Comparative analysis of travelling salesman problem using metaheuristic algorithms

2016 ◽  
Author(s):  
Ayush Agarwal ◽  
Rajendra Singh
Keyword(s):  
Comparative Analysis ◽  
Travelling Salesman Problem ◽  
Metaheuristic Algorithms ◽  
Travelling Salesman

DIMMA

2010 ◽  
Vol 1 (4) ◽  
pp. 57-74 ◽  
Author(s):  
Masoud Yaghini ◽  
Rahim Akhavan
Keyword(s):  
Tabu Search ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Metaheuristic Algorithms ◽  
Standard Process ◽  
Travelling Salesman ◽  
Design And Implementation ◽  
The Future ◽  
Implementation Methodology

Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.

DIMMA

2012 ◽  
pp. 583-601
Author(s):  
Masoud Yaghini ◽  
Mohammad Rahim Akhavan Kazemzadeh
Keyword(s):  
Tabu Search ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Metaheuristic Algorithms ◽  
Standard Process ◽  
Travelling Salesman ◽  
Design And Implementation ◽  
The Future ◽  
Implementation Methodology

Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.

DIMMA

2012 ◽  
pp. 90-108
Author(s):  
Masoud Yaghini ◽  
Mohammad Rahim Akhavan Kazemzadeh
Keyword(s):  
Tabu Search ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Metaheuristic Algorithms ◽  
Standard Process ◽  
Travelling Salesman ◽  
Design And Implementation ◽  
The Future ◽  
Implementation Methodology

Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.

scholarly journals EFFICIENT METAHEURISTIC ALGORITHMS FOR THE MULTI-STRIPE TRAVELLING SALESMAN PROBLEM

2020 ◽  
Vol 36 (3) ◽  
pp. 233-250
Author(s):  
Ban Ha Bang
Keyword(s):  
Quadratic Assignment Problem ◽  
Travelling Salesman Problem ◽  
Computation Time ◽  
Metaheuristic Algorithms ◽  
Neighborhood Search ◽  
Quadratic Assignment ◽  
Travelling Salesman ◽  
Local Optima ◽  
Benchmark Instances ◽  
Main Components

The Multi-stripe Travelling Salesman Problem (Ms-TSP) is an extension of the Travelling Salesman Problem (TSP). In the \textit{q}-stripe TSP with $q \geq 1$, the objective function sums the costs for travelling from one customer to each of the next \textit{q} customers along the tour. The resulting \textit{q}-stripe TSP generalizes the TSP and forms a special case of the Quadratic Assignment Problem. To solve medium and large size instances, a metaheuristic algorithm is proposed. The proposed algorithm has two main components, which are construction and improvement phases. The construction phase generates a solution using Greedy Randomized Adaptive Search Procedure (GRASP) while the optimization phase improves the solution with several variants of Variable Neighborhood Search, both coupled with a technique called Shaking Technique to escape from local optima. In addition, Adaptive Memory is integrated into our algorithms to balance between the diversification and intensification. To show the efficiency of our proposed metaheuristic algorithms, we extensively experiment on benchmark instances. The results indicate that the developed algorithms can produce efficient and effective solutions at a reasonable computation time.

Comparative Analysis of ACO Algorithms for the Solution of the Travelling Salesman Problem

2021 ◽  
pp. 338-358
Author(s):  
Gloria Lola Quispe ◽  
Maria Fernanda Rodríguez ◽  
José Daniel Ontiveros
Keyword(s):  
Comparative Analysis ◽  
Travelling Salesman Problem ◽  
Point Of View ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant System ◽  
Travelling Salesman ◽  
Deterministic Algorithms ◽  
Test Instance ◽  
High Degree

Metaheuristics are non-deterministic algorithms. Metaheuristic strategies are related to design. This chapter presents an introduction on metaheuristics, from the point of view of its theoretical study and the foundations for its use. Likewise, a description and comparative study of the ant colony-based algorithms is carried out. These are ant system (AS), ant colony system (ACS), and max-min ant system (MMAS). These results serve to deliver solutions to complex problems and generally with a high degree of combinatorics for those there is no way to find the best reasonable time. An experimentation and analysis of the results of the ACO algorithms (optimization by ants colonies) is also carried out. For the evaluation of the algorithms, comparisons are made for instances of the TSPLIB test instance library. Therefore, it is deepened in the resolution of the travelling salesman problem (TSP), and a comparative analysis of the different algorithms is carried out in order to see which one adjusts better.

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