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 court cases of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers awarded 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 tools, and hybrid algorithms - for half I. themes addressed partially II are equivalent to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent structures, information mining equipment, computing device studying tools, characteristic choice algorithms, trend acceptance equipment, clever regulate, different optimization algorithms and functions, facts fusion and swarm intelligence, in addition to fish institution seek - foundations and applications.
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Additional info for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I
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. The introduced global and local separation measures over time, δg (t) and δl (t), were observed to be a very adequate representation of the behavior of the systems.
In: Del Bimbo, A. ) ICIAP 1997. LNCS, vol. 1310, pp. 709–716. Springer, Heidelberg (1997) 14. org/ 15. : Towards Cooperative Robotic Swarm Recognition: Object Classification and Validation. In: 3rd International Conference on Advanced Design Manufacture. Key Eng. Mat, vol. 450, pp. 320–324. tw Abstract. Particle Swarm Optimization (PSO), proposed by Professor Kennedy and Eberhart in 1995, attracts many attentions to solve for a lot of optimization problems nowadays. Due to its simplicity of setting-parameters and computational efficiency, it becomes one of the most popular algorithms in optimizations.
0035 Enforced Mutation to Enhancing the Capability of PSO Algorithms 33 (Algorithm #1) (1). 763, randxx=-10-4. (2). 0035. (3). One-variable enforced mutation is used. From Table 2, the EMPSO shows its good capability to find the optimal solution with so less generations for the high dimension Rastrigin functions with high success rates. From Fig. 2 there are a lot of local peaks and valleys for this Rastrigin function, this problem is a little bit difficult than sphere function. However, EMPSO can solve the problem as well with few failures.