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GECCO
2007
Springer
210views Optimization» more  GECCO 2007»
14 years 1 months ago
Markov chain models of bare-bones particle swarm optimizers
We apply a novel theoretical approach to better understand the behaviour of different types of bare-bones PSOs. It avoids many common but unrealistic assumptions often used in an...
Riccardo Poli, William B. Langdon
CEC
2007
IEEE
13 years 7 months ago
Entropy-based Memetic Particle Swarm Optimization for computing periodic orbits of nonlinear mappings
— The computation of periodic orbits of nonlinear mappings is very important for studying and better understanding the dynamics of complex systems. Evolutionary algorithms have s...
Y. G. Petalas, Konstantinos E. Parsopoulos, Michae...
AMC
2007
129views more  AMC 2007»
13 years 7 months ago
Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization
In a distributed computing system (DCS), we need to allocate a number of modules to different processors for execution. It is desired to maximize the processor synergism in order...
Peng-Yeng Yin, Shiuh-Sheng Yu, Pei-Pei Wang, Yi-Te...
CEC
2008
IEEE
14 years 2 months ago
Improved Particle Swarm Optimization with low-discrepancy sequences
— Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequenc...
Millie Pant, Radha Thangaraj, Crina Grosan, Ajith ...
BMCBI
2006
146views more  BMCBI 2006»
13 years 7 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...