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An integrated cuckoo search optimizer for single and multi-objective optimization problems
Author(s) -
Xiangbo Qi,
Zhonghu Yuan,
Yan Song
Publication year - 2021
Publication title -
peerj computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.370
Subject(s) - cuckoo search , benchmark (surveying) , computer science , set (abstract data type) , mathematical optimization , cuckoo , algorithm , mathematics , particle swarm optimization , geodesy , programming language , geography , zoology , biology
Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.

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