基于差分进化算子变异的中心引力优化算法

针对中心引力优化算法易陷入局部最优这一不足,加强算法的全局寻优能力,提出一种改进的中心引力优化算法,根据差分算法本身的固有特性,通过引入差分进化算子对当前粒子位置的分量进行变异,促使算法摆脱局部最优,增强算法的全局收敛性.最后选取5个经典函数对算法进行测试,并与其他算法进行比较分析,结果证明算法的精度得到了明显提高,从而验证了该算法的有效性和可行性.

Central force optimization algorithm based on differential evolution operator mutation

In order to avoid obtaining local optimal solution of central force optimization algorithm,strengthen the ability of searching,a new algorithm is proposed based on differential evolution algorithm.According to the characteristics of differential evolution algorithm,differential evolution operator mutation is introduced to mutate the component of particle and reduce the possibility of trapping in the local optimum and to improve the convergence speed of global searching.Using 5 classic benchmark functions to test,simulation results show that,compared with several other algorithms,the precision of the new algorithm is remarkably improved,therefore the effectiveness and feasibility of the algorithm is proved correct.