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Ant Colony Optimization: Principle, Convergence and Application

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

Abstract

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.

Keywords

Flight Simulator Simulation Experimental Result Pheromone Amount Integrate Time Absolute Error Parameter Tuning Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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