A preference multi-objective optimization based on adaptive rank clone and differential evolution

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• 作者：Ruochen Liu (1)
  Xiao Wang (1)
  Jing Liu (1)
  Lingfen Fang (1)
  Licheng Jiao (1)
  
• 关键词：Preference multi ; objective optimization ; Artificial immune system ; Differential evolution ; Preference rank
• 刊名：Natural Computing
• 出版年：2013
• 出版时间：March 2013
• 年：2013
• 卷：12
• 期：1
• 页码：109-132
• 全文大小：1,099 KB
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• 作者单位：Ruochen Liu (1)
  Xiao Wang (1)
  Jing Liu (1)
  Lingfen Fang (1)
  Licheng Jiao (1)
  
  1. Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China
  
• ISSN：1572-9796

文摘

Evolutionary multi-objective optimization (EMO) algorithms have been used in various real-world applications. However, most of the Pareto domination based multi-objective optimization evolutionary algorithms are not suitable for many-objective optimization. Recently, EMO algorithm incorporated decision maker’s preferences became a new trend for solving many-objective problems and showed a good performance. In this paper, we first use a new selection scheme and an adaptive rank based clone scheme to exploit the dynamic information of the online antibody population. Moreover, a special differential evolution (DE) scheme is combined with directional information by selecting parents for the DE calculation according to the ranks of individuals within a population. So the dominated solutions can learn the information of the non-dominated ones by using directional information. The proposed method has been extensively compared with two-archive algorithm, light beam search non-dominated sorting genetic algorithm II and preference rank immune memory clone selection algorithm over several benchmark multi-objective optimization problems with from two to ten objectives. The experimental results indicate that the proposed algorithm achieves competitive results.