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

作者：Zou, Juan; Zheng, Jinhua^*; Deng, Cheng; Shen, Ruimin

来源：Soft Computing, 2015, 19(8): 2275-2286.

DOI：10.1007/s00500-014-1409-3

• Multi-objective optimization
• Redundant objective reduction algorithm
• Non-redundant objective sets
• Spatial similarity ratio
• Evaluation methodology

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 nonredundant 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 nonredundant objective sets.

• 出版日期2015-8
• 单位湘潭大学; 中国人民解放军信息工程大学