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A Penalty-Based Differential Evolution for Multimodal Optimization
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AI
DOI：10.1109/TCYB.2021.3117359.png)
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Abstract
En 中文
It is very difficult to locate multiple global optimal solutions (GOSs) of multimodal optimization problems (MMOPs). To deal with this issue, a penalty-based multimodal optimization differential evolution (DE), called PMODE, is developed in this article. In PMODE, a penalty strategy with a dynamic penalty radius is constructed to solve MMOPs. An elite selection mechanism is designed to identify and select elite solutions. The neighboring areas of these elite solutions are penalized. PMODE uses a popular DE variant--JADE as its search engine. The proposed PMODE is compared with several other state-of-the-art multimodal optimization algorithms on 20 MMOPs used in the IEEE CEC2013 special session. The experimental results show that PMODE performs better than other state-of-the-art methods.
Keywords:
Optimization
Statistics
Sociology
Heuristic algorithms
Linear programming
Clustering algorithms
Urban areas
Differential evolution (DE)
multimodal optimization problems (MMOPs)
penalty strategy
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W