基于SFLA-GA混合算法求解时间最优的旅行商问题

张勇; 高鑫鑫; 王昱洁

doi:10.11999/JEIT170484

基于SFLA-GA混合算法求解时间最优的旅行商问题

doi: 10.11999/JEIT170484

基金项目:

国家科技支撑计划项目(2013BAH52F01)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-05-18
- 修回日期: 2017-11-08
- 刊出日期: 2018-02-19

Solving the Time Optimal Traveling Salesman Problem Based on Hybrid Shuffled Frog Leaping Algorithm-Genetic Algorithm

Funds:

The National Science and Technology Support Program of China (2013BAH52F01)

摘要

摘要: 该文以经典的对称旅行商问题(Symmetric Traveling Salesman Problem, STSP)为基础，求解时间最优的旅行商问题(Time Optimal TSP, TOTSP)，将拟合函数引入到混合蛙跳遗传算法(SFLA-GA)的适应度函数来反映景点客流量随时间的变化，旨在旅游旺季为游客提供一条游览时间最短的路径推送服务。实验结果表明：相对于随机游览路径，SFLA-GA混合算法得到的游览路径明显节省了游览时间；与SFLA和混合粒子群遗传算法(PSO-GA)相比较，SFLA-GA混合算法具有计算量少、收敛速度快、对初始种群依赖性低以及全局性更好等优点，在求解TOTSP上搜索性能更强、时间更优。
- 时间最优的旅行商问题 /
- 混合蛙跳遗传算法 /
- 适应度函数 /
- 拟合函数 /
- 游览时间
Abstract: In order to provide a recommended-path service for tourists with the shortest traveling time in the peak-season, the Time Optimal Traveling Salesman Problem (TOTSP) is further studied and the fit function is introduced into the fitness function of the hybrid Shuffled Frog Leaping Algorithm-Genetic Algorithm (SFLA-GA) to reflect the change of traffic over time, which is based on the classic and Symmetrical Traveling Salesman Problem (STSP). The experimental results show that compared with the random tour path, the tour path significantly saves the tour time which is obtained by the hybrid SFLA-GA. Compared with SFLA and hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA), the hybrid SFLA-GA has some advantages, such as less amount of calculation, fast speed of convergence, low dependency on initial population, good global superiority and so on. The hybrid SFLA-GA has stronger search capability and less search time in solving the TOTSP.
- Time optimal traveling salesman problem /
- Hybrid Shuffled Frog Leaping Algorithm-Genetic Algorithm (SFLA-GA) /
- Fitness function /
- Fit function /
- Tour time

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参考文献(25)

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