一种求旅行商问题的离散粒子群算法

针对旅行商问题提出一种离散粒子群算法。算法重新定义了速度及其与粒子位置的相关算子,设计了"距离排序矩阵"(保存距离城市由近到远的其他城市的矩阵),并根据它生成可动态变化的优秀基因库来指导粒子高效地进行全局搜索。本文用TSPLIB中的部分案例进行实验,实验结果表明,该算法在求解旅行商问题上有很好的性能,并且具有很好的鲁棒性。     

粒子群算法求解旅行商问题程序设计

粒子群优化算法是一种具备全局搜索能力的群集智能优化算法,针对一类离散的、NP完全的组合优化问题——旅行商问题,该文介绍了用粒子群算法求解旅行商问题的改进策略和主要模块的程序设计思想。将算法应用到20个城市的解旅行商问题所得到的结果与遗传算法进行比较,数字仿真与结果比较表明了改进粒子群算法求解该问题的有效性。     

粒子群算法求解旅行商问题程序设计

粒子群优化算法是一种具备全局搜索能力的群集智能优化算法,针对一类离散的、NP完全的组合优化问题——旅行商问题．该文介绍了用粒子群算法求解旅行商问题的改进策略和主要模块的程序设计思想。将算法应用到20个城市的解旅行商问题所得到的结果与遗传算法进行比较,数字仿真与结果比较表明了改进粒子群算法求解该问题的有效性。     

基于遗传算法的一类多旅行商问题研究

旅行商问题是一个经典的NP完全问题,对多人旅行商问题的求解则更具有意义。以往对求解多人旅行商问题的研究局限于以所有旅行商路径总和最小为优化标准,而对所有旅行商路径最大值最小的多旅行商一类问题研究的相对较少。针对所有旅行商路径最大值最小的多旅行商一类问题,用遗传算法优化,并且提出了矩阵解码方法。该方法适于距离对称和非对称的多旅行商问题求解。以距离非对称的多旅行商问题的实例进行了仿真,并对不同交叉算子性能进行了比较。     

基于遗传算法的多旅行商问题研究

针对所有旅行商路径总和最小为优化标准的多旅行商一类问题,用遗传算法优化,并提出了矩阵解码方法。对距离非对称的多旅行商问题的实例进行了仿真,并对不同交叉算子性能进行了比较。结果表明,该算法是有效的,适用于距离对称和非对称的多旅行商问题求解。     

基于递阶遗传算法的多旅行商问题优化*

旅行商问题是一个经典的NP问题,对多人旅行商问题的求解则更具有意义。为了解决所有旅行商路径总和最小为优化标准的多旅行商一类问题,提出了一种递阶遗传算法和矩阵解码方法。该算法根据问题的特点,采用一种递阶编码方案,此编码与多旅行商问题一一对应。用递阶遗传算法优化多旅行商问题无须设计专门的遗传算子,操作简单,并且解码方法适于求解距离对称和距离非对称的多旅行商问题。计算结果表明,递阶遗传算法是有效的,能适用于优化多旅行商问题。     

差分演化算法求解旅行商问题

设计了基于差分演化算法的新算法来求解旅行商问题.在新算法中,旅行商问题的城市的个数作为向量的维数,每个向量的元素的大小顺序作为旅行商问题的一个可行解.实验表明,该算法能够成功求解小规模的旅行商问题,而且算法稳健性好;再与同类算法的优化结果相比较,表明了该算法计算量小、收敛速度快的优点.     

一种求解旅行商问题的高效混合遗传算法

旅行商问题(TravellingSalesmanProblemTSP)是一个典型的组合优化难题,论文提出一种求解旅行商问题的高效混合遗传算法。该算法结合遗传算法和2-opt邻域搜索优化技术,并针对旅行商问题的特点,提出K近邻点集以缩减搜索空间从而加快求解速度。基于典型实例的仿真结果表明,此算法的求解效率比较高。     

一种新的基于并行蚁群算法的旅行商问题求解方法的研究

分析了旅行商问题,结合蚁群算法的群体的协作与学习能力,提出了一种基于蚁群算法的的旅行商并行计算模式．该算法根据蚁群的本质特征,并结合计算中通讯的开销,采用了粗粒度模型,并引入变异思想,能够提高算法的收敛速度,以获得更好的优化解．通过旅行商问题的仿真实验获得的结果表明,该算法对于蚁群算法具有较好的改进效果,能够很好地解决旅行商一类的问题问题求解。     

