基于改进混合蛙跳算法的CVRP求解

骆剑平; 李霞; 陈泯融

doi:10.3724/SP.J.1146.2010.00328

基于改进混合蛙跳算法的CVRP求解

doi: 10.3724/SP.J.1146.2010.00328

基金项目:

国家自然科学基金(60772148)和高等学校博士点基金(200805900 001)资助课题

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- 被引次数: 0
出版历程
- 收稿日期: 2010-04-01
- 修回日期: 2010-08-23
- 刊出日期: 2011-02-19

Improved Shuffled Frog Leaping Algorithm for Solving CVRP

摘要

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(Power Law Extremal Optimization，-EO)融合于SFLA，针对CVRP对-EO过程进行设计和改进。改进的-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。
- 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
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 (-EO) to further improve the local search ability. The fitness for the components of an individual is carefully designed and the neighborhood for-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.
- Intelligence optimization /
- Evolutionary algorithm /
- Shuffled Frog Leaping Algorithm (SFLA) /
- Extremal Optimization (EO) /
- Vehicle Routing Problem (VRP) /
- Convergence

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