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Lookup NU author(s): Professor Qiangda Yang, Dr Huang Huang, Dr Jie ZhangORCiD, Dr Peng Liu
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
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 modeadopted 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 threeengineeringdesign 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.
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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