AN EFFICIENT CROSSOVER OPERATOR FOR TRAVELING SALESMAN PROBLEM

Authors

  • A. Shariat Mohaymany
  • M. Babaei
  • M. Rajabi Bahaabadi
Abstract:

Crossover operator plays a crucial role in the efficiency of genetic algorithm (GA). Several crossover operators have been proposed for solving the travelling salesman problem (TSP) in the literature. These operators have paid less attention to the characteristics of the traveling salesman problem, and majority of these operators can only generate feasible solutions. In this paper, a crossover operator is presented that has the capability of generating solutions based on a logical reasoning. In other words, the solution space is explored by the proposed method purposefully. Numerical results based on 26 benchmark instances demonstrate the efficiency of the proposed method compared with the previous meta-heuristic methods.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

New Genetic Operator (Jump Crossover) for the Traveling Salesman Problem

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesm...

full text

An Approach for Solving Traveling Salesman Problem

In this paper, we introduce a new approach for solving the traveling salesman problems (TSP) and provide a solution algorithm for a variant of this problem. The concept of the proposed method is based on the Hungarian algorithm, which has been used to solve an assignment problem for reaching an optimal solution. We introduced a new fittest criterion for crossing over such problems, and illu...

full text

Voronoi Quantized Crossover for Traveling Salesman Problem

It is known that the performance of a genetic algorithm depends on the survival environment and the reproducibility of building blocks. In this paper, we propose a new encoding/crossover scheme that uses genic distance which explicitly de nes the distance between each pair of genes in the chromosome. It pursues both relatively high survival probabilities of more epistatic gene groups and divers...

full text

Voronoi Quantizied Crossover For Traveling Salesman Problem

It is known that the performance of a genetic algorithm depends on the survival environment and the reproducibility of building blocks. In this paper, we propose a new encoding/crossover scheme that uses genic distance which explicitly defines the distance between each pair of genes in the chromosome. It pursues both relatively high survival probabilities of more epistatic gene groups and diver...

full text

Solving the Traveling Salesman Problem by an Efficient Hybrid Metaheuristic Algorithm

The traveling salesman problem (TSP) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. The TSP is probably the most famous and extensively studied problem in the field of combinatorial optimization. Because this problem is an NP-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

full text

Solving the Traveling Salesman Problem by an Efficient Hybrid Metaheuristic Algorithm

The traveling salesman problem (TSP) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. The TSP is probably the most famous and extensively studied problem in the field of combinatorial optimization. Because this problem is an NP-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 2  issue 4

pages  607- 619

publication date 2012-10

By following a journal you will be notified via email when a new issue of this journal is published.

Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023