基于猴群算法求解旅行商问题

鉴于旅行商问题是一个NP难问题,而猴群算法是一种新的群体智能优化算法,因此,利用猴群算法给出旅行商问题的求解。在分析了旅行商问题的特点后,采用整数编码的方式来表示猴群的位置,这样就解决了猴群算法在求解含有离散变量的组合优化问题时,算法中的爬过程失效的问题,有效地利用猴群算法求解旅行商问题。为了提高猴群算法的性能,在猴群算法的爬过程中,引入好动策略,给出改进算法,并将其应用到求解旅行商问题。在仿真实验中,与其他算法进行比较,结果表明利用改进猴群算法能够有效地求解旅行商问题。     

热轧计划中的多旅行商问题及其计算方法*

针对热轧批计划问题进行了MTSP(多旅行商问题)建模,并对该问题设计了混合遗传算法,经某大型钢厂实例数据进行了仿真测试.计算结果表明,该算法给出了较优的轧制批计划方案,解决了热轧轧制批计划的编制问题.     

A novel state transition simulated annealing algorithm for the multiple traveling salesmen problem

The Journal of Supercomputing - In this study, we consider the multiple traveling salesman problem (MTSP) with multiple depots, closed paths, and a constraint on the number of cities visited by...     

A self-organised travelling salesman

Using the principles of self-organisation and Darwin's theory of evolution, an algorithm has been developed to solve the geometric travelling salesman problem (TSP). In this approach, we have virtual and real nodes (cities) which can have equal or different masses (weights). The virtual nodes and their neighours are attracted toward the fixed cities by a Newtonian force. The birth and death of the virtual nodes creates a world in which only the fittest survive. This approach has been successfully tested on many problems of different sizes, with a constant error of about 4.6% across the whole range. The computing time follows a power series (square law) versus the number of cities. Comparison of our results with those obtained by a simulated annealing method showed the solutions that obtained by this self-organisation method are of a better quality, especially for large size problems.     

A new grouping genetic algorithm approach to the multiple traveling salesperson problem

The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.     

A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES/spl I.bar/DECOMPOSE

In addition to the classical heuristic algorithms of operations research, there have also been several approaches based on artificial neural networks for solving the traveling salesman problem. Their efficiency, however, decreases as the problem size (number of cities) increases. A technique to reduce the complexity of a large-scale traveling salesman problem (TSP) instance is to decompose or partition it into smaller subproblems. We introduce an all-neural decomposition heuristic that is based on a recent self-organizing map called KNIES, which has been successfully implemented for solving both the Euclidean traveling salesman problem and the Euclidean Hamiltonian path problem. Our solution for the Euclidean TSP proceeds by solving the Euclidean HPP for the subproblems, and then patching these solutions together. No such all-neural solution has ever been reported.     

基于HGA的最小旅行时间多旅行商问题研究

为了解决最小化旅行时间的多旅行商一类问题,提出了一种递阶遗传算法和矩阵解码方法。该算法根据问题的特点,采用一种递阶编码方案,此编码与多旅行商问题一一对应。用递阶遗传算法优化多旅行商问题不需设计专门的遗传算子,操作简单,并且解码方法适于求解距离矩阵对称和距离矩阵非对称的多旅行商问题。计算结果表明,递阶遗传算法是有效的,能适用于优化最小化完成时间的多旅行商问题。     

A Quantum-inspired Ant Colony Optimization for solving a sustainable four-dimensional traveling salesman problem under type-2 fuzzy variable

In this paper, a Quantum-inspired Ant Colony Optimization (Qi-ACO) is proposed to solve a sustainable four-dimensional traveling salesman problem (4DTSP). In 4DTSP, various paths with a different number of conveyances are available to travel between any two cities. In this model, we have considered a sustainable 4DTSP in terms of emission as a constraint. Since travel costs and emissions are uncertain/imprecise in nature, so here we consider type-2 variables. Sustainable development in the traveling salesman problem (TSP) sector can be divided into two major sections: economy and environmental. Sustainable TSP development requires balancing to achieve the maximum benefits for these two sectors. For increasing development in sustainable transportation, we need to use some strategies for increasing sustainability. These strategies include improving route and vehicle selection, routing plan, vehicle speed, etc. The novelties of the proposed Qi-ACO algorithm are (i) Qubit generated based on the amount of emission of the vehicle as well as travel cost between two cities, (ii) pheromone initialized and updated depends on the qubit, (iii) quantum-inspired technique makes fast computation. The proposed sustainable 4DTSP is illustrated with some numerical data. The defuzzification of type-2 fuzzy variable based on the Critical value (CV) method is used in this model. The supremacy of the proposed method is established through some statistical tests. The proposed algorithm and its modified form can be easily adapted in ship routing, supply chain problems, and other fields.