Enhanced Genetic Algorithm Inspired Cuckoo Search (GACS) Algorithm for Combinatorial Optimization Travelling Salesman Problem

Authors(2) :-R. Pavithra, K. Mythili

MLT finds potentially useful patterns in the data. Optimisation problems, either single-objective or multi-objective, are generally difficult to solve. The most famous example is probably the traveling salesman problem (TSP) in which a salesperson intends to visit a number of cities exactly once, and returning to its starting point, while minimizing the total distance traveled or the overall cost of the trip. TSP is one of the most widely studied problems in combinatorial optimization. It belongs to the class of NP-hard optimization problems, whose the computational complexity increases exponentially with the number of cities. It is often used for testing optimization algorithms. This paper proposed genetic algorithm based cuckoo search technique for TSP. Result significantly improves the performance of the TSP. The algorithm is implemented using MATLAB. Results seems to be promising when compared with the existing methods for the problem.

Authors and Affiliations

R. Pavithra
Hindusthan College of Arts and Science, Coimbatore, Tamil Nadu, India
K. Mythili
Head & Associate Professor, Department of IT/CT, Hindusthan College of Arts and Science, Coimbatore, Tamil Nadu, India

MLT, TSP, Genetic Algorithm, Cuckoo search, NP Hard problem

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 746-750
Manuscript Number : CSEIT1724166
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

R. Pavithra, K. Mythili, "Enhanced Genetic Algorithm Inspired Cuckoo Search (GACS) Algorithm for Combinatorial Optimization Travelling Salesman Problem ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.746-750, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT1724166

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