1) traveling salesman problem
旅行商问题
1.
Improved particle swarm optimization based on k-center and its application in traveling salesman problem;
基于k-中心点法的改进粒子群算法在旅行商问题中的应用
2.
Modified particle swarm optimization algorithm for traveling salesman problem;
改进粒子群优化算法求解旅行商问题
3.
Hybrid approach based on Ant Colony System for solving traveling salesman problem;
基于MATLAB的混合型蚁群算法求解旅行商问题
2) TSP
旅行商问题
1.
Application Research for TSP Based on Neural Network-Ant Colony Algorithm;
蚁群神经网络在旅行商问题中的应用
2.
Improved Genetic Algorithms for TSP;
旅行商问题(TSP)的改进遗传算法
3.
An improved Simulated Annealing Algorithm to TSP;
旅行商问题(TSP)的改进模拟退火算法
3) traveling salesman problem(TSP)
旅行商问题
1.
On this basis,a new network was proposed to solve traveling salesman problem(TSP) by decaying self-feedback.
在此基础上,通过衰减自反馈,提出了求解旅行商问题(Traveling Salesman Problem,TSP)的新网络。
2.
To increase the convergence speed of the genetic algorithm in solving the traveling salesman problem(TSP),combined with adaptive operators and competitive strategy between parents and their children,an adaptive genetic algorithm based on the regional search is proposed.
为了提高用遗传算法求解旅行商问题(TSP)的收敛速度,结合自适应算子和父子竞争策略等优化思想,提出了基于分区搜索的自适应遗传算法。
3.
This paper proposed a full set of evolutionary strategies that include new method of encoding of chromosomes,invert,rotate and swap operations,for solving traveling salesman problem(TSP)by genetic algorithm(GA)The strategies are different from those commonly used in GA except the invert operation.
本文提出用遗传算法（ＧＡ）求解旅行商问题（ＴＳＰ）的一整套进化策略，包括染色体的编码、反向运算、循环运算、交换运算。
4) Travelling salesman problem
旅行商问题
1.
Predatory search algorithm based on tabu list and its experimental research on travelling salesman problems;
基于禁忌表的捕食搜索算法及其在旅行商问题中的实验研究
2.
The problem of path planning for coordinated mine clearance operation of multiple autonomous underwater vehicles was formulated as a kind of multiple travelling salesman problem and two models of multiple travelling salesman problem were developed from the mission background information.
将智能水下机器人集群协同清扫水雷的路径规划问题归结为多人旅行商问题,并根据清扫水雷的任务背景提出两种多人旅行商问题模型。
3.
This paper introduces six different random sampling methods for cycling permutations and carries out a further study of solving the travelling salesman problem by simulated annealing.
提出了循环排序中６种不同的随机抽样方式，对旅行商问题（ＴＳＰ）的模拟退火求解进行了进一步深入研究。
5) travelling salesman problem(TSP)
旅行商问题
1.
Aimed at many fields combinational optimization problems being transformed into travelling salesman problem(TSP),a complex particle swarm optimization(CPSO) algorithm was put forward to solve TSP.
针对众多领域的组合优化问题可转化为旅行商问题(TSP),提出求解TSP的粒子群复形(CPSO)算法。
2.
The operation of the genetic algorithm of Travelling Salesman Problem(TSP) needs lots of time and it is easy to fall into the local optimal solution.
针对旅行商问题(Travelling Salesman Problem,TSP)的遗传算法的大规模操作,需要大量运算时间而且容易造成局部最优解,提出一种并行混合遗传算法。
3.
When the method of antibody distilling and injecting used in Travelling Salesman Problem(TSP) were presented,the convergence of AIGA was proven theoretically.
结合旅行商问题(TSP),给出了示范抗体的提取和注射方法,并给出了算法收敛性的理论证明。
补充资料：旅行推销员问题
旅行推销员问题（又称为旅行商问题、tsp问题）是一个多局部最优的最优化问题：有n个城市，一个推销员要从其中某一个城市出发，唯一走遍所有的城市，再回到他出发的城市，求最短的路线。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。