Population declining ant colony optimization algorithm and its applications

  • Authors:

  • Zhilu Wu;Nan Zhao;Guanghui Ren;Taifan Quan

  • Affiliations:

  • School of Electronics and Information Technology, Harbin Institute of Technology 342#, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology 342#, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology 342#, Harbin, Heilongjiang 150001, China;School of Electronics and Information Technology, Harbin Institute of Technology 342#, Harbin, Heilongjiang 150001, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Population declining ant colony optimization (PDACO) algorithm is proposed and 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 stops searching early. PDACO can enlarge searching range through increasing the initial population of the ant colony, and the population declines in successive iterations. So, the performance of PDACO is superior with the same computational complexity. PDACO is applied to TSP and multiuser detection. Via computer simulations it is shown that PDACO has better performance in solving these two problems than ACO algorithms.