Download e-book for iPad: Fireworks Algorithm: A Novel Swarm Intelligence by Ying Tan
By Ying Tan
This e-book is dedicated to the cutting-edge in all elements of fireworks set of rules (FWA), with specific emphasis at the effective greater types of FWA. It describes the main monstrous theoretical research together with uncomplicated precept and implementation of FWA and modeling and theoretical research of FWA. It covers exhaustively the main fresh major study into the advancements of FWA thus far. furthermore, the publication describes a couple of complex issues within the study of FWA, together with multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, numerous profitable purposes of FWA on non-negative matrix factorization (NMF), textual content clustering, development acceptance, and seismic inversion challenge, and swarm robotics, are illustrated in information, which would shed new mild on extra real-world functions in destiny. Addressing a multidisciplinary subject, it's going to entice researchers and pros within the parts of metahuristics, swarm intelligence, evolutionary computation, complicated optimization fixing, etc.
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Extra info for Fireworks Algorithm: A Novel Swarm Intelligence Optimization Method
Zhang, B. Zhang, Y. Zheng, A hybrid biogeography-based optimization and fireworks algorithm, in Advances in Swarm Intelligence (Springer, Berlin, 2014), pp. 1–7 45. J. McCaffrey, Fireworks algorithm optimization, MSDN Mag. 29(12). (2014). http://msdn. aspx 16 1 Introduction 46. Y-J. Zheng, Q. Song, S-Y. Chen, Multiobjective fireworks optimization for variable-rate fertilization in oil crop production. Appl. Soft Comput. 13(11), 4253–4263 (2013) 47. J. Zhang, On fireworks algorithm for solving 0/1 knapsack problem.
Flow chart of GES . . . . . . . . . . . . ..... 272 . . . . 273 279 281 281 ..... 287 ..... 289 291 . . . . . . 4 FWA with and without Gaussian mutation on function Sphere and Generalized Rosenbrock . . . . . . . Comparison of FWA with CPSO and SPSO (lower than 10−6 is treated as zero) . . . . . . . . . . . Random number generators tested. . . . . . . . RNG efficiency comparison under different batch size (# of random numbers per nanosecond).
9 Interleaving storage . . . . . . . . . . . . 10 Continuous storage. . . . . . . . . . . . 11 Speedup versus FWA . . . . . . . . . . . 12 Speedup versus PSO . . . . . . . . . . . 1 Scheme of very coarse NMF approximation with very low-rank k. Although k is significantly smaller than m and n, the typical structure of the original data matrix can be retained (note the three different groups of data objects in the left, middle, and right part of A) . . .
Fireworks Algorithm: A Novel Swarm Intelligence Optimization Method by Ying Tan