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A collaborative cuckoo search algorithm with modified operation mode

Lookup NU author(s): Professor Qiangda Yang, Dr Huang Huang, Dr Jie ZhangORCiD, Dr Peng Liu

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).

Abstract

Cuckoo search (CS) is a nature-inspired algorithm that has shown its favorable potentialfor solving complex optimization problems. Nevertheless, there is a lack of effective informationsharing between individuals in CS, which would doubtless limit its achievable performance. Whileseveral CS variants have considered this issue, they commonly strengthen the information sharing injust one of the two search parts (i.e., global and local search parts). In this paper, to further addressthe above issue and to get a more rational allocation of the workloads of global search and localsearch, a new CS variant called collaborative CS with modified operation mode (CCSMO) isproposed. One novelty is that a collaborative mechanism is presented to strengthen the informationsharing and collaboration between individuals in both search parts, and correspondingly, two newiterative strategies are introduced respectively for global search and local search. Another novelty isthat the conventional operation mode􀀃adopted by almost all existing CS-based algorithms is modifiedfor more rationally allocating the workloads of global search and local search. To validate theperformance of CCSMO, extensive experiments and comparisons between CCSMO and 17state-of-the-art algorithms are made on two popular test suites from IEEE Conference onEvolutionary Computation (CEC). Besides, the algorithm is also applied to solve three􀀃engineeringdesign problems and one large-scale combined heat and power economic dispatch problem. Theresults demonstrate that CCSMO can offer highly competitive performance. Additionally, the timecomplexity, search behavior, modification effectiveness, and parameter sensitivity of CCSMO arealso evaluated.

Publication metadata

Author(s): Yang Q, Huang H, Zhang J, Gao H, Liu P

Publication type: Article

Publication status: Published

Journal: Engineering Applications of Artificial Intelligence

Year: 2023

Volume: 121

Print publication date: 01/05/2023

Online publication date: 28/02/2023

Acceptance date: 13/02/2023

Date deposited: 15/02/2023

ISSN (print): 0952-1976

ISSN (electronic): 1873-6769

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.engappai.2023.106006

DOI: 10.1016/j.engappai.2023.106006

ePrints DOI: 10.57711/qyzx-1t87

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