Varying the Population Size of Artificial Foraging
Swarms on Time Varying Landscapes
56. Carlos Fernandes,
Vitorino Ramos, Agostinho C. Rosa, Varying the Population Size of
Artificial Foraging Swarms on Time Varying Landscapes, in W. Duch, J.
Kacprzyk, E. Oja, S. Zadrozny (Eds.), Artificial
Neural Networks:
Biological Inspirations, Proc. ICANN´05: 15th Int. Conf.,
Warsaw,
Poland, LNCS series, Vol. 3696, Part I, pp. 311-316, Springer-Verlag,
Sept. 2005.
Figure - A self-organized swarm emerging a characteristic flocking migration behaviour between one deep valley (South region) and one peak (North region), surpassing in intermediate steps (Mickey Mouse shape) some local optima. Over each foraging step, the population self-regulates.
PDF file: paper (1063 Kb)
Abstract: Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with wich it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to Self-Organization, Stigmergy and Social Foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will adress the collective adaptation of a social community to a cultural (environmenatl, contextual) or media informational dynamical landscape, represented here - for the purpose of different experiments - by several three-dimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes.
º Laura Lanzarini, Victoria Leza, Armando De Giusti, "Particle Swarm Optimization with Variable Population Size", L. Rutkowski, R. Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (Eds.): 9th International Conference on Artificial Intelligence and Soft Computing - ICAISC08, Zakopane, Poland, LNCS Vol. 5097, pp. 438-449, Springer, June 2008.
º Wang Guang-hui, Zeng Jian-chao, "Particle Swarm Optimization algorithm with Varying Population Size", in Computer Engineering and Applications Journal, Vol.44, n.11, pp. 52-56, 2008.
º Mao Li, Jia Heng-song, Bian Feng, "Automatic Classification of Images based on Classification Ant Colony Algorithm", in Computer and Applications Journal, ISSN 1002-8331, Vol. 181, pp. 68-70, China, Sept. 2007.
º Sorin Cristian Cheran, "Artificial Life models in 3D worlds: Virtual Ant Colonies for the Reconstruction of the Bronchial and Vascular Trees and the Pleura in Lung Computed Tomography (CT)", PhD Thesis, Università degli Studi di Torino, Torino, Italy, 2007.
º W.J. Tang, Q.H Wu, J.R. Saunders, "Bacterial Foraging Algorithm For Dynamic Environments", in CEC 2006 - IEEE Congress on Evolutionary Computation, pp. 1324-1330, July 2006.
º Yan Chen-yang, Zhang You-peng, Xiong Wei-qing , "Artificial Ant Colony Based on Grayscale Grads Perception on Digital Image Edge Detection", in Journal of Computer Engineering and Applications, Vol.42, No.36, pp.23-27, 2006.
º Marcel van Velden; "ManetLoc: A Location based Approach to Distributed World-Knowledge in Mobile Ad-Hoc Networks", Master Thesis, Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Department Mediamatica / Man-Machine Interaction, Netherlands, April 2005.
º Neal Richter, "Natural Computation", Artificial Intelligence course (CS 436), Montana State Univ., Montana, USA, 2007.
Related Works:
Figure - A self-organized swarm emerging a characteristic flocking migration behaviour between one deep valley (South region) and one peak (North region), surpassing in intermediate steps (Mickey Mouse shape) some local optima. Over each foraging step, the population self-regulates.
PDF file: paper (1063 Kb)
Abstract: Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with wich it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to Self-Organization, Stigmergy and Social Foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will adress the collective adaptation of a social community to a cultural (environmenatl, contextual) or media informational dynamical landscape, represented here - for the purpose of different experiments - by several three-dimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes.
Keywords: Dynamic Optimization, Stigmergy, Swarm Intelligence and Perception, Social Cognitive Maps, Social Foraging, Self-Organization, Distributed Search and Optimization.
Cited by:º Laura Lanzarini, Victoria Leza, Armando De Giusti, "Particle Swarm Optimization with Variable Population Size", L. Rutkowski, R. Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (Eds.): 9th International Conference on Artificial Intelligence and Soft Computing - ICAISC08, Zakopane, Poland, LNCS Vol. 5097, pp. 438-449, Springer, June 2008.
º Wang Guang-hui, Zeng Jian-chao, "Particle Swarm Optimization algorithm with Varying Population Size", in Computer Engineering and Applications Journal, Vol.44, n.11, pp. 52-56, 2008.
º Mao Li, Jia Heng-song, Bian Feng, "Automatic Classification of Images based on Classification Ant Colony Algorithm", in Computer and Applications Journal, ISSN 1002-8331, Vol. 181, pp. 68-70, China, Sept. 2007.
º Sorin Cristian Cheran, "Artificial Life models in 3D worlds: Virtual Ant Colonies for the Reconstruction of the Bronchial and Vascular Trees and the Pleura in Lung Computed Tomography (CT)", PhD Thesis, Università degli Studi di Torino, Torino, Italy, 2007.
º W.J. Tang, Q.H Wu, J.R. Saunders, "Bacterial Foraging Algorithm For Dynamic Environments", in CEC 2006 - IEEE Congress on Evolutionary Computation, pp. 1324-1330, July 2006.
º Yan Chen-yang, Zhang You-peng, Xiong Wei-qing , "Artificial Ant Colony Based on Grayscale Grads Perception on Digital Image Edge Detection", in Journal of Computer Engineering and Applications, Vol.42, No.36, pp.23-27, 2006.
º Marcel van Velden; "ManetLoc: A Location based Approach to Distributed World-Knowledge in Mobile Ad-Hoc Networks", Master Thesis, Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Department Mediamatica / Man-Machine Interaction, Netherlands, April 2005.
º Neal Richter, "Natural Computation", Artificial Intelligence course (CS 436), Montana State Univ., Montana, USA, 2007.
Related Works:
69. Computational Chemotaxis in Ants and Bacteria over Dynamic Environments.
63. Social
Cognitive Maps, Swarm
Collective Perception and Distributed Search on Dynamic Landscapes.
61. On
Self-Regulated Swarms, Societal
Memory, Speed and Dynamics.