Modified Ant Colony Optimization Algorithm for Traveling Salesman Problem

Abstract

Ant colony optimization is a technique for optimization that was introduced in the early 1990’s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. First, we study with the biological inspiration of ant colony optimization algorithms and how this biological inspiration can be transferred into an algorithm for Traveling Salesman Problem (TSP). Then, ant colony optimization outlined in more general terms in the context of discrete optimization, and some of the nowadays best performing ant colony optimization variants are studied. This research approach lies at initial stage at present, and a new modified ant algorithm is proposed for the traditional ant algorithm easily appears precocious and stagnation behavior phenomenon in this paper. And the various parameter of ant colony algorithm is adjusted. Selecting a typical TSP instance to experiment, the results are indicated that the new modified ant colony algorithm has a better ability to search the global optimal solution and have better stability.