Solving Travelling Salesman Problem by Using Optimization Algorithms

Suhair Saud; Halife Kodaz; İsmail Babaoğlu

Article Details

Solving Travelling Salesman Problem by Using Optimization Algorithms

Authors:

Suhair Saud | Halife Kodaz | İsmail Babaoğlu

Publisher:

KnE Social Sciences

Journal:

KnE Social Sciences Vol.3 Issue 1, pp.17-32

Document Type:

Article

Publication Date:

1/2018

Document Language:

English

Document ID (Org No.):

2001515868

pISSN:

2518-668X

Abstract:

 This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered  as one of the widely known of combinatorial optimization. Combinatorial optimization problems are usually simple to state but very difficult to solve. ACO, PSO, and SFLA are intelligent meta-heuristic optimization algorithms with strong ability to analyze the optimization problems and find the optimal solution. They were tested on benchmark problems from TSPLIB and the test results were compared with each other.

Article Details

Solving Travelling Salesman Problem by Using Optimization Algorithms

Authors: Suhair Saud | Halife Kodaz | İsmail Babaoğlu

Publisher: KnE Social Sciences

Journal: KnE Social Sciences Vol.3 Issue 1, pp.17-32

Document Type: Article

Publication Date: 1/2018

Document Language: English

Document ID (Org No.): 2001515868

pISSN: 2518-668X

Issues

Authors:

Suhair Saud | Halife Kodaz | İsmail Babaoğlu

Publisher:

KnE Social Sciences

Journal:

KnE Social Sciences Vol.3 Issue 1, pp.17-32

Document Type:

Article

Publication Date:

1/2018

Document Language:

English

Document ID (Org No.):

2001515868

pISSN:

2518-668X