Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Journal of Global Optimization
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Parallel Optimization of Evolutionary Algorithms
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Neural Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
Real-time particle swarm optimization based current harmonic cancellation
Engineering Applications of Artificial Intelligence
Meta-learning based optimization of metabolic pathway data-mining inference system
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Particle swarm optimization with disagreements
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems
Expert Systems with Applications: An International Journal
PSO for reservoir computing optimization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Expert Systems with Applications: An International Journal
Is the meta-EA a viable optimization method?
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Improving e-learning communities through optimal composition of multidisciplinary learning groups
Computers in Human Behavior
Review: A parameter selection strategy for particle swarm optimization based on particle positions
Expert Systems with Applications: An International Journal
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The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, whereas previous approaches to meta-optimization have tuned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimizing a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases.