基于自适应进化策略的人工蜂群优化算法

提出一种自适应进化策略的人工蜂群优化算法来提高基本人工蜂群优化算法的性能。算法中每个引领蜂拥有4种进化策略，在迭代过程中通过计算每种进化策略的立即价值、未来价值和综合奖励来决定引领蜂个体的进化行为，并通过多策略进化概率变异方式来提升个体寻优速度或避免陷入局部最优解。典型高维复杂函数测试表明，该算法具有很好的收敛精度和计算速度。

Artificial Bee Colony Optimization Algorithm Based on Adaptive Evolution Strategy

An artificial bee colony optimization algorithm based on adaptive evolution strategy is proposed to improve the performance of the artificial bee colony algorithm. Each leader individual has four evolutionary strategies in the algorithm. In the iteration process, the evolutionary behavior of the leader individual is determined by calculating the immediate value, the future value and the comprehensive reward of each evolutionary strategy. And then a multi-strategy evolutionary probability mutation method is proposed to improve the individual search speed or to avoid falling into the local optimal solution. Typical high-dimensional complex function tests show that the algorithm has good convergence accuracy and computational speed.