Application of improved chimp optimization algorithm integrating multiple strategies in reservoirs optimization operation

An improved chimp optimization algorithm integrating multiple strategies (MS-ICHOA) is developed to solve large-scale hydropower system operation optimization model. This method aims to comprehensively overcome the problem of falling into local optima easily in the later stage for the standard CHOA...

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Bibliographic Details
Published in:Expert systems with applications Vol. 285; p. 127945
Main Authors: Yang, Peiyao, Jiang, Zhiqiang, Qin, Hui, Zhang, Yongchuan, Jia, Benjun
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2025
Subjects:
Cascade reservoirs
Chimp optimization algorithm
Metaheuristic algorithm
Operation optimization
Power generation
Chimp optimization algorithm
Operation optimization
Cascade reservoirs
Metaheuristic algorithm
Power generation
ISSN:0957-4174
Online Access:Get full text
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Summary:An improved chimp optimization algorithm integrating multiple strategies (MS-ICHOA) is developed to solve large-scale hydropower system operation optimization model. This method aims to comprehensively overcome the problem of falling into local optima easily in the later stage for the standard CHOA by incorporating different strategies into each stage of the CHOA optimization principle, that is opposition-based learning strategy based on convex lens imaging and Cauchy mutation strategy, stochastic sine convergence factor, differential mutation strategy, water wave dynamic adaptive factor and sine cosine operator, as well as Metropolis criterion. The comparison results between the proposed method and various well-known intelligent algorithms on 23 classic benchmark functions and 30 CEC2017 benchmark functions show that the MS-ICHOA exhibits superior performance regarding statistical analysis, convergence, robustness and diversity. Finally, the case study on the operation optimization of China’s Three Gorges-Gezhouba cascade reservoirs shows that the MS-ICHOA can obtain satisfactory scheduling schemes for five typical inflow scenarios, namely ultra-wet, relatively wet, normal, relatively dry and ultra-dry. Meanwhile, the average power generation generated increase by 0.16 %–4.68 %, 0.13 %–4.22 %, 0.16 %–5.80 %, 0.29 %–6.23 % and 3.19 %–4.17 % than the comparison algorithms, respectively. Therefore, MS-ICHOA provides a powerful tool to handle complex engineering optimization problems.
ISSN:0957-4174
DOI:10.1016/j.eswa.2025.127945