基于精英种群策略的协同差分进化算法

针对差分进化算法在处理函数优化时存在的过早收敛和易陷入局部最优的问题,提出了一种基于精英种群策略的协同差分进化算法。在优化过程中,首先对种群进行适应度值评估和排序,提取前N个优秀个体组成精英种群,其余个体随机分为3个等大的子种群,每个子种群采取不同的进化策略,以此来保证种群的多样性;然后每隔一定代数,根据新的适应度值更新精英种群和其余3个子种群,这样可以有效地避免算法陷入局部最优;最后,将所提出的算法与4个先进的差分进化算法在CEC2014的30个标准测试函数上进行对比实验。实验结果表明,所提出的算法能够有效提高收敛速度,具有较高的收敛精度和较好的优化性能。

A cooperative differential evolution algorithm with elitist-population strategy

To solve the problems of premature convergence and easy falling into local optimum in the differential evolution algorithm when dealing with function optimization problems of population diversity, we propose a cooperative differential evolution algorithm with elitist population strategy (ESCDE). In the population optimization process, we firstly evaluate population fitness, extract the first several excellent individuals as the elite population and divide the others randomly into three sub-populations of the same size. Each sub population adopts a different evolution strategy to guarantee the diversity of the population and improve the performance of the algorithm. And then, the elite population and the remaining three sub populations are updated according to the new fitness value to avoid falling into local optimum. Finally, comparison are made between the proposed algorithm and four other advanced differential evolution algorithms on 30 standard test functions in CEC2014. Experimental results indicate that the proposed algorithm can improve convergence speed effectively with higher convergence precision and better optimization performance.