Cooperative learning in neural networks using particle swarm optimizers

By Frans van den Bergh, Andries Petrus Engelbrecht. S. Afr. Comput. J., 2000

383 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aec1538916f6a6ec3a30ea6b8e84d33a9bb741a2

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Citations in the corpus

A discrete version of particle swarm optimization for flowshop scheduling problems (2007, methodology): “PSO has been applied successfully to continuous nonlinear functions [1], neural networks [3], nonlinear constrained optimization problems [4], etc.”
Time-series prediction using a local linear wavelet neural network (2006, unknown): “Some example applications include neural network training [7,28, 29 ,6] and function minimization [23,24].”
An analysis of particle swarm optimizers (2002, methodology): “It can be used to solve a wide array of different optimisation problems, including most of the problems that can be solved using Genetic Algorithms; some example applications include neural network training [41, 135, 136, 34] and function minimization [121, 124].”
A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem (2007, methodology): “Since PSO was first introduced to optimize various continuous nonlinear functions by Kennedy and Eberhart [8,20], it has been successfully applied to a wide range of applications such as power and voltage control [58,1], neural network training [52], mass-spring system [5], task assignment [42], sup”
A Cooperative approach to particle swarm optimization (2004, methodology): “This measure thus provides a good indication of the relative ranking of the algorithms when using PSOs to train neural networks [8][9], where the cost of a single function evaluation is”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “Other work using PSO in collaboration with ANNs has been carried out by Ismail and Engelbrecht (1999), van den Bergh and Engelbrecht (1999, 2000), Voss and Feng (2001, 2002, 2003), in combination with a Group Method Data Handling ANN (psychological-social metaphor only), Mendes et al. (2002), Xiao…”
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks (2007, methodology): “In order to overcome the disadvantages of gradient based algorithms, many global optimization methods have been proposed for training feed-forward neural networks such as Genetic Algorithms [4,5,6,7,8,9,10,11,12,13,14,15,16,17], The Particle Swarm Optimization algorithm [18, 19, 20, 22, 21, 23], Dif”