Continuous Ant Colony Optimization for Identification of Time Delays in the Linear Plant

Interpolated Ant Colony Optimization (IACO) for a continuous domain was proposed in the paper. The IACO uses the same mechanisms as the classical ACO applied to discrete optimization. The continuous search space is sampled by individuals on the basis of the linear interpolated trace of the pheromone. It allows to obtain a simple and efficient optimization algorithm. The proposed algorithm is then used to identify delays in linear dynamic systems. The examination results show that it is an effective tool for global optimization problems.

[1]  Ian C. Parmee,et al.  The Ant Colony Metaphor for Searching Continuous Design Spaces , 1995, Evolutionary Computing, AISB Workshop.

[2]  P.-P. Grasse.  La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs , 1959, Insectes Sociaux.

[3]  Richard F. Hartl,et al.  D-Ants: Savings Based Ants divide and conquer the vehicle routing problem , 2004, Comput. Oper. Res..

[4]  Janusz P. Paplinski.  Hybrid genetic and Nelder-Mead algorithms for identification of time delays , 2009 .

[5]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[6]  Nicolas Monmarché,et al.  On how Pachycondyla apicalis ants suggest a new search algorithm , 2000, Future Gener. Comput. Syst..

[7]  Johann Dréo,et al.  A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions , 2002, Ant Algorithms.

[8]  E. Boukas.  Stochastic Output Feedback of Uncertain Time-Delay Systems with Saturating Actuators , 2003, 2003 4th International Conference on Control and Automation Proceedings.

[9]  Bor-Sen Chen,et al.  A global estimation for multichannel time-delay and signal parameters via genetic algorithm , 2001, Signal Process..

[10]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[11]  Alain Hertz,et al.  Ants can colour graphs , 1997 .

[12]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[14]  Alan Olinsky,et al.  A genetic algorithm approach to nonlinear least squares estimation , 2004 .

[15]  S. Bjorklund,et al.  A review of time-delay estimation techniques , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[16]  Richard F. Hartl,et al.  Applying the ANT System to the Vehicle Routing Problem , 1999 .

[17]  Hartmut Schmeck,et al.  Ant colony optimization for resource-constrained project scheduling , 2000, IEEE Trans. Evol. Comput..

[18]  X. Chen,et al.  Global optimization methods for time delay estimation , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[19]  Kiyoshi Wada,et al.  Identification of continuous-time systems with unknown time delays by global nonlinear least-squares method , 2004 .

[20]  Silvano Martello,et al.  Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization , 2012 .

[21]  Subbarao Kambhampati,et al.  Evolutionary Computing , 1997, Lecture Notes in Computer Science.

[22]  Vivek S. Borkar,et al.  Stochastic approximation algorithms: Overview and recent trends , 1999 .