期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 20, 期 3, 页码 475-480出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2015.2457616
关键词
Constraint; decomposition approach; evolutionary multiobjective optimization
资金
- Cheung Kong Scholars Programme of China [K5051302050]
- National Natural Science Foundation of China [61473241, 61273313, 61175063]
A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.
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