中文摘要：

该文提出基于实数编码模式的混合蛙跳算法（Shuffled Frog Leaping Algorithm,SFLA）求解容量约束车辆路径问题（Capacitated Vehicle Routing Problem,CVRP）;把具有极强局部搜索能力的幂律极值动力学优化（PowerLaw Extremal Optimization,τ-EO）融合于SFLA,针对CVRP对τ-EO过程进行设计和改进。改进的τ-EO采用新颖的组元适应度计算方法;采用幂律概率分布来挑选需要变异的组元;根据最邻近城市表,采用幂律概率分布挑选变异组元的最佳邻近城市,执行线路间或线路内的变异。求解测试库中的实例,证明该改进算法有效。

英文摘要：

An improved Shuffled Frog Leaping Algorithm（SFLA） is proposed to solve the Capacitated Vehicle Routing Problem（CVRP）based on real-coded patterns.It is then combined with the power-law Extremal Optimization（） to further improve the local search ability.The fitness for the components of an individual is carefully designed and the neighborhood for mutation is established according to power-law probability distribution.Experimental results show that the proposed algorithm outperforms other heuristic algorithms base on PSO and GA.