期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 22, 期 6, 页码 894-908出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2017.2769108
关键词
Affinity propagation clustering (APC); archive technique; differential evolution (DE); dual-strategy differential evolution (DSDE); multimodal optimization problems (MMOPs)
资金
- National Natural Science Foundations of China [61772207, 61402545, 61332002]
- Natural Science Foundations of Guangdong Province for Distinguished Young Scholars [2014A030306038]
- Project for Pearl River New Star in Science and Technology [201506010047]
- GDUPS
- Fundamental Research Funds for the Central Universities
- Hong Kong RGC General Research Fund [9042038, CityU 11205314]
Multimodal optimization problem (MMOP), which targets at searching for multiple optimal solutions simultaneously, is one of the most challenging problems for optimization. There are two general goals for solving MMOPs. One is to maintain population diversity so as to locate global optima as many as possible, while the other is to increase the accuracy of the solutions found. To achieve these two goals, a novel dual-strategy differential evolution (DSDE) with affinity propagation clustering (APC) is proposed in this paper. The novelties and advantages of DSDE include the following three aspects. First, a dual-strategy mutation scheme is designed to balance exploration and exploitation in generating offspring. Second, an adaptive selection mechanism based on APC is proposed to choose diverse individuals from different optimal regions for locating as many peaks as possible. Third, an archive technique is applied to detect and protect stagnated and converged individuals. These individuals are stored in the archive to preserve the found promising solutions and are reinitialized for exploring more new areas. The experimental results show that the proposed DSDE algorithm is better than or at least comparable to the state-of-the-art multimodal algorithms when evaluated on the benchmark problems from CEC2013, in terms of locating more global optima, obtaining higher accuracy solution, and converging with faster speed.
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