scholarly journals A Genetic Method using Hybrid Crossover for Solving Travelling Salesman Problem

2019 ◽  
Vol 8 (2) ◽  
pp. 5066-5072
Keyword(s):  
Hybrid Method ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Performance Comparison ◽  
Initial Population ◽  
Genetic Approach ◽  
Comparison Standard ◽  
Travelling Salesman ◽  
Genetic Method ◽  
Better Than

This paper proposes a Genetic approach using Hybrid Crossover for Solving the Travelling Salesman Problem. Proposed hybrid method generates an initial population using Nearest Neighbor (NN) approach which is modified using “Sub-Path Mutation” (SPM) process. Modified population undergoes Distance Preserving Crossover (DPX) [2] and 2-opt Optimal mutation (2-opt) [1] to check for possible refinement. SPM searches position for the minimum distant city within a given path. This work is motivated by the algorithm developed by [3] who performed DPX and 2-opt mutation on the initial population generated using NN. For performance comparison, standard TSPLIB data is taken. The proposed hybrid method performances better in terms of % best error. It performs better than methods reported in [3 - 11].

scholarly journals The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem

2019 ◽  
Vol 12 (2) ◽  
pp. 98-105
Author(s):  
Ajchara Phu-ang ◽  
Duangjai Jitkongchuen
Keyword(s):  
Local Search ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Travelling Salesman Problem ◽  
Hill Climbing ◽  
Search Process ◽  
Initial Population ◽  
Travelling Salesman ◽  
Average Percentage ◽  
Surrounding Areas

This paper proposed the new algorithm intended to solve a specific real-world problem, the symmetric travelling salesman problem. The proposed algorithm is based on the concept of the galaxy based search algorithm (GbSA) and  embedded the new ideas called the clockwise search process and the cluster crossover operation. In the first step, the nearest neighbor algorithm introduces to generate the initial population. Then, the tabu list local search is employed to search for the new solution in surrounding areas of the initial population in the second step. The clockwise search process and the cluster crossover operation are employed to create more diversity of the new solution. Then, the final step, the hill climbing local search is utilized to increase the local search capabilities. The experiments with the standard benchmark test sets show that the proposed algorithm can be found the best average percentage deviation from the lower bound.

GPU-based parallel genetic approach to large-scale travelling salesman problem

2016 ◽  
Vol 72 (11) ◽  
pp. 4399-4414 ◽  
Author(s):  
Semin Kang ◽  
Sung-Soo Kim ◽  
Jongho Won ◽  
Young-Min Kang
Keyword(s):  
Large Scale ◽  
Travelling Salesman Problem ◽  
Genetic Approach ◽  
Travelling Salesman

scholarly journals GENETIC ALGORITHM FOR OPTIMIZING DISTRIBUTION WITH ROUTE RESTRICTION CONSTRAINT DUE TO TRAFFIC JAMS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 295-301
Author(s):  
N. Mouttaki ◽  
J. Benhra ◽  
G. Rguiga
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Classical Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Simple Method ◽  
Traffic Jams ◽  
Better Than

Abstract. The Travelling Salesman Problem (TSP) is a classical problem in combinatorial optimization that consists of finding the shortest tour through all cities such that the salesman visits each city only one time and returns to the starting city. Genetic algorithm is one of the powerful ways to solve problems of traveling salesman problem TSP. The current genetic algorithm aims to take in consideration the constraints happening during the execution of genetic algorithm, such as traffic jams when solving TSP. This program has two important contributions. First one is proposing simple method into taking in consideration an inconvenient route linked to traffic jams. The second one is the use of closeness strategy during the initialization step, which can accelerate the execution time of the algorithm.The results of the experiments show that the improved algorithm works better than some other algorithms. The conclusion ends the analysis with recommendations and future works.

scholarly journals Comparative Study of Prim and Genetic Algorithms in Minimum Spanning Tree and Travelling Salesman Problem

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Andre Hasudungan Lubis
Keyword(s):  
Genetic Algorithm ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Maximum Value ◽  
Large Numbers ◽  
Crossover And Mutation ◽  
Better Than

Optimization is the essential thing in an algorithm. It can save the operational cost of an activity. At the Minimum Spanning Tree, the goal to be achieved is how all nodes are connected with the smallest weights. Several algorithms can calculate the use of weights in this graph. Genetic and Primary algorithms are two very popular algorithms for optimization. Prim calculates the weights based on the short-est distance from a graph. This algorithm eliminates the connected loop to minimize circuit. The nature of this algorithm is to trace all nodes to the smallest weights on a given graph. The genetic algorithm works by determining the random value as first initialization. This algorithm will perform selection, crossover, and mutation by the number of rounds specified. It is possible that this algorithm can not achieve the maximum value. The nature of the genetic algorithm is to work with probability. The results obtained are the most optimal results according to this algorithm. The results of this study indicate that the Prim is better than Genetics in determining the weights at the minimum spanning tree while Genetic algorithm is better for travelling salesman problem. Genetics will have maximum results when using large numbers of rotations and populations.

