Improving Proximity and Diversity in Multiobjective Evolutionary Algorithms

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概要

• 論文の詳細を見る

• This paper presents an approach for improving proximity and diversity in multiobjective evolutionary algorithms (MOEAs). The idea is to discover new nondominated solutions in the promising area of search space. It can be achieved by applying mutation only to the most converged and the least crowded individuals. In other words, the proximity and diversity can be improved because new nondominated solutions are found in the vicinity of the individuals highly converged and less crowded. Empirical results on multiobjective knapsack problems (MKPs) demonstrate that the proposed approach discovers a set of nondominated solutions much closer to the global Pareto front while maintaining a better distribution of the solutions.

• （社）電子情報通信学会の論文
• 2010-10-01

著者

• Ahn Chang

  School Of Information & Communication Engineering Sungkyunkwan University

• KIM Yehoon

  Department of Electrical Engineering, Samsung Advanced Institute of Technology

• Ahn Chang

  School Of Electrical Engineering Korea University:(present Address)department Of Information And Com

• Kim Yehoon

  Department Of Electrical Engineering Samsung Advanced Institute Of Technology

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