自适应分组差分变异狼群优化算法

摘要

Due to the shortcomings that wolf pack algorithm is not high solving precision and easy to fall into the local convergence region,adaptive grouping difference variation wolf pack algorithm is proposed based on the excellent characteristics of cloud model transformation between qualitative and quantitative.Individual wolves are initialized by good-point set.Individual hunting behavior is accomplished through the cloud model theory and the self energy of the wolf is considered in the siege behavior.Finally,the differential evolution algorithm and the chaos theory are used to complete the individual variation to explore the global optimal location.The simulation results show that the proposed algorithm has fine capability of finding global optimum,especially for multi peak function.%针对狼群优化算法寻优精度不高和易陷入局部收敛区域的缺点,结合云模型在知识表达时具有不确定中带有确定性的特性,提出一种自适应分组差分变异狼群优化算法.其思想是采用佳点集理论对狼群进行初始化,通过云模型理论来完成个体游猎行为,在围攻行为中考虑狼个体的自身能量,最后利用差分进化算法和混沌理论完成个体变异,并进行探索全局最优位置.典型复杂函数测试表明,该算法能有效找出全局最优解,特别适宜于多峰值函数寻优.