Modified Ant Colony Optimization with pheromone mutation for travelling salesman problem

Abstract

Ant Colony Optimization (ACO) algorithm is a stochastic algorithm that is used for solving combinational optimization problem. It is inspired by the foraging behavior of ant colony. The ant colony walks along density of pheromone from ant's nest to feeding sources. It leads to create shortest path from ant's nest to feeding sources. ACO is normally troubled with the problems of trapping in local optimum. This paper proposed an improved ACO algorithm by mutation is applied with pheromone of ants when ant colony traps in local optimum. The proposed technique is tested on twenty-two maps from the Traveling Salesman Problem Library (TSPLIB) and gives more satisfied search results in comparison with ACOs.