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31 
Apply algorithm of changes to solve traveling salesman problemChio, Chou Hei January 2011 (has links)
University of Macau / Faculty of Science and Technology / Department of Mathematics

32 
Solving Traveling Salesman Problem With a noncomplete GraphEmami Taba, Mahsa Sadat January 2009 (has links)
One of the simplest, but still NPhard, routing problems is the Traveling Salesman Problem (TSP). In the TSP, one is given a set of cities and a way of measuring the distance between cities. One has to find the shortest tour that visits all cities exactly once and returns back to the starting city. In stateoftheart algorithms, they all assume that a complete graph is given as an input. However, for very large graphs, generating all edges in a complete graph, which corresponds to finding shortest paths for all city pairs, could be timeconsuming. This is definitely a major obstacle for some reallife applications, especially when the tour needs to be generated in realtime. The objective, in this thesis, is to find a nearoptimal TSP tour with a reduced set of edges in the complete graph. In particular, the following problems are investigated: which subset of edges can be produced in a shorter time comparing to the time for generating the complete graph? Is there a subset of edges in the complete graph that results in a better nearoptimal tour than other sets? With a noncomplete graph, which improvement algorithms work better? In this thesis, we study six algorithms to generate subsets of edges in a complete graph. To evaluate the proposed algorithms, extensive experiments are conducted with the wellknown TSP data in a TSP library. In these experiments, we evaluate these algorithms in terms of tour quality, time and scalability.

33 
The Asymmetric Traveling Salesman ProblemMattsson, Per January 2010 (has links)
This thesis is a survey on the approximability of the asymmetric traveling salesmanproblem with triangle inequality (ATSP).In the ATSP we are given a set of cities and a function that gives the cost of travelingbetween any pair of cities. The cost function must satisfy the triangle inequality, i.e.the cost of traveling from city A to city B cannot be larger than the cost of travelingfrom A to some other city C and then to B. However, we allow the cost function tobe asymmetric, i.e. the cost of traveling from city A to city B may not equal the costof traveling from B to A. The problem is then to find the cheapest tour that visit eachcity exactly once. This problem is NPhard, and thus we are mainly interested in approximationalgorithms. We study the repeated cycle cover heuristic by Frieze et al. We alsostudy the HeldKarp heuristic, including the recent result by Asadpour et al. that givesa new upper bound on the integrality gap. Finally we present the result ofPapadimitriou and Vempala which shows that it is NPhard to approximate the ATSP with a ratio better than 117/116.

34 
Solving Traveling Salesman Problem With a noncomplete GraphEmami Taba, Mahsa Sadat January 2009 (has links)
One of the simplest, but still NPhard, routing problems is the Traveling Salesman Problem (TSP). In the TSP, one is given a set of cities and a way of measuring the distance between cities. One has to find the shortest tour that visits all cities exactly once and returns back to the starting city. In stateoftheart algorithms, they all assume that a complete graph is given as an input. However, for very large graphs, generating all edges in a complete graph, which corresponds to finding shortest paths for all city pairs, could be timeconsuming. This is definitely a major obstacle for some reallife applications, especially when the tour needs to be generated in realtime. The objective, in this thesis, is to find a nearoptimal TSP tour with a reduced set of edges in the complete graph. In particular, the following problems are investigated: which subset of edges can be produced in a shorter time comparing to the time for generating the complete graph? Is there a subset of edges in the complete graph that results in a better nearoptimal tour than other sets? With a noncomplete graph, which improvement algorithms work better? In this thesis, we study six algorithms to generate subsets of edges in a complete graph. To evaluate the proposed algorithms, extensive experiments are conducted with the wellknown TSP data in a TSP library. In these experiments, we evaluate these algorithms in terms of tour quality, time and scalability.

35 
Solving the Traveling Salesman Problem by Ant Colony Optimization Algorithms with DNA ComputingHuang, HungWei 29 July 2004 (has links)
Previous research on DNA computing has shown that DNA algorithms are useful to solve some combinatorial problems, such as the Hamiltonian path problem and the traveling salesman problem. The basic concept implicit in previous DNA algorithms is the brute force method. That is, all possible solutions are created initially, then inappropriate solutions are eliminated, and finally the remaining solutions are correct or the best ones.
However, correct solutions may be destroyed while the procedure is executed. In order to avoid such an error, we recommend combining the conventional concepts of DNA computing with a heuristic optimization method and apply the new approach to design strategies. In this thesis, we present a DNA algorithm based on ant colony optimization (ACO) for solving the traveling salesman problem (TSP). Our method manipulates DNA strands of candidate solutions initially. Even if the correct solutions are destroyed during the process of filtering out, the remaining solutions can be reconstructed and correct solutions can be reformed. After filtering out inappropriate solutions, we employ control of melting temperature to amplify the surviving DNA strings proportionally. The product is used as the input and the iteration is performed repeatedly. Accordingly, the concentration of correct solutions will be increased.
Our results agree with that obtained by conventional ant colony optimization algorithms and are better than that obtained by genetic algorithms. The same idea can be applied to design methods for solving other combinatorial problems with DNA computing.