scholarly journals Penerapan Bat Algorithm Dalam Penyelsaian Kasus Travelling Salesman Problem (TSP) Pada Internship Program

2021 ◽  
Vol 6 (2) ◽  
pp. 111-116
Author(s):  
Veri Julianto ◽  
Hendrik Setyo Utomo ◽  
Muhammad Rusyadi Arrahimi
Keyword(s):  
Global Minimum ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Bat Algorithm ◽  
Travelling Salesman ◽  
Street Vendors ◽  
Shortest Distance ◽  
Using Data ◽  
Feasible Solutions ◽  
The Traveling Salesman Problem

This optimization is an optimization case that organizes all possible and feasible solutions in discrete form. One form of combinatorial optimization that can be used as material in testing a method is the Traveling Salesman Problem (TSP). In this study, the bat algorithm will be used to find the optimum value in TSP. Utilization of the Metaheuristic Algorithm through the concept of the Bat Algorithm is able to provide optimal results in searching for the shortest distance in the case of TSP. Based on trials conducted using data on the location of student street vendors, the Bat Algortima is able to obtain the global minimum or the shortest distance when compared to the nearest neighbor method, Hungarian method, branch and bound method.

scholarly journals Lifting the Performance of a Heuristic for the Time-Dependent Travelling Salesman Problem through Machine Learning

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 340
Author(s):  
Gianpaolo Ghiani ◽  
Tommaso Adamo ◽  
Pierpaolo Greco ◽  
Emanuela Guerriero
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Computing Time ◽  
Time Dependent ◽  
Machine Learning Techniques ◽  
Travelling Salesman ◽  
Distribution Management ◽  
Learning Techniques ◽  
Average Improvement

In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the quality of the solutions generated by a heuristic for the Time-Dependent Travelling Salesman Problem with no increased computing time. This can be useful in a real-time setting where a speed update (or the arrival of a new customer request) may lead to the reoptimization of the planned route. The main contribution of this work is to show how to reuse the information gained in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. We use a method based on the nearest neighbor procedure (supervised learning) and the K-means algorithm with the Euclidean distance (unsupervised learning). In order to show the effectiveness of this approach, the computational experiments have been carried out for the dataset generated based on the real travel time functions of two European cities: Paris and London. The overall average improvement of our heuristic over the classical nearest neighbor procedure is about 5% for London, and about 4% for Paris.

An Improvement Particle Swarm Optimization for Travelling Salesman Problem with the Mutation Operator

2015 ◽  
Vol 781 ◽  
pp. 527-530 ◽  
Author(s):  
Jaturong Sriborikit ◽  
Panwit Tuwanut
Keyword(s):  
Particle Swarm Optimization ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Local Optimum ◽  
Mutation Operator ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Research Show ◽  
Better Than

This document proposed improvement PSO with applying mutation operator for solving Travelling Salesman Problem. To PSO solve or decrease trapping in local optimum. From experiment results of this research show that results of PSO with applying mutation operator obtain better than results of normal PSO for solving TSP.

scholarly journals Improving Initial Population for Genetic Algorithm using the Multi Linear Regression Based Technique

2020 ◽  
Vol 23 (1) ◽  
pp. E1-E10
Author(s):  
Esra'a Alkafaween ◽  
Ahmad B. A. Hassanat ◽  
Sakher Tarawneh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Regression ◽  
Heuristic Search ◽  
Large Scale ◽  
Travelling Salesman Problem ◽  
Initial Population ◽  
Problem Size ◽  
Travelling Salesman ◽  
Multi Linear Regression

Genetic algorithms (GAs) are powerful heuristic search techniques that are used successfully to solve problems for many different applications. Seeding the initial population is considered as the first step of the GAs. In this work, a new method is proposed, for the initial population seeding called the Multi Linear Regression Based Technique (MLRBT). That method divides a given large scale TSP problem into smaller sub-problems and the technique works frequently until the sub-problem size is very small, four cities or less. Experiments were carried out using the well-known Travelling Salesman Problem (TSP) instances and they showed promising results in improving the GAs' performance to solve the TSP.

Application of Genetic Algorithm on Travelling Salesman Person

2014 ◽  
Vol 1048 ◽  
pp. 526-530
Author(s):  
Sambourou Massinanke ◽  
Chao Zhu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Optimization Method ◽  
Initial Population ◽  
Travelling Salesman ◽  
Minimum Path ◽  
Minimum Number ◽  
Permutation Problem ◽  
The City ◽  
Survival Of The Fittest

GA (Genetic algorithm) is an optimization method based on operators (mutation and crossover) utilizing a survival of the fittest idea. They are utilized favorably in various problems. (TSP) Travelling salesman problem is one of the famous studied. TSP is a permutation problem in which the aim is to determine the shortest tour between n different points (cities), otherwise, the problem aims to find a route covering all cities where that the total distance is minimal. In this study a single salesman travels to each of the cities and close the loop by returning to the city he started, the aim of this study is to determine the minimum number of generations in which salesman does the minimum path, cities are chosen at random as initial population. The new generations are then created iteratively till the proper path is attained.

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