Hybrid multi-strategy chaos somersault foraging chimp optimization algorithm research

To address the problems of slow convergence speed and low accuracy of the chimp optimization algorithm (ChOA), and to prevent falling into the local optimum, a chaos somersault foraging ChOA (CSFChOA) is proposed. First, the cat chaotic sequence is introduced to generate the initial solutions, and t...

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Vydáno v:Mathematical biosciences and engineering : MBE Ročník 20; číslo 7; s. 12263 - 12297
Hlavní autoři: Yang, Xiaorui, Zhang, Yumei, Lv, Xiaojiao, Yang, Honghong, Sun, Zengguo, Wu, Xiaojun
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States AIMS Press 01.01.2023
Témata:
cat chaotic sequence
chimp optimization algorithm
convergence
local optimum
opposition-based learning
somersault foraging
cat chaotic sequence
somersault foraging
opposition-based learning
chimp optimization algorithm
convergence
local optimum
ISSN:1551-0018, 1551-0018
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Shrnutí:To address the problems of slow convergence speed and low accuracy of the chimp optimization algorithm (ChOA), and to prevent falling into the local optimum, a chaos somersault foraging ChOA (CSFChOA) is proposed. First, the cat chaotic sequence is introduced to generate the initial solutions, and then opposition-based learning is used to select better solutions to form the initial population, which can ensure the diversity of the algorithm at the beginning and improve the convergence speed and optimum searching accuracy. Considering that the algorithm is likely to fall into local optimum in the final stage, by taking the optimal solution as the pivot, chimps with better adaptation at the mirror image position replace chimps from the original population using the somersault foraging strategy, which can increase the population diversity and expand the search scope. The optimization search tests were performed on 23 standard test functions and CEC2019 test functions, and the Wilcoxon rank sum test was used for statistical analysis. The CSFChOA was compared with the ChOA and other improved intelligent optimization algorithms. The experimental results show that the CSFChOA outperforms most of the other algorithms in terms of mean and standard deviation, which indicates that the CSFChOA performs well in terms of the convergence accuracy, convergence speed and robustness of global optimization in both low-dimensional and high-dimensional experiments. Finally, through the test and analysis comparison of two complex engineering design problems, the CSFChOA was shown to outperform other algorithms in terms of optimal cost. For the design of the speed reducer, the performance of the CSFChOA is 100% better than other algorithms in terms of optimal cost; and, for the design of a three-bar truss, the performance of the CSFChOA is 6.77% better than other algorithms in terms of optimal cost, which verifies the feasibility, applicability and superiority of the CSFChOA in practical engineering problems.
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ISSN:1551-0018
1551-0018
DOI:10.3934/mbe.2023546