多种群协方差学习差分进化算法

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。

Multi-populations Covariance Learning Differential Evolution Algorithm

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.