By Juan Luis Fernández-Martínez, Esperanza García-Gonzalo, Saras Saraswathi (auth.), Ying Tan, Yuhui Shi, Yi Chai, Guoyin Wang (eds.)
The two-volume set (LNCS 6728 and 6729) constitutes the refereed lawsuits of the foreign convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers provided have been conscientiously reviewed and chosen from 298 submissions. The papers are prepared in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, functions of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, synthetic immune procedure, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy equipment, and hybrid algorithms - for half I. issues addressed partly II are resembling multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent structures, facts mining equipment, laptop studying equipment, function choice algorithms, trend reputation tools, clever keep watch over, different optimization algorithms and functions, information fusion and swarm intelligence, in addition to fish institution seek - foundations and applications.
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Extra resources for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I
2 Modeling and Embedding for Swarming For the particular case of swarm modeling, we want to use the same model for each particle just like in the Vicsek model. Since we are working with positions in 2D space, the position of a particle depends on two components, which means we require two diﬀerent functions for a single model. Therefore the single model that all particles follow can be deﬁned as: f[z(t)] = f1 (z(t)) f2 (z(t)) (5) To simplify the model, instead of trying to predict the absolute position xi at t + 1, we can substitute equation 4 by predicting the relative change in position Δxi (t + 1) = xi (t + 1) − xi (t).
Collective Construction with Multiple Robots. In: Proceedings of 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2696–2701 (2002) 3. : Building Patterned Structures with Robot Swarms. , Zhu, S. ) 19th International Joint Conference on Artificial Intelligence, IJCAI 2005, pp. 1495–1502 (2005) 4. : Robot Search in 3D Swarm Construction. In: 1st. International Conference on Self-Adaptive and Self-Organizing Systems, pp. 363–366 (2007) 5. : Three-Dimensional Construction with Mobile Robots and Modular Blocks.
This paper forms part of the research that focuses on the identification of different object shapes. Y. Tan et al. ): ICSI 2011, Part I, LNCS 6728, pp. 19–27, 2011. © Springer-Verlag Berlin Heidelberg 2011 20 D. King and P. Breedon In  and  each robot or agent in the swarm could individually assess and identify any object that required moving cooperatively. What if an individual agent cannot identify the object alone, due to similarities in different components? There would then be a need for cooperative sensing, where agents share partial knowledge of the components.