Abstract
Differential evolution (DE) is a recently invented global optimization algorithm. In this paper, a new differential evolution algorithm, self-adaptive chaos differential evolution (SACDE) with chaos mutation factor and dynamically changing weighting factor and crossover factor is presented. The evolution speed factor and aggregation degree factor of the population are introduced in this new algorithm. In each iteration process, the weighting factor is changed dynamically based on the current aggregation degree factor, and the crossover factor is changed dynamically based on the current evolution speed factor. The chaos mutation factor is introduced to avoid falling into the local optimum. DE and SACDE are tested with two well-known benchmark functions. The experiments show that the convergence speed of SACDE is significantly superior to DE, the convergence accuracy is also increased.
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References
DE Homepage: http://www.icsi.berkeley.edu/~storn/code.html
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhenyu, G., Bo, C., Min, Y., Binggang, C. (2006). Self-Adaptive Chaos Differential Evolution. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_128
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DOI: https://doi.org/10.1007/11881070_128
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
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