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论文:2019,Vol:37,Issue(5):1000-1010 |
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引用本文： |
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吴文海, 郭晓峰, 周思羽, 刘锦涛. 基于广义反向学习的自适应约束差分进化算法[J]. 西北工业大学学报 |
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WU Wenhai, GUO Xiaofeng, ZHOU Siyu, LIU Jintao. Adaptive Constrained Differential Evolution Algorithm by Using Generalized Opposition-Based Learning[J]. Northwestern polytechnical university |
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| 基于广义反向学习的自适应约束差分进化算法 |
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| 吴文海1, 郭晓峰1, 周思羽1, 刘锦涛2 |
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1. 海军航空大学(青岛校区) 航空仪电控制工程与指挥系, 山东 青岛 266041;
2. 陆军工程大学 指挥控制工程学院, 江苏 南京 210002 |
| 摘要: |
| 差分进化算法是一种基于"贪婪竞争"机制的全局寻优算法，其控制参量少、结构简单，具有较高的可靠性和收敛性，将约束处理机制引入到差分进化算法可以高效解决约束优化问题。提出一种基于广义反向学习的自适应约束差分进化算法，利用广义反向学习机制生成初始种群并执行种群"代跳"操作，采用自适应权衡模型将约束区分状态处理以及改进自适应变异操作对个体进行排序变异。通过与CDE、DDE、A-DDE、εDE以及DPDE算法进行试验比较以及对广义反向学习和改进自适应排序操作性能分析证明该算法具有较好的寻优精度及收敛速度。 |
| 关键词:
约束优化
差分进化算法
广义反向学习
自适应
权衡模型
排序变异操作
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| Adaptive Constrained Differential Evolution Algorithm by Using Generalized Opposition-Based Learning |
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| WU Wenhai1, GUO Xiaofeng1, ZHOU Siyu1, LIU Jintao2 |
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1. Department of Aviation Control and Command, Qingdao Campus, Naval Aviation University, Qingdao 266041, China;
2. College of Command and Control Engineering, Army Engineering University, Nanjing 210002, China |
| Abstract: |
| Differential evolution is a global optimization algorithm based on greedy competition mechanism, which has the advantages of simple structure, less control parameters, higher reliability and convergence. Combining with the constraint-handling techniques, the constraint optimization problem can be efficiently solved. An adaptive differential evolution algorithm is proposed by using generalized opposition-based learning (GOBL-ACDE), in which the generalized opposition-based learning is used to generate initial population and executes the generation jumping. And the adaptive trade-off model is utilized to handle the constraints as the improved adaptive ranking mutation operator is adopted to generate new population. The experimental results show that the algorithm has better performance in accuracy and convergence speed comparing with CDE, DDE, A-DDE and. And the effect of the generalized opposition-based learning and improved adaptive ranking mutation operator of the GOBL-ACDE have been analyzed and evaluated as well. |
| Key words:
constrained optimization
differential evolution
generalized opposition-based learning
adaptation
trade-off model
ranking mutation
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| 收稿日期: 2018-10-08
修回日期:
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| DOI: 10.1051/jnwpu/20193751000 |
| 基金项目: 国家重点研发计划（2018YFC0806900，2016YFC0800606，2016YFC0800310）资助 |
| 通讯作者:
Email： |
| 作者简介: 吴文海(1962-),海军航空大学教授、博士生导师,主要从事精确制导与控制研究。
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