An adaptive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation
Differential evolution (DE) algorithm is prone to premature convergence and local optimization in solving complex optimization problems. In order to solve these problems, the belief space strategy, generalized opposition-based learning strategy and parameter adaptive strategy are introduced into DE...
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| Published in | Applied soft computing Vol. 127; p. 109419 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Differential evolution (DE) algorithm is prone to premature convergence and local optimization in solving complex optimization problems. In order to solve these problems, the belief space strategy, generalized opposition-based learning strategy and parameter adaptive strategy are introduced into DE to propose an improved adaptive DE algorithm, namely ACDE/F in this paper. In the ACDE/F, the idea of cultural algorithm and different mutation strategies are introduced into belief space to balance the global exploration ability and local optimization ability. A generalized opposition-based learning strategy is designed to improve the convergence speed of local optimization and increase the population diversity. A parameter adaptive adjustment strategy is developed to reasonably adjust the mutation factor and crossover factor to avoid to fall into local optimum. In order to test and verify the optimization performance of the ACDE/F, the unimodal functions and multimodal functions from CEC 2005 and CEC 2017 are selected in here. The experiment results show that the ACDE/F has better optimization performance than the DE with different strategies, WMSDE, DE2/F, GOAL-RNADE and DE/best/1. In addition, the actual gate allocation problem is selected to verify the practical application ability of the ACDE/F. The ACDE/F obtains the maximum allocation rate and average allocation rate of 98% and 96.8%, respectively. Therefore, the experimental results show that the ACDE/F can effectively solve the gate allocation problem and obtain ideal gate allocation results. |
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| AbstractList | Differential evolution (DE) algorithm is prone to premature convergence and local optimization in solving complex optimization problems. In order to solve these problems, the belief space strategy, generalized opposition-based learning strategy and parameter adaptive strategy are introduced into DE to propose an improved adaptive DE algorithm, namely ACDE/F in this paper. In the ACDE/F, the idea of cultural algorithm and different mutation strategies are introduced into belief space to balance the global exploration ability and local optimization ability. A generalized opposition-based learning strategy is designed to improve the convergence speed of local optimization and increase the population diversity. A parameter adaptive adjustment strategy is developed to reasonably adjust the mutation factor and crossover factor to avoid to fall into local optimum. In order to test and verify the optimization performance of the ACDE/F, the unimodal functions and multimodal functions from CEC 2005 and CEC 2017 are selected in here. The experiment results show that the ACDE/F has better optimization performance than the DE with different strategies, WMSDE, DE2/F, GOAL-RNADE and DE/best/1. In addition, the actual gate allocation problem is selected to verify the practical application ability of the ACDE/F. The ACDE/F obtains the maximum allocation rate and average allocation rate of 98% and 96.8%, respectively. Therefore, the experimental results show that the ACDE/F can effectively solve the gate allocation problem and obtain ideal gate allocation results. |
| ArticleNumber | 109419 |
| Author | Zhao, Huimin Ni, Hongcheng Chen, Huiling Deng, Wu Liu, Yi |
| Author_xml | – sequence: 1 givenname: Wu surname: Deng fullname: Deng, Wu organization: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China – sequence: 2 givenname: Hongcheng surname: Ni fullname: Ni, Hongcheng organization: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China – sequence: 3 givenname: Yi orcidid: 0000-0003-3411-2193 surname: Liu fullname: Liu, Yi organization: Research Center of Big Data and Information Management, Civil Aviation Management Institute of China, Beijing 100102, China – sequence: 4 givenname: Huiling surname: Chen fullname: Chen, Huiling email: chenhuiling.jlu@gmail.com organization: Key Laboratory of Intelligent Image Processing and Analysis, Wenzhou University, Wenzhou 325035, China – sequence: 5 givenname: Huimin surname: Zhao fullname: Zhao, Huimin email: hm_zhao1977@126.com organization: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China |
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| Keywords | Differential evolution Global optimization Gate allocation Parameter adaptation Belief space Opposition-based learning |
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