Abstract
Ant Colony optimization is a heuristic technique which has been applied to a number of combinatorial optimization problem and is based on the foraging behavior of the ants. Travelling Salesperson problem is a combinatorial optimization problem which requires that each city should be visited once. In this research paper we use the K means clustering technique and Enhanced Ant Colony Optimization algorithm to solve the TSP problem. We show a comparison of the traditional approach with the proposed approach. The simulated results show that the proposed algorithm is better compared to the traditional approach.
Highlights
Swarm Intelligence is a new approach to problem solving which is based on the collective behavior of simple agents interacting locally with one another and their environment and was first introduced by Gerardo Beni and Jing Wang in 1980 in the context of cellular robotic systems [1].Ant Colony Optimization is a meta-heuristic technique which is used to solve the various combinatorial optimization problems and was first proposed by the Italian Scholar M.Dorigo in 1991[2,3]
If there are n cities to be travelled n-1! is the total number of possible routes that can be covered paper proposed a new algorithm for solving TSP using Ant Colony Optimization Algorithm based on immunity and multiple ant colonies
Bifan Li [15] in his paper proposed a new model of Ant Colony Optimization to solve the travelling salesperson problem by introducing ants with memory into the Ant Colony System .In the new ant system ants can remember and make use of the best so far solutions, so that the algorithm is able to converge into at least near optimum solution quickly
Summary
Swarm Intelligence is a new approach to problem solving which is based on the collective behavior of simple agents interacting locally with one another and their environment and was first introduced by Gerardo Beni and Jing Wang in 1980 in the context of cellular robotic systems [1].Ant Colony Optimization is a meta-heuristic technique which is used to solve the various combinatorial optimization problems and was first proposed by the Italian Scholar M.Dorigo in 1991[2,3]. Hara.A[14] in his paper proposed a new ACO method using heterogeneous ants for Travelling Salesman Problem. In his proposed method there exist the normal ants and the exploratory ants which construct partial solutions. Shih-Pang Tseng [16] in his paper presented an efficient method for speeding up Ant Colony Optimization called Pattern Reduction Enhanced Ant Colony Optimization He used Travelling Salesperson problem to evaluate the performance of the proposed Algorithm. Gao Shang [17] in his paper integrated Ant Colony Optimization and Association rule to solve the TSP problem The results of this new algorithm are better when compared to simulated annealing, genetic algorithm and the standard ant colony algorithm
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