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 complaints of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers offered have been conscientiously reviewed and chosen from 298 submissions. The papers are equipped in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, purposes of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, man made immune method, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy tools, and hybrid algorithms - for half I. subject matters addressed partly II are equivalent to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent platforms, facts mining equipment, computing device studying tools, function choice algorithms, trend popularity equipment, clever regulate, different optimization algorithms and purposes, facts fusion and swarm intelligence, in addition to fish institution seek - foundations and applications.
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Additional resources for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I
6%. 3%. So, the results for the negative class are higher than the results for the positive class (phosphorylated), although there were the same numbers of each class in our trials. 6 Conclusions Particle swarm optimization (PSO) is an evolutionary computational technique used for optimization motivated by the social behavior of individuals in large groups in nature. Diﬀerent approaches have been used to understand how this algorithm works and trying to improve its convergence properties for diﬀerent kind of problems.
Xi (t + 1) = f[zi (t)] + ei (t) (6) where e(t) is an array with the model prediction errors for each coordinate. The embedding z(t) of a particle i should consider enough information from the nearest neighbors, since they have a direct inﬂuence in its motion. To deﬁne our embedding zi (t), we can consider the average change in position Δxi (t) of i and its neighbors, since it gives enough information about the magnitude and direction of velocity vi (t), which actually is enough to model the Vicsek data (see equations 1-3).
7. Global (a) and local (b) separation measures for the high density case of the modiﬁed Vicsek model and both local interactions qualitatively arrive at a steady state with a very low deviation. Figure 8 shows an example of how the separation of a population into two swarms is emulated very well qualitatively in a simulation. Fig. 8. Simulation snapshots of the modiﬁed Vicsek model and the retrieved model, for a high density case with the same initial conditions 7 Conclusions The results show that automated mathematical modeling from time series data using radial basis functions can produce discrete time models capable of emulating to a certain extent the collective behavior of the classical Vicsek model.