen, D.A.V., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Publisher, New York (2002)
Deb, K.: Multiobjective Optimization using Evolutionary Algorithms. John Wiley and Son Ltd, New York (2001)
Deb, K., Zope, P., Jain, A.: Distributed computing of pareto optimal solutions using multi-objective evolutionary algorithms. Technical report, No. 2002008, KANGAL, IITK, India (2002)
Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the First International Conference on Genetic Algorithms, Hillsdale, New Jersey, pp. 93–100 (1985)
Tan, K.C., Lee, T.H., Khor, E.F.: Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Transactions on Evolutionary Computation 5(6), 565–588 (2001)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical report, ETH in Zurich, Swiss (2001)
Zitzler, E., Thiele, L.: Multi-objective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, Springer, Heidelberg (1998)
Zitzler, E., Thiele, L., Deb, K.: Comparision of multiobjective evolutionary algorithms: Emprical results. Evolutionary Computation 8(1), 173–195 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bui, L.T., Deb, K., Abbass, H.A., Essam, D. (2006). Dual Guidance in Evolutionary Multi-objective Optimization by Localization. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_49
Download citation
DOI: https://doi.org/10.1007/11903697_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
eBook Packages: Computer ScienceComputer Science (R0)