An evaluation of non-redundant objective sets based on the spatial similarity ratio

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• 作者：Juan Zou ; Jinhua Zheng ; Cheng Deng ; Ruimin Shen
• 关键词：Multi ; objective optimization ; Redundant objective reduction algorithm ; Non ; redundant objective sets ; Spatial similarity ratio ; Evaluation methodology
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：19
• 期：8
• 页码：2275-2286
• 全文大小：3,494 KB
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• 作者单位：Juan Zou (1) (2)
  Jinhua Zheng (1) (2)
  Cheng Deng (1) (2)
  Ruimin Shen (1) (2)
  
  1. Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China
  2. Key Laboratory of Intelligent Computing and Information Processing (Xiangtan University), Ministry of Education, Xiangtan, Hunan Province, China
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

Multi-objective optimization is a challenging task in many disciplines. Although a number of algorithms have simplified this problem by degenerating redundant objectives into low-dimensional sets, there is currently no consensus method for evaluating their performance. In this paper, we propose an evaluation method that uses a spatial similarity ratio (SSR) to determine the quality of non-redundant objective sets (NRSs). We consider the reduction of all NRSs of three functions from 5D to 2D or 3D using our SSR-based method, and compare the results to those given by an inverted generational distance-based method. The results demonstrate that our method is more accurate, as it takes information from both the non-redundant and redundant objective sets into consideration. In addition, using the proposed SSR-based approach, no prior knowledge of the true Pareto set is required. Therefore, we can conclude that our SSR-based method is feasible for the assessment of non-redundant objective sets.