序列规划问题一般转化为旅行商问题来求解。
The problem of sequence planning can be translated into Traveling Salesman Problem (TSP).
本文提出了一些对遗传算法应用于旅行商问题的方法。
This paper presents some approaches to the application of Genetic Algorithms to the Traveling Salesman Problem.
迄今为止，中国旅行商问题的最优解是15904公里。
Till now, the best published result of Chinese-Travelling Salesman Problem is 15904km.
本文提出用神经网络解旅行商问题(tsp)的改进算法。
This article proposes a modified algorithm for solving the travelling salesman problem (TSP) by neural network.
标准遗传算法在解决旅行商问题时效率不高，容易陷于局部最优解。
Traditional genetic algorithms have low efficiency and tend to be trapped by local optimizations.
蚂蚁用一种叫做信息素的化学物质来解决它们自己的“旅行商问题”。
Ants solve their own version using chemical signals called pheromones.
实验结果表明，GPSO算法在求解旅行商问题上，优化效果明显。
The experimental results demonstrate that GPSO algorithm has obvious optimization effect in solving TSP.
基于旅行商问题的实验证明，算法具有较好的全局搜索能力和收敛速度。
Experimental results on traveling salesman problem show that proposed algorithm has a good global searching ability and high convergence speed.
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.
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。
Traveling salesman problem(TSP) and nonlinear equations are two kinds of important problems with widely applications.
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。
Experimental results on Traveling Salesman Problem (TSP) demonstrate that the proposed algorithm is viable and efficient.
利用函数极值和旅行商问题分别对方案的资源耗费、运行速度的有效性进行了验证。
The optimal solution of function and classical travel sales problem are used to verify the effect of the schemes on resource consumption and running speed.
影片递送问题是旅行商问题和多路旅行商问题的扩展，具有重要的理论和实际意义。
The Film Copy Deliverer Problem is the extension of the Traveling Salesman Problem and the Multiple Traveling Salesman Problem, and it is significance in theory and practice.
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)。
By choosing appropriate operators and parameters, genetic algorithms (GA) can solve the traveling salesman problem (TSP) effectively.
最后还将改进的算法进行适当推广，给出了求解多旅行商问题(mtsp)的具体步骤。
Finally, we develop our algorithm to solve MTSP and give the concrete method.
根据旅行商问题（TSP）的邻域搜索算法的思想，提出了型材下料问题的一种优化算法。
The paper based on the idea of K-OPT Algorithm for TSP, present a swap algorithm for the one-dimensional cutting-stock problem.
提出了一种基于多人博弈的演化优化方法（EAMG），用于解决旅行商问题（TSP）。
An evolutionary algorithm based on multi-player game theory(EAMG) for the TSP was proposed.
用列队竞争算法解旅行商问题获得了满意的结果，显示出列队竞争算法良好的全局搜索性能。
By using LCA to solve traveling salesman problem, satisfactory results are obtained. Examples show that LCA has good global search characteristic.
旅行商问题作为经典的组合优化问题，实际中的应用非常广泛，但它却是一个NP完全问题。
The TSP problem is considered as classical optimization grouping problem, which is widely used in practice, but it is real a difficult NP problem.
本文对经典的旅行商问题给出一种精确式算法，计算结果表明，它具有一定的优越性和实用性。
This paper offers an exact algorithm for the classic travelling salesman problem, the computational results show that it has some obvious advantages and practical applications.
受自然界物种群体间相互联系的启发，提出了群体间竞争与协作的遗传算法来解决旅行商问题。
Enlighted by the mutual contacts between the nature species groups, the paper proposes the inter-group competition and collaboration of genetic algorithms to solve the traveling salesman problem.
问题描述：旅行商问题 某售货员要到若干城市去推销商品，已知各城市之间的路程（或旅费）。
Problem Description: A salesman traveling salesman problem to a number of cities to sell commodities, known distance between cities (or travel).
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 Film Deliverer Problem(FDP), a new problem in the combination optimization is much more complicated than the Traveling Salesman Problem(TSP).
本题中周先生想到各地旅游，每个城市都被访问一次且仅一次，我们考虑到了旅行商问题的解决方法。
The problem in Chow expect travel around, every city is accessed once and only once, we take into account the solution to the traveling salesman problem.
本题中周先生想到各地旅游，每个城市都被访问一次且仅一次，我们考虑到了旅行商问题的解决方法。
The problem in Chow expect travel around, every city is accessed once and only once, we take into account the solution to the traveling salesman problem.
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