Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bjorklund, S., Ljung, L.: A Review of Time-Delay Estimation Techniques. In: IEEE Conference on Decision and Control 2003, Maui, USA, vol. 3, pp. 2502–2507 (2003)
Boukas, E.K.: Stochastic output feedback of uncertain time-delay system with saturing actuators. J. of Optimization Theory and Applications 118(2), 255–273 (2003)
Chen, X., Wang, M.: Global optimization methods for time delay estimation. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 1, pp. 212–215 (2004)
Chen, B.-S., Hung, J.-C.: A global estimation for multichannel time-delay and signal parameters via genetic algorithm. Signal Processing 81(5), 1061–1067 (2001)
Harada, K., Kobayashi, Y., Okita, T.: Identification of Linear Systems With Time Delay and Unknown Order Electrical. Engineering in Japan (English translation of Denki Gakkai Ronbunshi) 145(3), 61–68 (2003)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a colony of cooperating agents. IEEE Trans. on SMC-Part B 26(1), 29–41 (1996)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Grassé, P.P.: La reconstruction du nid et les coordinations inter-individuelles chez bellicositermes natalensis et cubitermes sp. La thorie de la stigmergie: Essai dinterprtation des termites constructeurs. Insectes Sociaux 6, 41–81 (1959)
Dorigo, M.: Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992)
Bullnheimer, B., Hartl, R.F., Strauss, C.: Applying the Ant System to the Vehicle Routing Problem. In: Voss, S., et al. (eds.) Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer (1999)
Costa, D., Hertz, A.: Ants can color graphs. J. Oper. Res. Soc. 48, 295–305 (1997)
Merkle, D., Middendorf, M., Schmeck, H.: Ant colony optimization for resource-constrained project scheduling. IEEE Trans. Evol. Comput. 6(4), 333–346 (2002)
Reimann, M., Doerner, K., Hartl, R.F.: D-ants: Savings based ants divide and conquer the vehicle routing problems. Comput. Oper. Res. 31(4), 563–591 (2004)
Bilchev, B., Parmee, I.C.: The Ant Colony Metaphor for Searching Continuous Design Spaces. In: Fogarty, T.C. (ed.) AISB-WS 1995. LNCS, vol. 993, pp. 25–39. Springer, Heidelberg (1995)
Monmarché, N., Venturini, G., Slimane, M.: On how pachycondyla apicalis ants suggest a new search algorithm. Future Generation Comput. Syst. 16, 937–946 (2000)
Dréo, J., Siarry, P.: A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 216–221. Springer, Heidelberg (2002)
Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European J. of Operat.Research 185, 1155–1173 (2006)
Koroec, P.: Stigmergy as an approach to metaheuristic optimization, Ph.D.Thesis, Joef Stefan International Postgraduate School, Ljubljana, Slovenia
Olinsky, A.D., Quinn, J.T., Mangiameli, P.M., Chen, S.K.: A genetic algorithm approach to nonlinear least squares estimation. Int. J. Math. Educ. SCI. Tehnol. 35(2), 207–217 (2004)
Bharth, B., Borkar, V.S.: Stochastic approximation algorithms: overview and resent trends. Sadhana India 24(Parts 4 & 5), 425–452 (1999)
Iemura, H., Yang, Z., Kanae, S., Wada, K.: Identification of continous-time systems with unknow time delays by global nonlinear least-squares method. In: IFAC Workshop on Adaptation and Lerning in Control and Signal Processing, Yokohama, Japan (2004)
Papliński, J.P.: Hybrid genetic and Nelder-Mead algorithms for identification of time delay. In: 14th IEEE IFAC International Conference Methods and Models in Automation and Robotics, Midzyzdroje, Poland (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Papliński, J. (2012). Continuous Ant Colony Optimization for Identification of Time Delays in the Linear Plant. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-29353-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29352-8
Online ISBN: 978-3-642-29353-5
eBook Packages: Computer ScienceComputer Science (R0)