Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem
by Xin Zhang; Xiang-tao Li; Ming-hao Yin
International Journal of Bio-Inspired Computation (IJBIC), Vol. 13, No. 2, 2019

Abstract: Cuckoo search (CS) is an efficient bio-inspired algorithm and has been studied on global optimisation problems extensively. It is expert in solving complicated functions but converges slowly. Another optimisation algorithm, covariance matrix adaption evolution strategy (CMA_ES) can speed up the convergence rate via the self-adaptative mutation distribution and the cumulative evolution path, whereas it performs badly in complex functions. Therefore, in this paper, we devise a hybridisation of CS and CMA_ES and name it CS_CMA, to improve performance for the different optimisation problems. An evolved population is initialised at the beginning of iteration, using the information of previous evolution. Self-adaptive parameter adjustments are employed through the successful parameter values. To validate the performance of CS_CMA, comparative experiments are conducted based on seven high-dimensional benchmark functions provided for CEC 2008 and an engineering optimisation problem chosen from CEC' 2011, and computational results demonstrate that CS_CMA outperforms other competitor algorithms.

Online publication date: Tue, 19-Mar-2019

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