Volume 37, Number 5, October 2019
|Page(s)||1000 - 1010|
|Published online||14 January 2020|
Adaptive Constrained Differential Evolution Algorithm by Using Generalized Opposition-Based Learning
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
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
关键字 : 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
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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