A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization

被引:1
|
作者
Peng, Funan [1 ,2 ,3 ]
Lv, Li [1 ,2 ]
Chen, Weiru [3 ]
Wang, Jun [3 ]
机构
[1] Chinese Acad Sci, Shenyang Inst Comp Technol, Shenyang 110168, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 101408, Peoples R China
[3] Shenyang Univ Chem Technol, Coll Comp Sci & Technol, Shenyang 110142, Peoples R China
关键词
many-objective optimization; decomposition; evolutionary algorithm; projection plane; free dimension; objective domain; INDICATOR;
D O I
10.3390/pr11051564
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Many-objective optimization problems (MaOPs) are challenging optimization problems in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides the objective space into projection plane and free dimension(s). The balance between convergence and diversity is maintained using a Bi-Elite queue. The MOEA/PII is not only an algorithm, but also an algorithm framework. We can choose a decomposition-based or dominance-based algorithm to be the free dimension algorithm. When it is an algorithm framework, it exhibits a better performance. We compare the performance of the algorithm and the algorithm with the MOEA/PII framework. The performance is evaluated by benchmark test instances DTLZ1-7 and WFG1-9 on 3, 5, 8, 10, and 15 objectives using IGD-metric and HV-metric. In addition, we investigated its superior performance on the wireless sensor networks deployment problem using C-metric. Moreover, determining objective domain for the objects of the wireless sensor networks deployment problem reduces the time and makes the solution set more responsive to user needs.
引用
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页数:22
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