Interleaving guidance in evolutionary multi-objective optimization

Bui, Lam Thu ; Deb, Kalyanmoy ; Abbass, Hussein A. ; Essam, Daryl (2008) Interleaving guidance in evolutionary multi-objective optimization Journal of Computer Science and Technology, 23 (1). pp. 44-63. ISSN 1000-9000

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In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres (local models) 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 by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Pareto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version.

Item Type:Article
Source:Copyright of this article belongs to Springer.
Keywords:Evolutionary Multi-objective Optimization; Guided Dominance; Local Models
ID Code:75139
Deposited On:21 Dec 2011 14:20
Last Modified:21 Dec 2011 14:20

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