Using Local Searches Algorithms with Ant Colony Optimization for the Solution of TSP Problems
DOI:
https://doi.org/10.25007/ajnu.v7n3a193Keywords:
Travels Salesman Problem (TSP), Ant Colony Algorithm (ACO), Great Deluge Algorithm, Optimization, Opt algorithmAbstract
Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.
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Almufti, S. Mohammed (2017), " Using Swarm Intelligence for solving NP-Hard Problems", Academic Journal of Nawroz University, doi
( https ://doi.org/10.25007/ajnu.v6n3a78 ).
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Copyright (c) 2018 Renas R. Asaad, Nisreen L. Abdulnabi
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