Ant colony optimization algorithm with mutation mechanism and its applications

  • Authors:

  • Nan Zhao;Zhilu Wu;Yaqin Zhao;Taifan Quan

  • Affiliations:

  • School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

Quantified Score

Hi-index 12.07

Visualization

Abstract

Mutated ant colony optimization (MACO) algorithm is proposed by introducing the mutation mechanism to the ACO algorithm, and is applied to the traveling salesman problem (TSP) and multiuser detection in this paper. Ant colony optimization (ACO) algorithms have already successfully been used in combinatorial optimization, however, as the pheromone accumulates, we may not get a global optimum because it can get stuck in a local minimum resulting in a bad steady state. The presented MACO algorithm can enlarge searching range and avoid local minima by randomly changing one or more elements of the local best solution, which is the mutation operation in genetic algorithm. As the mutation operation is simple to implement, the performance of MACO is superior with almost the same computational complexity. MACO is applied to TSP and multiuser detection, and via computer simulations it is shown that MACO has much better performance in solving these two problems than ACO algorithms.