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Improved Shuffled Frog Leaping Algorithm for Solving CVRP
基于改进混合蛙跳算法的CVRP求解

Keywords: Intelligence optimization,Evolutionary algorithm,Shuffled Frog Leaping Algorithm (SFLA),Extremal Optimization (EO),Vehicle Routing Problem (VRP),Convergence
智能优化
,进化算法,混合蛙跳算法,极值动力学优化,车辆路径问题,收敛性

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Abstract:

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 (τ-EO) to further improve the local search ability. The fitness for the components of an individual is carefully designed and the neighborhood for τ-EO 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.

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