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Abstract Convex Underestimation Assisted Multistage Differential Evolution
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DOI：10.1109/TCYB.2017.2710626.png)
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摘要
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In differential evolution (DE), different strategies applied in different evolutionary stages may be more effective than a single strategy used in the entire evolutionary process. However, it is not trivial to appropriately determine the evolutionary stage. In this paper, we present an abstract convex underestimation-assisted multistage DE. In the proposed algorithm, the underestimation is calculated through the supporting vectors of some neighboring individuals. Based on the variation of the average underestimation error (UE), the evolutionary process is divided into three stages. Each stage includes a pool of suitable candidate strategies. At the beginning of each generation, the evolutionary stage is first estimated according to the average UE of the previous generation. Subsequently, a strategy is automatically chosen from the corresponding candidate pool to create a mutant vector. In addition, a centroid-based strategy which utilizes the information of multiple superior individuals is designed to balance the population diversity and convergence speed in the second stage. Experiments are conducted on 23 widely used test functions, CEC 2013, and CEC 2014 benchmark sets to demonstrate the performance of the proposed algorithm. The results reveal that the proposed algorithm exhibits better performance compared with several advanced DE variants and some non-DE approaches.
Keyword:
Abstract convex
differential evolution (DE)
evolutionary algorithm (EA)
global optimization
underestimation
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IF:
10.5
论文数:
1.1W
被引数:
5.0W
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