36 
Greedy randomized adaptive search procedure for traveling salesman problemLee, Seung Ho 16 August 2006 (has links)
In this thesis we use greedy randomize adaptive search procedure (GRASP) to solve
the traveling salesman problem (TSP). Starting with nearest neighbor method to
construct the initial TSP tour, we apply the 2opt and the pathrelinking method
for the initial tour improvement. To increase 2opt search speed, fixedradius near
neighbor search and don0t − look bit techniques are introduced. For the same reason
a new efficient data structure, the reverse array, is proposed to represent the TSP
tour. Computational results show that GRASP gives fairly good solutions in a short
time.

37 
A new rank based version of the Ant System. A computational study.Bullnheimer, Bernd, Hartl, Richard F., Strauß, Christine January 1997 (has links) (PDF)
The ant system is a new metaheuristic for hard combinatorial optimization problems. It is a populationbased approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP), but has been also successfully applied to problems such as quadratic assignment, jobshop scheduling, vehicle routing and graph coloring.In this paper we introduce a new rank based version of the ant system and present results of a computational study, where we compare the ant system with simulated annealing and a genetic algorithm on several TSP instances. It turns out that our rank based ant system can compete with the other methods in terms of average behavior, and shows even better worst case behavior. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

38 
Dynamic Programming: Salesman to SurgeonQian, David January 2013 (has links)
Dynamic Programming is an optimization technique used in computer science and mathematics. Introduced in the 1950s, it has been applied to many classic combinatorial optimization problems, such as the Shortest Path Problem, the Knapsack Problem, and the Traveling Salesman Problem, with varying degrees of practical success.
In this thesis, we present two applications of dynamic programming to optimization problems. The first application is as a method to compute the BranchCutandPrice (BCP) family of lower bounds for the Traveling Salesman Problem (TSP), and several vehicle routing problems that generalize it. We then prove that the BCP family provides a set of lower bounds that is at least as strong as the Approximate Linear Program (ALP) family of lower bounds for the TSP. The second application is a novel dynamic programming model used to determine the placement of cuts for a particular form of skull surgery called Cranial Vault Remodeling.

39 
Biogeographybased optimization synergies with evolutionary strategies, immigration refusal, and Kalman filters /Du, Dawei. January 2009 (has links)
Thesis (M.S.)Cleveland State University, 2009. / Abstract. Title from PDF t.p. (viewed on Sept. 8, 2009). Includes bibliographical references (p. 7073). Available online via the OhioLINK ETD Center and also available in print.

40 
Brain Connectome Network Properties VisualizationZhang, Chenfeng 12 1900 (has links)
Indiana UniversityPurdue University Indianapolis (IUPUI) / Brain connectome network visualization could help the neurologists inspect the brain structure easily and quickly. In the thesis, the model of the brain connectome network is visualized in both three dimensions (3D) environment and two dimensions (2D) environment. One is named “Brain Explorer for Connectomic Analysis” (BECA) developed by the previous research already. It could present the 3D model of brain structure with region of interests (ROIs) in different colors [5]. The other is mainly for the information visualization of brain connectome in 2D. It adopts the forcedirected layout to visualize the network. However, the brain network visualization could not bring the user intuitively ideas about brain structure. Sometimes, with the increasing scales of ROIs (nodes), the visualization would bring more visual clutter for readers [3]. So, brain connectome network properties visualization becomes a useful complement to brain network visualization. For a better understanding of the effect of Alzheimer’s disease on the brain nerves, the thesis introduces several methods about the brain graph properties visualization. There are the five selected graph properties discussed in the thesis. The degree and closeness are node properties. The shortest path, maximum flow, and clique are edge properties. Except for clique, the other properties are visualized in both 3D and 2D. The clique is visualized only in 2D. For the clique, a new hypergraph visualization method is proposed with three different algorithms. Instead of using an extra node to present a clique, the thesis uses a “belt” to connect all nodes within the same clique. The methods of node connections are based on the traveling salesman problem (TSP) and Law of cosines. In addition, the thesis also applies the result of the clique to adjust the forcedirected layout of brain graph in 2D to dramatically eliminate the visual clutter. Therefore, with the support of the graph properties visualization, the brain connectome network visualization tools become more flexible.

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