traveling-salesman

I made a memetic algorithm in Python for traveling salesman problem . However, all the test data (list of distances between cities) I've encountered lack the information of the best solution, so I can't know how close to global optimum my algorithm gets. Does anyone know where I can find some tsp test data (preferably in matrix form, but anything's good) with known best solution? Did you google? http://www.tsp.gatech.edu/data/index.html That page provides several test-cases of which 16 has an optimal solution. Perhaps you can generate your own test data? This definitely won't be comprehensive

Edit : An improvement to this algorithm been found. Your are welcome to see it. This question is the an improvement of my old question. Now I want to show you Java code sample , and explain my algorithm in more details. I think that I found a polynomial algorithm to get an exact solution to the Traveling Salesman Problem. My implementation is build from 5 steps: 1) Quick setup 2) Search for solution 3) Stop condition 1 4) Stop condition 2 5) Stop condition 3 I want to start from step 2 and 3, and if I do not get wrong there I will show you the rest of it. So what I am going to show you now, is

I'm trying to solve an augmented TSP problem using a graph database, but I'm struggling. I'm great with SQL, but am a total noob on cypher. I've created a simple graph with cities (nodes) and flights (relationships). THE SETUP: Travel to 8 different cities (1 city per week, no duplicates) with the lowest total flight cost. I'm trying to solve an optimal path to minimize the cost of the flights, which changes each week. Here is a file on pastebin containing my nodes & relationships. Just run it against Neo4JShell to insert the data. I started off using this article as a basis but it doesn't

I'm trying to solve this problem http://coj.uci.cu/24h/problem.xhtml?abb=1368 . After a lot of research, and spending a lot of time i was able to implement a Branch and Bound algorithm for TSP, which gets a path passing all points and returning to start. I was thinking that removing the longest edge from that path i would get the answer, but just when i finished my algorithm, i discovered that this isn't true in all cases, reading this question: Minimal Distance Hamiltonian Path Javascript I've found some answers saying that adding a dummy point with zero distance to every other point, and

In a "dense" graph, I am trying to construct a Hamiltonian cycle using Palmer's Algorithm . However, I need more explanation for this algorithm because it does not work with me when I implement it. It seems that there is an unclear part in Wikipedia's explanation. I would be thankful if someone explains it more clearly or give me some links to read. Here's the algorithm statement: Palmer (1997) describes the following simple algorithm for constructing a Hamiltonian cycle in a graph meeting Ore's condition. Arrange the vertices arbitrarily into a cycle, ignoring adjacencies in the graph. While

I want to solve the TSP problem using a dynamic programming algorithm in Python.The problem is: Input: cities represented as a list of points. For example, [(1,2), (0.3, 4.5), (9, 3)...]. The distance between cities is defined as the Euclidean distance. Output: the minimum cost of a traveling salesman tour for this instance, rounded down to the nearest integer. And the pseudo-code is: Let A = 2-D array, indexed by subsets of {1, 2, ,3, ..., n} that contains 1 and destinations j belongs to {1, 2, 3,...n} 1. Base case: 2. if S = {0}, then A[S, 1] = 0; 3. else, A[S, 1] = Infinity. 4.for m = 2, 3,

I have implemented a Genetic Algorithm to solve the Traveling Salesman Problem (TSP). When I use only mutation, I find better solutions than when I add in crossover. I know that normal crossover methods do not work for TSP, so I implemented both the Ordered Crossover and the PMX Crossover methods, and both suffer from bad results. Here are the other parameters I'm using: Mutation : Single Swap Mutation or Inverted Subsequence Mutation ( as described by Tiendil here ) with mutation rates tested between 1% and 25%. Selection : Roulette Wheel Selection Fitness function : 1 / distance of tour