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Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

Kangshun Li, Zhuozhi Liang, Shuling Yang, Zhangxing Chen, Hui Wang, Zhiyi Lin

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)13(1)

ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522564560|DOI: 10.4018/IJCINI.2019010104

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MLA

Li, Kangshun, et al. "Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape." IJCINI vol.13, no.1 2019: pp.36-61. http://doi.org/10.4018/IJCINI.2019010104

APA

Li, K., Liang, Z., Yang, S., Chen, Z., Wang, H., & Lin, Z. (2019). Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 13(1), 36-61. http://doi.org/10.4018/IJCINI.2019010104

Chicago

Li, Kangshun, et al. "Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 13, no.1: 36-61. http://doi.org/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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Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

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