https://github.com/onkolahmet/tsp
using 2-opt algorithm with fast and guided local search heuristics
https://github.com/onkolahmet/tsp
heuristic-search-algorithms local-search tsp-problem
Last synced: 3 months ago
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using 2-opt algorithm with fast and guided local search heuristics
- Host: GitHub
- URL: https://github.com/onkolahmet/tsp
- Owner: onkolahmet
- Created: 2020-07-12T14:33:32.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-10-23T21:34:58.000Z (about 5 years ago)
- Last Synced: 2025-03-30T11:30:27.625Z (8 months ago)
- Topics: heuristic-search-algorithms, local-search, tsp-problem
- Language: Java
- Homepage:
- Size: 409 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# TSP
- The travelling salesman problem (TSP) asks the following question:
- "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?"
- Main purpose of this problem is to find the shortest path which is as close to the optimal tour as possible.
- Since TSP problem is [NP-hard problem](https://en.wikipedia.org/wiki/NP-hardness), best approach for effiency is to find the approximate result in shortest amount of time.
## What is used and how it works ?
- I decided to use [2-opt heuristic algorithm](https://en.wikipedia.org/wiki/2-opt).
- In this approach algorithm works by looking for search area of possible solutions that will result in different set of permutations of a tour.
## 2-opt Approach with Fast Local Search(FLS) Algorithm
- For optimizing this approach , in first step, I used Fast Local Search(FLS) optimization. Having
declared 2-opt algorithm will create a chance to explore all possible 2-opt moves.
- One approach is to continuously select two pair of edges and check whether they would result a better tour.
- Instead of selecting random edges, FLS will iterate through all potential pair of edges and checking them for
potential moves. If we found a move that can be used, then the corresponding pair of edges will be
used in 2-opt algorithm and searching will be continued from the previous edge in the tour until
there is no possible moves left. Then at that point we found 2-optimal.
- The trouble that comes with this approach is wasting time to evaluating moves that will not give an
improvement. To overcome this situation if a move is found by the program, I simply declare the pair
of edges as used with using setUsedBits() and isUsed() functions. [So that program will not check
these edges ever again](http://tsp-basics.blogspot.com/2017/03/using-dont-look-bits-dlb-2-opt.html).
## 2-opt Approach with Guided Local Search(GLS) Algorithm
- In addition to FLS i decided to use [Guided Local Search](https://en.wikipedia.org/wiki/Guided_Local_Search) since it works synchronously with FLS.
- Guided Local Search is a metaheuristic search method. This approach is a method that builds on top of a local
search algorithm to explore the search space for finding near-optimal solutions.
- The main purpose of this approach is guiding a local search algorithm, FLS in this case, by improving the corresponding
objects that are declared and assigning a cost value to possible edges so that we can compare efficiently.
## References
- https://en.wikipedia.org/wiki/Guided_Local_Search
- https://en.wikipedia.org/wiki/NP-hardness
- https://pdfs.semanticscholar.org/2210/d4c2694894da361baf9175ac358970b58033.pdf
- http://tsp-basics.blogspot.com/2017/03/using-dont-look-bits-dlb-2-opt.html
- https://en.wikipedia.org/wiki/2-opt