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针对微分进化算法(DE)易陷入局部最优解、进化后期收敛速度慢、求解精度低等缺点,结合DE/rand/1和DE/best/1两种变异模式分别具有全局探索能力和局部开发能力的优点,引入精英存档策略和控制参数自适应策略,提出一种双变异模式协同自适应微分进化(DMCSa DE)算法.15个典型benchmark测试函数的实验结果表明,DMCSa DE能够有效提高算法的全局探索能力和局部开发能力,避免早熟收敛,大大提高算法的收敛性能和鲁棒性,同时,精英种群的大小对DMCSa DE的优化性能具有明显的影响.
Aiming at the disadvantages of the differential evolution algorithm (DE), such as easy to fall into the local optimal solution, the slow convergence rate in the late evolutionary stage and the low solution precision, the two modes of mutation DE / rand / 1 and DE / best / 1 have global exploration capability and local development (DMCSa DE) algorithm is proposed.Experimental results of 15 typical benchmark test functions show that DMCSa DE can effectively improve the performance of the algorithm Global exploration ability and local development ability to avoid premature convergence, and greatly improve the convergence performance and robustness of the algorithm. At the same time, the size of elite population has a significant impact on the optimization performance of DMCSa DE.