Improved intelligent clonal optimizer based on adaptive parameter strategy

Jiahao Zhang; Zhengming Gao; Suruo Li; Juan Zhao; Wenguang Song; Jiahao Zhang; Zhengming Gao; Suruo Li; Juan Zhao; Wenguang Song

doi:10.3934/mbe.2022481

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 10: 10275-10315. doi: 10.3934/mbe.2022481

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Research article

Improved intelligent clonal optimizer based on adaptive parameter strategy

1.
School of computer science, Yangtze University, Jingzhou 434000, China
2.
School of computer engineering, Jingchu University of Technology, Jingmen 448000, China
3.
School of electronics and information engineering, Jingchu University of Technology, Jingmen 448000, China

Academic Editor: Hamid Reza Karimi

Received: 01 June 2022 Revised: 11 July 2022 Accepted: 12 July 2022 Published: 21 July 2022

The intelligent clonal optimizer (ICO) is a new evolutionary algorithm, which adopts a new cloning and selection mechanism. In order to improve the performance of the algorithm, quasi-opposition-based and quasi-reflection-based learning strategy is applied according to the transition information from exploration to exploitation of ICO to speed up the convergence speed of ICO and enhance the diversity of the population. Furthermore, to avoid the stagnation of the optimal value update, an adaptive parameter method is designed. When the update of the optimal value falls into stagnation, it can adjust the parameter of controlling the exploration and exploitation in ICO to enhance the convergence rate of ICO and accuracy of the solution. At last, an improved intelligent chaotic clonal optimizer (IICO) based on adaptive parameter strategy is proposed. In this paper, twenty-seven benchmark functions, eight CEC 2104 test functions and three engineering optimization problems are used to verify the numerical optimization ability of IICO. Results of the proposed IICO are compared to ten similar meta-heuristic algorithms. The obtained results confirmed that the IICO exhibits competitive performance in convergence rate and accurate convergence.
- continuous optimization problems,
- opposition-based learning,
- intelligent clonal optimizer,
- adaptive parameter
Citation: Jiahao Zhang, Zhengming Gao, Suruo Li, Juan Zhao, Wenguang Song. Improved intelligent clonal optimizer based on adaptive parameter strategy[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 10275-10315. doi: 10.3934/mbe.2022481

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Abstract

The intelligent clonal optimizer (ICO) is a new evolutionary algorithm, which adopts a new cloning and selection mechanism. In order to improve the performance of the algorithm, quasi-opposition-based and quasi-reflection-based learning strategy is applied according to the transition information from exploration to exploitation of ICO to speed up the convergence speed of ICO and enhance the diversity of the population. Furthermore, to avoid the stagnation of the optimal value update, an adaptive parameter method is designed. When the update of the optimal value falls into stagnation, it can adjust the parameter of controlling the exploration and exploitation in ICO to enhance the convergence rate of ICO and accuracy of the solution. At last, an improved intelligent chaotic clonal optimizer (IICO) based on adaptive parameter strategy is proposed. In this paper, twenty-seven benchmark functions, eight CEC 2104 test functions and three engineering optimization problems are used to verify the numerical optimization ability of IICO. Results of the proposed IICO are compared to ten similar meta-heuristic algorithms. The obtained results confirmed that the IICO exhibits competitive performance in convergence rate and accurate convergence.

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