Ant colony optimisation is a population-based advanced approach for finding the solution of difficult problems with the help of a bioinspired approach from the behaviour of natural ants. The ant colony algorithm is a propelled optimisation method which is utilised to take care of combinatorial optimisation problems. The significant features of this algorithm are the utilisation of a mixture of preinformation and postinformation for organizing great solutions. The ant colony algorithm is used in this paper for solving the travelling salesman problem of the real set of data and getting the optimal results on graphs. This algorithm is an meta-heuristic algorithm in which we used the 2-opt local search method for tour construction and roulette wheel selection method for selection of nodes while constructing the route. The results show that this algorithm can efficiently find the optimal path of the hundred cities with minimum time and cost.

Ant Colony Optimization Meta-heuristic for Solving Real Travelling Salesman Problem

Ant Colony Optimization (ACO) is a kind of Meta heuristics approach which is simulated from the social behaviour of ants. It could be a good alternative approach to solve NP hard combinatorial optimization problems such as 0-1 knapsack problem and the Traveling Salesman Problem (TSP).The ACO can get a solution that is quite nearer to the optimal solution; however premature input bogs the system down. Parallelization is an effective way to solve large-scale ant colony optimization problems, since the better solution requires larger number of ants and iterations which consume more time. The problem can be solved by Map Reduce based ACO approach

Omprakash Tembhurne

Papers

An Approach to Combinatorial Problems by MapReduce Based Ant Colony Optimization