A parallel ant colony algorithm on massively parallel processors and its convergence analysis for the travelling salesman problem

  • Authors:

  • Ling Chen;Hai-Ying Sun;Shu Wang

  • Affiliations:

  • Department of Computer Science and Engineering, Yangzhou University, Yangzhou 225009, China and State Key Lab of Novel Software Tech, Nanjing University, Nanjing 210093, China;Department of Computer Science and Engineering, Yangzhou University, Yangzhou 225009, China;Department of Computer Science and Engineering, Yangzhou University, Yangzhou 225009, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

An adaptive parallel ant colony optimisation (PACO) algorithm on massively parallel processors (MPPs) is presented. In the algorithm, we propose a strategy for information exchange between processors that makes each processor choose a partner to communicate with and update their pheromone adaptively. We also propose a method of adaptively adjusting the time interval for the exchange of information according to the diversity of the solutions, to increase the quality of the optimisation results and to avoid early convergence. The analysis and proof of the convergence of the PACO algorithm is presented. Experimental results of the TSP confirm our theoretical conclusions and show that our PACO algorithm has a high convergence speed, high speedup and high efficiency.