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.

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.

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 Solving Multiple Traveling Salesman Problem by Meerkat Swarm Optimization Algorithm

2019 ◽  
Vol 54 (3) ◽  
Author(s):  
Belal Al-Khateeb ◽  
Mohammed Yousif
Keyword(s):  
Traveling Salesman Problem ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Real Life ◽  
Traveling Salesman ◽  
Swarm Optimization ◽  
Total Cost ◽  
Life Problems ◽  
Multiple Traveling Salesman Problem ◽  
The Traveling Salesman Problem

Multiple Traveling Salesman Problem (MTSP) is one of various real-life applications, MTSP is the extension of the Traveling Salesman Problem (TSP). TSP focuses on searching of minimum or shortest path (traveling distance) to visit all cities by salesman, while the primary goal of MTSP is to find shortest path for m paths by n salesmen with minimized total cost. Wherever, total cost means the sum of distances of all salesmen. In this work, we proposed metaheuristic algorithm is called Meerkat Swarm Optimization (MSO) algorithm for solving MTSP and guarantee good quality solution in reasonable time for real-life problems. MSO is a metaheuristic optimization algorithm that is derived from the behavior of Meerkat in finding the shortest path. The implementation is done using many dataset from TSPLIB95. The results demonstrate that MSO in most results is better than another results that compared in average cost that means the MSO superior to other results of MTSP.

scholarly journals Model Penentuan Rute Terpendek Penjemputan Sampah Menggunakan Metode MTSP dan Algoritma Genetika

2021 ◽  
Vol 2 (1) ◽  
pp. 43-48
Author(s):  
Aswandi ◽  
Sugiarto Cokrowibowo ◽  
Arnita Irianti
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Individual Fitness ◽  
Problem Method ◽  
Multiple Traveling Salesman Problem ◽  
Shortest Route ◽  
Fitness Value ◽  
The Creation ◽  
The Traveling Salesman Problem

Garbage pick-ups performed by two or more people must have a route in their pickup. However, it is not easy to model the route of the pickup that each point must be passed and each point is only passed once. Now, the method to create a route has been done a lot, one of the most commonly used methods is the creation of routes using the Traveling Salesman Problem method. Traveling Salesman Problem is a method to determine the route of a series of cities where each city is only traversed once. In this study, the shortest route modeling was conducted using Multiple Traveling Salesman Problem and Genetic Algorithm to find out the shortest route model that can be passed in garbage pickup. In this study, datasets will be used as pick-up points to then be programmed to model the shortest routes that can be traveled. The application of Multiple Traveling Salesman Problem method using Genetic Algorithm shows success to model garbage pickup route based on existing dataset, by setting the parameters of 100 generations and 100 population and 4 salesmen obtained 90% of the best individual opportunities obtained with the best individual fitness value of 0.05209. The test was conducted using BlackBox testing and the results of this test that the functionality on the system is 100% appropriate.

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