Harmony Search Based Supervised Training of Artificial Neural Networks

Kattan, Ali; Abdullah, Rosni; Salam, Rosalina Abdul

doi:10.1109/isms.2010.31

Cited by 57 publications

(21 citation statements)

References 19 publications

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“…In addition, the proposed MMPSO algorithm is suitable for parallel implementation and the runtime of the MMPSO algorithm can be reduced to a much shorter time with parallel programming. Finally, the performance of the proposed MMPSO algorithm is compared with the performance reported in the literature for the HSA [16], KHA, GA [15], and the fireworks algorithm (FWA) [17] which split the data into 80% training and 20% testing, for six datasets. In order to make this comparison under the same conditions, six datasets are split into 80% training and 20% testing for this experiment.…”

Section: Resultsmentioning

confidence: 99%

“…Bolaji et al used the fireworks optimization algorithm (FWA) for ANN training and performed the experimental tests with UCI datasets. The experimental results were compared to the results obtained from the krill herd algorithm (KHA) [15], harmony search algorithm (HSA), and GA [16]. According to the experimental results, the FWA algorithm showed better classification performance [17].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Hacıbeyoğlu

Ibrahim

2018

Scientific Programming

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Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feedforward artificial neural networks training.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Hacıbeyoğlu

Ibrahim

2018

Scientific Programming

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“…Furthermore, some other evolutionary training algorithms can be integrated into the supervised learning part to train the network [24,25] which would overcome some known shortages of the backpropagation training method. Moreover, the smartphone application could incorporate built-in preprocessing capabilities and the ability to connect to a cloud service that would help the municipals to take proper action to repair roads suffering from hazards as soon as possible.…”

Section: Resultsmentioning

confidence: 99%

Detection of Motorway Disorders by Processing and Classification of Smartphone Signals Using Artificial Neural Networks

Salih

Kattan

Cevik

2016

International Journal of Natural Sciences Research

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“…Similarly, considering the efficiency of HS algorithm-that has a slow convergence rate, but guarantees a near-optimum solution [117]-many researchers applied HS for optimizing weight vector of the FNNs [175,176]. Moreover, the efficiency of HS comes from using m many harmonies (weight vectors), and iteratively improvising each harmony by computing new harmony (new solution vectors) using heuristic inspired by music pitch modification [117,177,178].…”

Section: Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

457

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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Harmony Search Based Supervised Training of Artificial Neural Networks

Cited by 57 publications

References 19 publications

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Detection of Motorway Disorders by Processing and Classification of Smartphone Signals Using Artificial Neural Networks

Metaheuristic design of feedforward neural networks: A review of two decades of research

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