Titlebook: Advances in Swarm Intelligence; First International Ying Tan,Yuhui Shi,Kay Chen Tan Conference proceedings 2010 The Editor(s) (if applicab

essential-fats

Paolo Cattorini,Roberto Mordacciization (PSO) to construct a two-population PSO model called PSOPB, composed of the host and the parasites population. In this model, the two populations exchange particles according to the fitness sorted in a certain number of iterations. In order to embody the law of "survival of the fittest" in b

bronchiole

Tractable

滔滔不絕地講

https://doi.org/10.1007/978-94-015-8344-2sed algorithm, the social part and recognition part of PSO both are modified in order to accelerate the convergence and improve the accuracy of the optimal solution. Especially, a novel recognition approach, called general recognition, is presented to furthermore improve the performance of PSO. Expe

Cryptic

Archipelago

Resign

Biomechanics Modeling and Concepts,d are not continuously available for computation, achieving a better make-span is a key issue. The existing algorithm SSAC has proved to be a good trade-off between availability and responsiveness while maintaining a good performance in the average response time of multiclass tasks. But the makespan

stratum-corneum

JEER

https://doi.org/10.1007/978-3-319-15096-3e Swarm Optimization (PSO). QPSO performs better than normal PSO on several benchmark problems. However, QPSO’s quantum bit(Qubit) is still in Hilbert space’s unit circle with only one variable, so the quantum properties have been undermined to a large extent. In this paper, the Bloch Sphere encodin

深陷

S. M. Niaz Arifin,Gregory R. Madeyproved particle swarm optimization (PSO) algorithm. To enhance the exploitation ability of PSO, a stochastic iterated local search is incorporated. To improve the exploration ability of PSO, a population update method is applied to replace non-promising particles. In addition, a solution pool that s