Abstract
How to balance exploration and exploitation is a key issue for evolution algorithm including differential algorithm (DE). Many researchers propose various improved mutation strategies to solve this issue for DE. Most of them can be classified as deterministic rules. That is to say, they select individuals according to predetermined methods and so the balance is static. However, different evolution stages require different balance between exploration and exploitation. In order to solve this problem, a general selection method named adaptive stochastic ranking based mutation strategies in DE(ASR-DE). In ASR-DE, it uses stochastic ranking method to rank all individuals according to their contribution in exploration and exploitation. The parameter P\(_f\) in stochastic ranking is adaptive controlled by a transform version of success rate. The individuals with the smaller ranking are more likely to be selected. 28 functions of CEC2013 is used here to verify the validity of testing method. The test results show that ASR-DE improves the standard DE and improved DE comparing with other methods.
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Acknowledgement
Supported by National Key Research and Development Program of China under grant number 2017YFC1405403, and National Natural Science Foundation of China under grant number 6107505961300127, and Green Industry Technology Leding Project (product development category) of Hubei University of Technology under grant number CPYF2017008, and Doctor’s program of Hubei University of Technology under grant number BSQD2017047, BSQD2017045.
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Xia, D., Lin, S., Yan, M., Xiong, C., Li, Y. (2019). A General Selection Method for Mutation Strategy in Differential Evolution. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_14
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DOI: https://doi.org/10.1007/978-981-13-6473-0_14
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