Component-Based Ranking Strategy for Evolutionary Optimization with Sparse Constraints

Abstract:

Most constraint-handling methods in constrained evolutionary optimization usually take advantage of only the valuable information of feasible solutions, while they don’t exploit adequately the information from infeasible ones. In this paper, a concept of “feasible component” is introduced to recognize the characteristics of diverse information extracted from infeasible solutions. Then a component-based ranking strategy is proposed for evolutionary optimization with sparse constraints by integrating feasible components and the idea of stochastic ranking. Experimental results on several problems with sparse constraints show that the component-based ranking strategy performs better than the stochastic ranking.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 3925-3929
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.3925
• Online since: May 2014
• Authors: Yu Wu*, Jing Wei Liu, Yue Hong Xie
• Keywords: Constrained Optimization, Evolutionary Algorithm (EA), Ranking Strategy

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