Niching chimp optimization for constraint multimodal engineering optimization problems

•Applying the Niching concept to ChOA (NChOA).•The NChOA proposes a novel constraint handling technique.•The NChOA's performance was evaluated on 37 numerical functions.•The NChOAs’ performance was tested on twelve real-world constraint problems. Two significant concerns need to be addressed to...

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Vydáno v:Expert systems with applications Ročník 198; s. 116887
Hlavní autoři: Gong, Shuo-Peng, Khishe, Mohammad, Mohammadi, Mokhtar
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 15.07.2022
Elsevier BV
Témata:
Algorithms
Benchmarks
Chemical synthesis
Chimp optimization algorithm
Constraint optimization
Constraints
Engineering
Livestock
Multimodal functions
Niching concept
Optimization
Searching
Swarm intelligence
Constraint optimization
Niching concept
Chimp optimization algorithm
Multimodal functions
ISSN:0957-4174, 1873-6793
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Shrnutí:•Applying the Niching concept to ChOA (NChOA).•The NChOA proposes a novel constraint handling technique.•The NChOA's performance was evaluated on 37 numerical functions.•The NChOAs’ performance was tested on twelve real-world constraint problems. Two significant concerns need to be addressed to handle multimodal problems: classifying various local/global optima and preserving these optimum values until the termination. Besides, a comprehensive local search ability is also a need to achieve the exact global optima. Chimp Optimization Algorithm (ChOA) is a recently swarm intelligence algorithm that needs less parameter tuning. The ChOA, on the other hand, is prone to early convergence and fails to strike a balance between exploration and exploitation when it comes to resolving multimodal challenges. In order to overcome these concerns, this paper embeds the niching technique in ChOA (NChOA) that includes the personal best qualities of PSO and a local search technique. To evaluate the NChOA’s performance, we analyze it against fifteen frequently utilized multimodal numerical test functions, ten complex IEEE CEC06-2019 suit tests, and twelve constrained real-world optimization problems in a variety of engineering fields, including industrial chemical producer, process design and synthesis, mechanical design, power system, power-electronic, and livestock teed ration problems. The results indicate that the NChOA outperforms several benchmark algorithms and sixteen out of eighteen state-of-the-art algorithms by an average rank of Friedman test greater than 81% for 25 numerical functions and twelve engineering problems while outperforming jDE100 and DISHchain1e + 12 by 22% and 41%, respectively. The Bonferroni-Dunn and Holm tests demonstrated that NChOA outperforms all benchmark and state-of-the-art algorithms for all numerical functions and engineering tasks while performing comparably to jDE100 and DISHchain1e + 12. The proposed NChOA, we believe, can be used to address difficulties involving multimodal search spaces. Additionally, NChOA is more broadly applicable than rival benchmarks to a broader range of engineering applications.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.116887