多种群协方差学习差分进化算法  

Multi-populations Covariance Learning Differential Evolution Algorithm

作　　者：杜永兆 范宇凌 柳培忠 唐加能 骆炎民[3] DU Yongzhao;FAN Yuling;LIU Peizhong;TANG Jianeng;LUO Yanmin(College of Engineering, Huaqiao University, Quanzhou 362021, China;College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China;College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China)

机构地区：[1]华侨大学工学院,泉州362021 [2]华侨大学机电及自动化学院,厦门361021 [3]华侨大学计算机科学与技术学院,厦门361021

出　　处：《电子与信息学报》2019年第6期1488-1495,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61605048,61231002,51075068);福建省教育厅项目(JA15035);泉州市科技局项目(2014Z103,2015Z114);华侨大学研究生科研创新能力培养计划(1611422002)~~

摘　　要：种群多样性与交叉算子在差分进化(DE)算法求解全局优化问题中具有重要作用,该文提出一种多种群协方差学习差分进化(MCDE)算法。首先,采用多种群机制的种群结构,利用每一子种群结合相应的变异策略保证进化过程个体多样性。然后,通过种群间的协方差学习,为交叉操作建立一个适当旋转的坐标系统;同时,使用自适应控制参数来平衡种群的勘测与收敛能力。最后,在单峰函数、多峰函数、偏移函数和高维函数的25个基准测试函数上进行测试,并同其他先进的进化算法对比,实验结果表明该文算法相较于其他算法在求解全局优化问题上达到最优效果。The diversity of the population and the crossover operator algorithm play an important role in solving global optimization problems in Differential Evolution (DE). The Multi-poplutions Covariance learning Differential Evolution (MCDE) algorithm is proposed. Firstly, the population structure is a multi-poplutions mechanism, and each subpopulation combines the corresponding mutation strategy to ensure the individual diversity in the evolutionary process. Then, the covariance learning establishes a proper rotation coordinate system for the crossover operation in the population. At the same time, the adaptive control parameters are used to balance the ability of population survey and convergence. Finally, the proposed algorithm is conducted on 25 benchmark functions including unimodal, multimodal, shifted and high-dimensional test functions and compared with the state-of-the-art evolutionary algorithms. The experimental results show that the proposed algorithm compared with other algorithms has the best effect on solving the global optimization problem.

关 键 词：差分进化 多种群 协方差学习 自适应参数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]