引用本文:耿焕同,周山胜,陈哲,等.基于分解的预测型动态多目标粒子群优化算法[J].控制与决策,2019,34(6):1307-1318
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基于分解的预测型动态多目标粒子群优化算法
耿焕同,周山胜,陈哲,韩伟民
(南京信息工程大学计算机与软件学院,南京210044)
摘要:
针对动态多目标问题求解,提出一种基于分解的预测型动态多目标粒子群优化算法.首先借助分解思想,将目标问题划分为多个不同的子问题,当问题动态变化时,选择对应于不同子问题的优化个体检测环境变化程度,以提高算法对不同动态问题的适应与响应能力;然后,设计一种群体预测策略,通过将目标空间中相同收敛方向上不同时刻的个体位置转换为时间序列,引入时间序列预测方法预测下一刻位置,从而提高预测种群的多样性和有效性,进而有效减少算法在问题变化后的收敛时间;最后,为避免问题发生变化后个体与子问题不匹配,设计一种再匹配策略,以提高预测策略的准确性.实验结果表明,在6个标准动态多目标测试问题上,与2个动态多目标优化算法进行比较,所提出算法在收敛性、分布性与稳定性上均具有显著优势.
关键词:  动态多目标优化  预测  粒子群优化  分解
DOI:10.13195/j.kzyjc.2017.1604
分类号:TP18
基金项目:国家重点研发计划项目(2017YFC1502104);“青蓝工程”基金项目(2016);南京大气科学联合研究中心项目(NJCAR2018MS05).
Decomposition-based predictive dynamic multi-objective particle swarm optimization algorithm
GENG Huan-tong,ZHOU Shan-sheng,CHEN Zhe,HAN Wei-min
(College of Computer & Software,Nanjing University of Information Science & Technology,Nanjing210044,China)
Abstract:
In order to solve the dynamic multi-objective problems, a predictive dynamic multi-objective particle swarm optimization algorithm based on decomposition is proposed. Firstly, the target problem is divided into several sub-problems by decomposing. When the problem changes, the optimized individuals corresponding to different sub-problems are chosen to detect the severity of changes to improve the ability of the algorithm to adapt and respond to different dynamic problems. Then, a group prediction strategy, which converts individual positions at different moments in the same convergence direction of the target space into time series, is designed to improve the diversity and validity of the prediction population by introducing the time series prediction method and forecasting the position of the next moment, which can effectively improve the algorithm convergence speed after the problem changes. Finally, in order to avoid the mismatch between individuals and subproblems after the environment changes, a rematching strategy is designed to effectively improve the accuracy of the prediction strategy. The experimental results show that compared with the four dynamic multi-objective optimization algorithms, the proposed algorithm has significant advantages in convergence, distribution and stability over six standard dynamic multi-objective problems.
Key words:  dynamic multi-objective problem  prediction  particle swarm optimization  decomposition

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