travelling-salesman-problem | C | Architecture library

by kamilmysliwiec C++ Version: Current License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | travelling-salesman-problem Summary

travelling-salesman-problem is a C++ library typically used in Architecture applications. travelling-salesman-problem has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

The Travelling Salesman Problem - parallel with OpenMP. C++

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travelling-salesman-problem has a low active ecosystem.

It has 6 star(s) with 1 fork(s). There are no watchers for this library.

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travelling-salesman-problem Key Features

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travelling-salesman-problem Examples and Code Snippets

Perform tabu search .

python

Lines of Code : 64

License : Permissive (MIT License)

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def tabu_search(
    first_solution, distance_of_first_solution, dict_of_neighbours, iters, size
):
    """
    Pure implementation of Tabu search algorithm for a Travelling Salesman Problem in
    Python.

    :param first_solution: The solution for

Community Discussions

Trending Discussions on travelling-salesman-problem

Suggestions for speeding up a dynamic programming solution to the Traveling Salesman Problem?

Using array_splice causes "Undefined Offset"-error

QUESTION

Suggestions for speeding up a dynamic programming solution to the Traveling Salesman Problem?

Asked 2018-Dec-15 at 05:21

I'm following an online course in which one of the assignments is to implement a dynamic programming algorithm to solve the Traveling Salesman Problem (TSP). My Python implementation works for small cases (~5 cities), but for the 'real' application of 25 cities it seems to be very slow. I'm looking for suggestions to speed up the algorithm.

The algorithm is described in the following excerpts:

The dynamic programming solution is also described at http://www.geeksforgeeks.org/travelling-salesman-problem-set-1/, where additional references are given.

The problem statement of the assignment is:

I've implemented the pseudocode using a pandas DataFrame object for the array A. Since sets are not hashable and can't be used as indices, I've instead used tuples, taking care to sort them in order to make them unique representations of sets. Here is the code along with several test cases of increasing size:

...

ANSWER

Answered 2017-Sep-20 at 22:00

Some ideas how to improve performance:

instead of tuples use 32 bit ints to represent your subsets - this should be enough if you have no more than 32 cities
on each step you need previously calculated values for subsets of size m - 1 only (you don't have to store any values for subsets of size m-2, m-3 etc.) - this may vastly reduce your memory usage

Source https://stackoverflow.com/questions/46317631

QUESTION

Using array_splice causes "Undefined Offset"-error

Asked 2017-Feb-11 at 00:04

Good evening guys,

Currently working on a piece of code with arrays that would be deleted if a condition is met. After trying it with array_unset I decided to use array_splice to not destroy the id-structure. Unfortunately I have the same problem:

...

ANSWER

Answered 2017-Feb-11 at 00:04

I tried to simplify your code as much as possible according to your description. If something will be unclear, don't hesitate to ask. I omitted the 'next_city' item in the result, because it seems unnecessary (due to same data in the next item), but the code can be easily altered.

Worth to mention those things after reviewing your code:

try to use variable variables ${xy} only when it's necessary, it's less readable, even for you after a year or two :-)
when you're dealig with iterables (e.g. arrays) with unclear index, use foreach loop instead of for. You won't come into problems with undefined indexes when you remove items from array.

The final code (without array definition):

Source https://stackoverflow.com/questions/42168322

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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