Title: A new hybrid method based on krill herd and cuckoo search for global optimisation tasks

Authors: Gai-Ge Wang; Amir H. Gandomi; Xin-She Yang; Amir H. Alavi

Addresses: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu, 221116, China; Institute of Algorithm and Big Data Analysis, Northeast Normal University, Changchun, 130117, China; School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China ' BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA ' School of Engineering and Information Sciences, Middlesex University, Hendon, London NW4 4BT, UK ' Department of Civil and Environmental Engineering, Michigan State University, East Lansing, Michigan, USA

Abstract: Recently, Gandomi and Alavi proposed a new heuristic search method, called krill herd (KH), for solving global optimisation problems. In order to make KH more effective, a hybrid meta-heuristic cuckoo search and krill herd (CSKH) method is proposed for function optimisation. The CSKH introduces krill updating (KU) and krill abandoning (KA) operator originated from cuckoo search (CS) during the process when the krill updating so as to greatly enhance its effectiveness and reliability dealing with numerical optimisation problems. The KU operator inspires the intensive exploitation and allows the krill individuals implement a careful search in the later run phase of the search, while KA operator is used to further enhance the exploration of the CSKH in place of a fraction of the worse krill at the end of each generation. The effectiveness of these improvements is tested by 14 standard benchmarking functions and experimental results show, in most cases, this hybrid meta-heuristic CSKH algorithm is more effective and efficient than the original KH and other approaches.

Keywords: global optimisation; krill herd; cuckoo search; krill updating operator; krill abandoning operator; hybrid metaheuristics.

DOI: 10.1504/IJBIC.2016.079569

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.5, pp.286 - 299

Received: 12 Mar 2013
Accepted: 10 Oct 2013
Published online: 04 Oct 2016 *

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