A Novel Fitness Function Based on Decomposition for Multi-objective Optimization Problems

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• 关键词：Evolutionary algorithm ; Multi ; objective optimization ; Fitness function ; Aggregation function ; Elliptic function
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9772
• 期：1
• 页码：16-25
• 全文大小：129 KB
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• 作者单位：Cai Dai (15)
  Xiujuan Lei (15)
  Xiaofang Guo (16)
  
  15. College of Computer Science, Shaanxi Normal University, Xi’an, 710062, China
  16. School of Science, Xi’an Technological University, Xi’an, Shaanxi, China
  
• 丛书名：Intelligent Computing Theories and Application
• ISBN：978-3-319-42294-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9772

文摘

Research on multi-objective optimization problems (MOPs) becomes one of the hottest topics of intelligent computation. The diversity of obtained solutions is of great importance for multi-objective evolutionary algorithms. To this end, in this paper, a novel fitness function based on decomposition is proposed to help solutions converge toward to the Pareto optimal solutions and maintain the diversity of solutions. First, the objective space is decomposed in a set of sub-regions based on a set of direction vectors and obtained solutions are classified. Then, for an obtained solution, the size of the class which contains the solution and an aggregation function value of the solution are used to calculate the fitness value of the solution. Aggregation function which decides whether the target space is divided evenly plays a very important role in the fitness function. A hyperellipsoidal function is designed for any-objective problems. The proposed algorithm has been compared with NSGAII and MOEA/D on various continuous test problems. Experimental results show that the proposed algorithm can find more accurate Pareto front with better diversity in most problems, and the hyperellipsoidal function works better than the weighted Tchebycheff.