Hybrid Cuckoo Search Algorithm with Covariance Matrix Adaption Evolution Strategy for Global optimisation Problem

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.