Advances in Swarm Intelligence: Second International by Juan Luis Fernández-Martínez, Esperanza García-Gonzalo,

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 foreign convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers awarded have been rigorously reviewed and chosen from 298 submissions. The papers are equipped 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 method, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy equipment, and hybrid algorithms - for half I. subject matters addressed partly II are reminiscent of multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent platforms, facts mining equipment, laptop studying tools, function choice algorithms, development acceptance tools, 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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Extra resources for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I

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1 Example 1: Sphere Function with Difficulty Index of 1 f 1 = n ∑ x i =1 2 i . , n , n is the dimensionality or number of vai riables to be optimized. The objective is to find the minimum of f1 function and the Enforced Mutation to Enhancing the Capability of PSO Algorithms 31 related variable locations. 1 shows the surface plot of the sphere function in 2 variables. There is a unique minimum point in the figure with location [0, 0]. Population size is 600. Parameters for PSO and enhanced mechanism (EMPSO) for this simple problem are (Algorithm #1) (1).

The PSO family: deduction, stochastic analysis and comparison. Swarm Int. 3, 245–273 (2009) 4. : Two algorithms of the extended PSO family. In: International Conference on Evolutionary Computation, ICEC 2010, Valencia, Spain, pp. 237–242 (October 2010) 5. : PSO: A powerful algorithm to solve geophysical inverse problems. Applications to a 1D-Dc resistivity case. J. of Appl. Geophys. 71(1), 13–25 (2010) 6. : Theoretical analysis of particle swarm trajectories through a mechanical analogy. Int.

2008, Article ID 861275, 15 (2008) 3. : The PSO family: deduction, stochastic analysis and comparison. Swarm Int. 3, 245–273 (2009) 4. : Two algorithms of the extended PSO family. In: International Conference on Evolutionary Computation, ICEC 2010, Valencia, Spain, pp. 237–242 (October 2010) 5. : PSO: A powerful algorithm to solve geophysical inverse problems. Applications to a 1D-Dc resistivity case. J. of Appl. Geophys. 71(1), 13–25 (2010) 6. : Theoretical analysis of particle swarm trajectories through a mechanical analogy.

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