Effects of corner weight vectors on the performance of decomposition-based

被引:4
作者
He, Linjun [1 ,3 ]
Camacho, Auraham [2 ]
Nan, Yang [1 ]
Trivedi, Anupam [3 ]
Ishibuchi, Hisao [1 ]
Srinivasan, Dipti [3 ]
机构
[1] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China
[2] CINVESTAV Unidad Tamaulipas, Victoria, Mexico
[3] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117575, Singapore
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
Decomposition-based multiobjective; evolutionary algorithm; Multiobjective optimization; Many-objective optimization; Weight vectors; Corner solutions; MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS; MANY-OBJECTIVE OPTIMIZATION; NORMAL-BOUNDARY INTERSECTION; GENETIC LOCAL SEARCH; REFERENCE-POINT; MOEA/D; SELECTION; STRATEGY;
D O I
10.1016/j.swevo.2023.101305
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, it was demonstrated that a decomposition-based multiobjective evolutionary algorithm with a pre -specified weight vector set cannot find a uniformly-distributed solution set over an inverted triangular Pareto front (PF). This is because the weight vectors are created by a simplex-lattice structure with a triangular shape. Much more boundary solutions are often obtained than inside solutions. Whereas non-uniformity of obtained solutions has been discussed in many studies, it has been overlooked that solutions around the corners of the inverted triangular PF are not always obtained. This means that the obtained solution set is not only non-uniform but also covers only a part of the PF. In this paper, first we explain why the corner solutions of the inverted triangular PF cannot always be found using the relation between the weight vectors and the PF. Next, we propose a simple method for generating additional weight vectors for the search of the corner solutions. Then, we perform computational experiments after combining the proposed method with several decomposition-based algorithms. Experimental results demonstrate that the proposed method is able to improve the performance of the examined decomposition-based algorithms (including those with weight adaptation mechanisms) on multiobjective problems with various irregular PFs.
引用
收藏
页数:13
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