混合模式搜索的分布式memetic差分进化算法

针对差分进化（DE）算法存在的早熟收敛与搜索停滞的问题，提出memetic分布式差分进化（DDE）算法。将memetic算法的思想融入到差分进化算法中，采用分布式的种群结构以及memetic算法中的混合策略，前者将初始种群分为多个子种群，子种群间根据冯·诺依曼拓扑结构周期性地实现信息交流，后者将差分进化算法作为进化的主要框架，模式搜索作为辅助手段，从而平衡算法的探索与开发能力。所提算法充分利用了模式搜索和差分进化算法的优势，建立了有效的搜索机制，增强了算法摆脱局部最优的能力，能够满足搜索过程对种群多样性及收敛速度的需求。将所提算法与几种先进的差分进化算法相比较，对标准测试函数进行优化的实验结果显示：所提算法在解的质量和收敛性能方面，均优于其他几种相比较的先进的差分进化算法。

Distributed memetic differential evolution algorithm combined with pattern search

In view of the problem of premature convergence and stagnation in the Differential Evolution (DE), the distributed memetic differential evolution was put forward. The idea of memetic algorithm was introduced into the DE algorithm. The distributed population structure and the combination strategy in memetic algorithm were applied. In the former strategy, the initial population was divided into multiple subpopulations according to the von Neumann topology and the periodical information exchange was realized among the subpopulations. And in the latter idea, the differential evolution was taken as an evolutionary frame that was assisted by pattern search to balance the exploration and exploitation abilities. The proposed algorithm made full use of advantages of the pattern search and differential evolution, set up an effective search mechanism and enhanced the algorithm to break away from local optima so as to satisfy the demand on population diversity and convergence speed of the search process. The proposed algorithm was run on a set of classic benchmark functions and compared with several state-of-the-art DE algorithms. Numerical results show that the proposed algorithm has excellent performance in terms of solution quality and convergence speed for all test problems given in this study.