2023
DOI: 10.1007/s00521-023-09236-y
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SEB-ChOA: an improved chimp optimization algorithm using spiral exploitation behavior

Leren Qian,
Mohammad Khishe,
Yiqian Huang
et al.
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Cited by 10 publications
(3 citation statements)
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“…For the CEC2019 100-Digit Challenge, each algorithm was run 50 times on each benchmark problem. The function grades were divided by Nc/25 for the twenty-five executions with the lowest assessment times [ 83 ]. Each challenge has a maximum possible score of 10, and the maximum possible score for the task is 100 if the highest 25 among the 50 runs for each of the Ten testing challenges correspond to 10 decimal places.…”
Section: Experimentation and Discussionmentioning
confidence: 99%
“…For the CEC2019 100-Digit Challenge, each algorithm was run 50 times on each benchmark problem. The function grades were divided by Nc/25 for the twenty-five executions with the lowest assessment times [ 83 ]. Each challenge has a maximum possible score of 10, and the maximum possible score for the task is 100 if the highest 25 among the 50 runs for each of the Ten testing challenges correspond to 10 decimal places.…”
Section: Experimentation and Discussionmentioning
confidence: 99%
“…There are many different types of MOAs to solve different types of multi-objective optimization problems (MOPs). The existing MOAs are faced with low speed of convergence [ 31 ], are easily trapped by local optima [ 32 , 33 ], lack diversity in solutions when solving MOPs [ 34 , 35 ], etc. Based on the single-objective besiege and conquer algorithm (BCA), we propose an MOBCA to solve these problems.…”
Section: Introductionmentioning
confidence: 99%
“…Extensive investigations have been carried out in the realm of power electronics and power conversion systems to optimize controller parameters. Diverse metaheuristic algorithms, such as atom search optimization (ASO) [ 36 ], weIghted meaN oF vectOrs optimizer (INFO) [ 37 ], hunger games search (HGS) optimizer [ 38 ], Aquila optimizer (AO) [ 39 ], particle swarm optimization [ 40 ], manta-ray foraging optimizer (MRFO) [ 41 ], chimp optimization algorithm (ChOA) [ [42] , [43] , [44] ], marine predators algorithm (MPA) [ 45 ], fuzzy whale optimization algorithm (FWOA) [ 46 ], grey wolf optimizer (GWO) [ 47 ], snow ablation optimizer (SAO) [ 48 ], and gorilla troops optimizer (GTO) [ 49 ] have been employed for this purpose. Snake optimizer (SO) [ 50 ] is a relatively new algorithm with limited utilization in power electronic converters and control in the literature at the time of writing this article.…”
Section: Introductionmentioning
confidence: 99%