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Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation

by Vani Maheshwari, Unmukh Datta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 13
Year of Publication: 2014
Authors: Vani Maheshwari, Unmukh Datta
10.5120/15945-5273

Vani Maheshwari, Unmukh Datta . Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation. International Journal of Computer Applications. 91, 13 ( April 2014), 37-40. DOI=10.5120/15945-5273

@article{ 10.5120/15945-5273,
author = { Vani Maheshwari, Unmukh Datta },
title = { Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 13 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number13/15945-5273/ },
doi = { 10.5120/15945-5273 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:42.085490+05:30
%A Vani Maheshwari
%A Unmukh Datta
%T Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 13
%P 37-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Swarm intelligence systems are made up of a population of simple agents interacting locally with each another and with their environment. Artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE) etc, are some example of swarm intelligence. In this work, an efficient modified version of ABC algorithm is proposed, where two additional operator crossover and mutation operator is used in the ABC algorithm. Here Crossover operator is used after the employed bee phase and mutation operator is used after scout bee phase of ABC algorithm. Proposed algorithm is applied at standard travelling salesman problem (TSP) for checking the efficiency of proposed algorithm and also simulated results are compared with ABC with uniform mutation algorithm and Basic ABC algorithm. The simulated result showed that the proposed algorithm is better than all the modified version of ABC algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Bee Colony crossover Mutation Genetic Algorithm Travelling salesman problem.

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