| 基于改进混合蛙跳算法的CVRP求解 |
| |
| 引用本文： | 骆剑平,李霞,陈泯融.基于改进混合蛙跳算法的CVRP求解[J].电子与信息学报,2011,33(2):429-434. |
| |
| 作者姓名： | 骆剑平 李霞 陈泯融 |
| |
| 作者单位： | 深圳大学信息工程学院,深圳,518060 |
| |
| 基金项目： | 国家自然科学基金(60772148); 高等学校博士点基金(200805900001)资助课题 |
| |
| 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。
|
| 关 键 词： | 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 |
| 收稿时间： | 2010-04-01 |
Improved Shuffled Frog Leaping Algorithm for Solving CVRP |
| |
| Luo Jian-ping,Li Xia,Chen Min-rong.Improved Shuffled Frog Leaping Algorithm for Solving CVRP[J].Journal of Electronics & Information Technology,2011,33(2):429-434. |
| |
| Authors: | Luo Jian-ping Li Xia Chen Min-rong |
| |
| Affiliation: | College of Information Engineering, Shenzhen University, Shenzhen 518060, China |
| |
| 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. |
| |
| Keywords: | Intelligence optimization Evolutionary algorithm Shuffled Frog Leaping Algorithm (SFLA) Extremal Optimization (EO) Vehicle Routing Problem (VRP) Convergence |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《电子与信息学报》浏览原始摘要信息 |
|
点击此处可从《电子与信息学报》下载全文 |
