scholarly journals Basic Evolutionary Approach to the Traveling Salesman Problem

2018 ◽  
Vol 1 (2) ◽  
pp. 30-38
Author(s):  
Débora Regina De São José ◽  
Mauricio Garcia Hernandez
Keyword(s):  
Traveling Salesman Problem ◽  
Evolutionary Programming ◽  
Traveling Salesman ◽  
Evolutionary Approach ◽  
Average Error ◽  
Test Case ◽  
Simple Simulation ◽  
Alternative Approach ◽  
Benchmark Instances ◽  
The Traveling Salesman Problem

Evolutionary programming (EP) is a metaheuristic method developed as an alternative approach to artificial intelligence. The aim of this paper is to bring an introduction to EP algorithms through the implementation of the basic D. B. Fogel’s Evolutionary Programing approach of 1988 and the emulation of his results in order to analyze the performance of the evolutionary programming method on solving a benchmark test case. The EP approach is implemented thru a simple simulation of natural evolution and the allowance of probabilistic survival of individuals. The novelty of this paper relies on testing the algorithm performance in some problems of well-known benchmark instances of the Traveling Salesman Problem, where that 1988 evolutionary approach was not tested. The reproduction of 1988 D. B. Fogel’s approach was possible, the found average error of this method for 200000 offspring applied to the benchmark instances was found to be in the order of the 10%.

scholarly journals Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP

1993 ◽  
Vol 1 (4) ◽  
pp. 313-333 ◽  
Author(s):  
Christine L. Valenzuela ◽  
Antonia J. Jones
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Search Problem ◽  
Genetic Approach ◽  
Solution Quality ◽  
Alternative Approach ◽  
The Traveling Salesman Problem

Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.

scholarly journals THE TRAVELING SALESMAN PROBLEM WITH MULTI-VISIT DRONE

2021 ◽  
Vol 37 (4) ◽  
pp. 465-493
Author(s):  
Quang Minh Ha ◽  
Duy Manh Vu ◽  
Xuan Thanh Le ◽  
Minh Ha Hoang
Keyword(s):  
Traveling Salesman Problem ◽  
Positive Impact ◽  
Iterated Local Search ◽  
Traveling Salesman ◽  
Mixed Integer ◽  
Solution Quality ◽  
Benchmark Instances ◽  
Time Optimal ◽  
The Traveling Salesman Problem ◽  
First Time

This paper deals with the Traveling Salesman Problem with Multi-Visit Drone (TSP-MVD) in which a truck works in collaboration with a drone that can serve up to q > 1 customers consecutively during each sortie. We propose a Mixed Integer Linear Programming (MILP) formulation and a metaheuristic based on Iterated Local Search to solve the problem. Benchmark instances collected from the literature of the special case with q = 1 are used to test the performance of our algorithms. The obtained results show that our MILP model can solve a number of instances to optimality. This is the first time optimal solutions for these instances are reported. Our ILS performs better other algorithms in terms of both solution quality and running time on several class of instances. The numerical results obtained by testing the methods on new randomly generated instances show again the effectiveness of the methods as well as the positive impact of using the multi-visit drone.

A New Heuristic for Traveling Salesman Problem Based on LCO

2012 ◽  
Author(s):  
Soichiro Yokoyama ◽  
Ikuo Suzuki ◽  
Masahito Yamamoto ◽  
Masashi Furukawa
Keyword(s):  
Traveling Salesman Problem ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Accurate Solution ◽  
Traveling Salesman ◽  
Wide Range ◽  
Benchmark Instances ◽  
Clustering Optimization ◽  
The Traveling Salesman Problem ◽  
Random Insertion

The Traveling Salesman Problem (TSP) is one of the most well known combinatorial optimization problem and has wide range of application. Since the TSP is NP-hard, many heuristics for the TSP have been developed. This study proposes a new heuristic for the TSP based on one of these heuristics named Local Clustering Optimization (LCO). LCO is a metaheuristic proposed by Furukawa at el. to give an accurate solution for large scale problems in a reasonable time. However, conventional LCO-based heuristics for the TSP is not suited to solving asymmetric instances. The proposed method iteratively adopts tour construction heuristics such as nearest neighbor and random insertion to get an accurate solution more efficiently for the both asymmetric and symmetric TSP. The proposed method and other heuristics are applied to benchmark instances from TSPLIB and randomly generated instances. The experiment shows the proposed method is superior to conventional LCO in terms of accuracy of the solution. However, the proposed method is inefficient for instances which are not close to Euclidean due to the same property of insertion heuristic.

A New Heuristic Procedure To Solve The Traveling Salesman Problem

2007 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Paulo Henrique Siqueira ◽  
Sérgio Scheer ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Heuristic Procedure ◽  
The Traveling Salesman Problem

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Sign in / Sign up

Export Citation Format

Share Document