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Complex System Modeling and Simulation 2022, 2(1): 35-58 https://doi.org/10.23919/CSMS.2022.0004
Open Access | Issue | Published: 30 March 2022

Differential Evolution with Level-Based Learning Mechanism

Show Author's Information Kangjia Qiao1,  Jing Liang1( ),  Boyang Qu2,  Kunjie Yu1,  Caitong Yue1,  Hui Song3
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China
School of Engineering, RMIT University, Melbourne 3000, Australia
Keywords:
Differential Evolution (DE), parameter adaptation, level-based learning, exemplar selection
Cite this article:
Qiao K, Liang J, Qu B, et al. Differential Evolution with Level-Based Learning Mechanism. Complex System Modeling and Simulation, 2022, 2(1): 35-58. https://doi.org/10.23919/CSMS.2022.0004
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Abstract Full text About this article

To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.


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About this article

Differential Evolution with Level-Based Learning Mechanism

Show Author's information Kangjia Qiao1, Jing Liang1( ), Boyang Qu2, Kunjie Yu1, Caitong Yue1, Hui Song3
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China
School of Engineering, RMIT University, Melbourne 3000, Australia

Abstract

To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.

Keywords: Differential Evolution (DE), parameter adaptation, level-based learning, exemplar selection

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Published: 30 March 2022
Issue date: March 2022

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This work was supported in part by the National Natural Science Fund for Outstanding Young Scholars of China (No. 61922072), the National Natural Science Foundation of China (Nos. 61876169, 61276238, 61806179, and 61976237), and Key Research and Development and Promotion Projects in Henan Province (No. 192102210098).

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