Genetic algorithm with neighbor solution approach for Traveling Salesman Problem

Title:

Genetic algorithm with neighbor solution approach for Traveling Salesman Problem

Creator:

Zitar, Raed Abu, Essam , Hanandeh, and Shehabat , Essa

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:2ce3b48b-d14b-4ef7-ba7e-12975b97b7a6
uuid:2ce3b48b-d14b-4ef7-ba7e-12975b97b7a6

Subject:

Genetic algorithm, nearest neighbor crossover, Traveling Salesman problem, and least cost path

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The Traveling Salesman Problem (TSP) is an NP-Complete problem. Many techniques were developed to solve such problem, including Genetic Algorithms (GA's). The goal of this research is to enhance the performance of the Genetic Algorithm (GA) in solving the TSP. We achieve this goal by developing a local search algorithm called Search for Nearest Solution Algorithm (SNSA). This algorithm produces better solutions in acceptable periods of time. A new Crossover operator is proposed and used in the SNSA. Results for benchmark cases of the TSP show that the algorithm can find known optimum solutions. Comparisons between the proposed algorithm and other GA based methods with known crossover techniques show that SNSA has good quality solutions.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:5

Harvested from:

CDK

Metadata only:

false