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Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape
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Author(s): Kangshun Li (South China Agricultural University, Guangzhou, China), Zhuozhi Liang (South China Agricultural University, Guangzhou, China), Shuling Yang (South China University of Technology, Guangzhou, China), Zhangxing Chen (University of Calgary, Calgary, Canada), Hui Wang (South China Agricultural University, Guangzhou, China)and Zhiyi Lin (Guangdong University of Technology, Guangzhou, China)
Copyright: 2019
Volume: 13
Issue: 1
Pages: 26
Source title:
International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)
Editor(s)-in-Chief: Kangshun Li (South China Agricultural University, China)
DOI: 10.4018/IJCINI.2019010104
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Abstract
Dynamic fitness landscape analyses contain different metrics to attempt to analyze optimization problems. In this article, some of dynamic fitness landscape metrics are selected to discuss differential evolution (DE) algorithm properties and performance. Based on traditional differential evolution algorithm, benchmark functions and dynamic fitness landscape measures such as fitness distance correlation for calculating the distance to the nearest global optimum, ruggedness based on entropy, dynamic severity for estimating dynamic properties, a fitness cloud for getting a visual rendering of evolvability and a gradient for analyzing micro changes of benchmark functions in differential evolution algorithm, the authors obtain useful results and try to apply effective data, figures and graphs to analyze the performance differential evolution algorithm and make conclusions. Those metrics have great value and more details as DE performance.
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