As an essential question of the intelligent distribution system, route optimization has many problem-solving models, the most typical one is Traveling Salesman problem (short for TSP).
路径优化是物流配送中智能调度系统的核心问题，其中最典型的问题模型就是旅行商问题即TSP问题。
By choosing appropriate operators and parameters, genetic algorithms (GA) can solve the traveling salesman problem (TSP) effectively.
通过选择合适的算子和参数，遗传算法(GA)可以有效求解旅行商问题(tsp)。
Then Traveling Salesman Problem is described and its mathematics model is provided. Some correlative algorithms are introduced and their capability of solving TSP is compared.
随后叙述了TSP的一般提法，描述了其数学模型，综合介绍了关于解决TSP的相关算法，并做了性能比较。
Solving Traveling Salesman problem (TSP) is an important problem in Genetic Algorithm's Application, it is an optimization problem of the TSP path encoding in essence.
求解tsp问题是遗传算法应用的一个重要领域，其本质是TSP问题中巡回路径编码串的组合最优化问题。
The Film Deliverer Problem(FDP), a new problem in the combination optimization is much more complicated than the Traveling Salesman Problem(TSP).
影片递送问题（简称FDP）是组合优化的一个新问题，它比旅行商问题（简称TSP）复杂得多。
The problem of sequence planning can be translated into Traveling Salesman Problem (TSP).
序列规划问题一般转化为旅行商问题来求解。
Traveling salesman problem(TSP) and nonlinear equations are two kinds of important problems with widely applications.
旅行商问题（TSP）和非线性方程组都是具有广泛的应用背景的重要问题。
The contrasting experiments on the typical traveling salesman problem (TSP) show that the proposed algorithm is better than standard ant colony system in speed and accuracy.
针对典型的旅行商问题(TSP)进行对比实验，验证了所提出的算法在速度和精度方面优于传统的蚁群系统。
Traveling salesman problem(TSP) is a NP complete combinatorial optimum problem.
旅行商问题是NP完全的组合优化问题。
Traveling Salesman Problem(TSP) is a classic combined optimization problem and it is proved that TSP is NP hard.
TSP（旅行商）问题作为经典的组合优化问题，已经被证明是一个NP难题。
This chaotic neural network is used to the 10-city traveling salesman problem (TSP), and the influence of trigonometric function self-feedback on TSP is analyzed.
混沌神经网络的10个城市的旅行商问题（TSP），和三角函数自反馈对TSP的影响进行了分析。
Traveling Salesman problem (TSP) is a classic combinatorial optimization problem and NP-hard.
旅行商问题是一个经典的组合优化问题，也是一个NP难问题。
In addition to the software for teaching purposes, can also be used to solve real life with TSP (ie, the traveling salesman problem) issues related issues.
本软件除了用于教学目的外，还可用于解决实际生活中的与TSP(即，旅行商问题)问题相关的问题。
The evolutionary algorithm using inver-over operator for the traveling salesman problem(TSP) has great ascendancy, because its ability in global searching for optimal individual is powerful.
使用逆转算子求解TSP的演化算法具有很强全局搜索能力，在求解TSP问题中显示了巨大的优势。
The Film Deliverer problem (FDP), a new problem in the combination optimization is much more complicated than the Traveling Salesman problem (TSP).
影片递送问题(简称FDP)是组合优化的一个新问题，它比旅行商问题(简称TSP)复杂得多。
RLTSP is quite close to the traveling-salesman problem in real life, and it is between the traditional traveling-salesman problem (TSP) and the graphical traveling-salesman problem (GTSP).
它更接近于现实生活中的旅行商问题，并且介于传统的旅行商问题（TSP）与图形旅行商问题（GTSP）之间。
This article discusses the computational complexity of Traveling Salesman Problem, and points out the difference of computational complexity between Decisive TSP and Optimal TSP.
讨论了货郎问题的计算复杂性，指出了货郎优化问题与货郎判定问题计算复杂性的差异。
Neural Network with Transient Chaos (TCNN) can be used to solve the Traveling Salesman Problem (TSP).
具有瞬态混沌特性的神经网络(TCNN)可以解tsp。
The polling robot has to go past all the checking points and return to initial location along the shortest route. As you know, this is Traveling Salesman Problem (TSP).
让巡检机器人走最短路径巡检所有检测点并回到初始位置，这应该是一个典型的货郎担问题。
Experimental results on Traveling Salesman Problem (TSP) demonstrate that the proposed algorithm is viable and efficient.
以旅行商问题(TSP)作为算例，实验结果验证了新算法的有效性和高效性。
In this paper, a personification algorithm for solving the Traveling Salesman Problem (TSP) is proposed, which is based on original greedy algorithm.
基于贪心算法提出了一种改进的求解旅行商问题(tsp)的拟人算法。
In this paper, a personification algorithm for solving the Traveling Salesman Problem (TSP) is proposed, which is based on original greedy algorithm.
基于贪心算法提出了一种改进的求解旅行商问题(tsp)的拟人算法。
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