An Improved Genetic Algorithm with 2-Opt Local Search for the Traveling Salesman Problem

2021 ◽  
pp. 404-409
Author(s):  
Jiashan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Improved Genetic Algorithm ◽  
The Traveling Salesman Problem

Application of an Improved Genetic Algorithm with the Search Space Compression in TSP

2013 ◽  
Vol 765-767 ◽  
pp. 687-689
Author(s):  
Yi Song ◽  
Ni Ni Wei
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Random Search ◽  
Search Space ◽  
Traveling Salesman ◽  
Improved Genetic Algorithm ◽  
Space Compression ◽  
Accelerate Convergence ◽  
The Traveling Salesman Problem ◽  
Exponential Relation

The Traveling Salesman Problem is a combinatorial optimization problem, the problem has been shown to belong to the NPC problem, the possible solution of Traveling Salesman Problem and the scale of the cities have the exponential relation, so the more bigger of the scale. In this paper, improve the search process of the genetic algorithm by introducing the idea is to compress the search space. The simulation results show that for solving the TSP, the algorithm can quickly obtain multiple high-quality solutions. It can reduce the blindness of random search and accelerate convergence of the algorithm.

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

The Auto Restoration of Paper Fragments Rules Based on the Traveling Salesman Model

2014 ◽  
Vol 533 ◽  
pp. 256-259
Author(s):  
Ao Jun Zhou
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Chinese Characters ◽  
Improved Genetic Algorithm ◽  
Restoration Process ◽  
Regular Paper ◽  
Edge Matching ◽  
Left And Right ◽  
The Traveling Salesman Problem

The restoration of regular paper fragments can be solved just as the Traveling Salesman Problem. Paper pretreatment and edge matching are the main steps during the restoration process, which contain binarization, screening between the left and right boundary depending the width of paper margins. For some longer paper fragments, the transverse splicing can be realized with Greed Algorithm. But the algorithm will get a non-ideal restoration for more fine paper fragments. Therefore the noise-suppressed processing before clustering from line to line through projection is essential in order to make sure that the regular English fragments can get the regularity like Chinese characters. The two experiments get an 100% correct rates when using the method combining the improved genetic algorithm for traveling salesman problem.combining the improved genetic algorithm for traveling salesman problem.

scholarly journals Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP)

2021 ◽  
Vol 16 (2) ◽  
pp. 173-184
Author(s):  
Y.D. Wang ◽  
X.C. Lu ◽  
J.R. Shen
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Real Life ◽  
Traveling Salesman ◽  
Improved Genetic Algorithm ◽  
Workload Balance ◽  
Multiple Traveling Salesman Problem ◽  
Polar Coordinate ◽  
Neighbourhood Algorithm ◽  
The Traveling Salesman Problem

The multiple traveling salesman problem (mTSP) is an extension of the traveling salesman problem (TSP), which has wider applications in real life than the traveling salesman problem such as transportation and delivery, task allocation, etc. In this paper, an improved genetic algorithm (VNS-GA) that uses polar coordinate classification to generate the initial solutions is proposed. It integrates the variable neighbourhood algorithm to solve the multiple objective optimization of the mTSP with workload balance. Aiming to workload balance, the first design of this paper is about generating initial solutions based on the polar coordinate classification. Then a distance comparison insertion operator is designed as a neighbourhood action for allocating paths in a targeted manner. Finally, the neighbourhood descent process in the variable neighbourhood algorithm is fused into the genetic algorithm for the expansion of search space. The improved algorithm is tested on the TSPLIB standard data set and compared with other genetic algorithms. The results show that the improved genetic algorithm can increase computational efficiency and obtain a better solution for workload balance and this algorithm has wild applications in real life such as multiple robots task allocation, school bus routing problem and other optimization problems.

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