Abstract
This paper presents a hybrid differential evolution (DE) algorithm based on chaos and generalized opposition-based learning (GOBL). In this algorithm, GOBL strategy transforms current search space into a new search space with a random probability, which provides more opportunities for the algorithm to find the global optimum. When the GOBL strategy isn’t executed, the chaotic operator, like a mutation operator, will be introduced to help the DE to jump out local optima and improve the global convergence rate. Simulation results show that this hybrid DE algorithm can electively enhance the searching efficiency and greatly improve the searching quality.
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Wang, J., Wu, Z., Wang, H. (2010). Hybrid Differential Evolution Algorithm with Chaos and Generalized Opposition-Based Learning. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_11
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DOI: https://doi.org/10.1007/978-3-642-16493-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16492-7
Online ISBN: 978-3-642-16493-4
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