Ant Colony Optimization: Principle, Convergence and Application

  • Haibin Duan
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 8)


Ant Colony Optimization (ACO) is a meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behaviour of real ant colonies. In this Chapter, we present a novel approach to the convergence proof that applies directly to the basic ACO model, and a kind of parameters tuning strategy for nonlinear PID(NLPID) controller using a grid-based ACO algorithm is also presented in detail. A series of simulation experimental results are provided to verify the performance the whole control system of the flight simulator with the grid-based ACO algorithm optimized NLPID.


Flight Simulator Simulation Experimental Result Pheromone Amount Integrate Time Absolute Error Parameter Tuning Strategy 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Haibin Duan
    • 1
  1. 1.School of Automation Science and Electrical EngineeringBeijing University of Aeronautics and AstronauticsBeijingP.R. China

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