Article: Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem Journal: International Journal of Bio-Inspired Computation (IJBIC) 2019 Vol.13 No.2 pp.102 - 110 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. Inderscience Publishers - linking academia, business and industry through research

Title: Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem

Authors: Xin Zhang; Xiang-tao Li; Ming-hao Yin

Addresses: College of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China ' College of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China ' College of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China

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.

Keywords: cuckoo search; covariance matrix adaptation evolutionary strategy; global optimisation; self-adaptive method; cumulation.

DOI: 10.1504/IJBIC.2019.098403

International Journal of Bio-Inspired Computation, 2019 Vol.13 No.2, pp.102 - 110

Received: 18 Jan 2016
Accepted: 14 Jan 2017
Published online: 19 Mar 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem

Keep up-to-date