A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification

Hewahi, Nabil M.; Hamra, Enas Abu

doi:10.4018/jitr.2017070104

Cited by 11 publications

(15 citation statements)

References 19 publications

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“…Various techniques and methods have been tried to get rid of local minima, some of these are using metaheuristic techniques. Many researches have been working to adjust the neural networks weights based on methaheuristic techniques to improv the classification, some of these are based on GA and PSO as examples [12] [19]. In other cases, reserchers attempt to combine more than one metaheuristic approach to utilize the capabilities of each one of them, therefore, trying to obtain better classification accuracy than using one technique alone [12,13].…”

Section: Metaheuristic Approaches For Annmentioning

confidence: 99%

“…Many researches have been working to adjust the neural networks weights based on methaheuristic techniques to improv the classification, some of these are based on GA and PSO as examples [12] [19]. In other cases, reserchers attempt to combine more than one metaheuristic approach to utilize the capabilities of each one of them, therefore, trying to obtain better classification accuracy than using one technique alone [12,13]. In this paper we select PSO as a well defined metaheutstic technique that has been used in improving the neural network classification as a technique to be extended by imaging techniques to measure the effectiveness of those imaging techniques in improving the neural network classification…”

Section: Metaheuristic Approaches For Annmentioning

confidence: 99%

“…Many researchers work to either propose new metaheuristic techniques inspired from biology or develop systems that combine various metaheuristic techniques. Proposing new techniques such as Genetic Algorithm (GA) [11], Simulated Annealing (SA) [17], ant colony [6,7], bat algorithm [31], PSO and fish swarm [16] [26], and Combining more than one technique such as PSO and GA, GA and SA, or PSO and SA [4] [12,13] [27] [31]. In both the cases, proposing new metaheuristic or combine more than one technique, the target is to better explore the search space and achieve better results (near-optimum).…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Effect of Imaging Techniques Extension to PSO on Neural Networks

Abbas¹,

Hewahi²

2020

IJIGSP

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In this paper we go through some very recent imaging techniques that are inspired from space exploration. The advantages of these techniques are to help in searching space. To explore the effectiveness of these imaging techniques on search spaces, we consider the Particle Swarm Optimization algorithm and extend it using the imaging techniques to train multiple neural networks using several datasets for the purpose of classification. The techniques were used during the population initialization stage and during the main search. The performance of the techniques has been measured based on various experiments, these techniques have been evaluated against each other, and against the particle swarm optimization algorithm alone taking into account the classification accuracy and training runtime. The results show that the use of imaging techniques produces better results.

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Section: Metaheuristic Approaches For Annmentioning

confidence: 99%

Section: Metaheuristic Approaches For Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the Effect of Imaging Techniques Extension to PSO on Neural Networks

Abbas¹,

Hewahi²

2020

IJIGSP

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“…The newly proposed algorithm has shown clear performance benefits over PSO-RBF in training speed, convergence accuracy and diagnosis accuracy. In 2017, Hewahi and Abu Hamra [14] produced a new algorithm that applies the BP algorithm on the ANN, then improves the results by applying the GA followed by the PSO algorithm and so on in a loop. They started by taking n number of ANN best results and putting them in one set, and then the loop of GA followed by PSO starts.…”

Section: Related Workmentioning

confidence: 99%

A Biology Inspired Algorithm to Mitigate the Local Minima Problem and Improve the Classification in Neural Networks

al.¹

2019

IJCDS

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ANN is a very well-known approach used for classification based on supervised machine learning. This approach faces some issues, notably the local minima problem, which leads to diminished accuracy in the results. To solve the problem of local minima, a new algorithm called RPSOGAC has been proposed. The proposed algorithm combines the strengths of both optimization algorithms PSO and GA to improve the classification accuracy of ANNs. RPSOGAC starts by finding the best weights that lead to the best ANN classification result using the backpropagation algorithm and adds these weights to the initial population. The other individuals of the population are randomly generated. Based on randomness, the algorithm reciprocally and continually switches between applying the GA and PSO algorithms until it reaches to the best solution. Two major differences between RPSOGAC and other previous algorithms, firstly is the random selection of GA and PSO, which gives equal opportunities for them to improve the classification. Secondly, during PSO, a competition between two population sets are performed to come up with a new population having the best individuals, this gives a chance for expected to improve individuals to enhance in the future if possible. Various experiments on six different datasets related to four domains have been conducted to show the classification accuracy of RPSOGAC. Also, a comparative study has been performed to compare the accuracy performance of classification between RPSOGAC and other algorithms. The obtained results show that RPSOGAC outperforms other approaches in four datasets and in the other two the results are very close.

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“…Recently, artificial intelligent techniques, such as Genetic Algorithms (GA) (Mor & Gupta, 2014;Mjahed, 2006), Particle Swarm Optimization (PSO) (Rini et al, 2011;Jena et al, 2015), Artificial Bee Colony Algorithm (Chen & Xiao, 2019), Fuzzy Logic (Raj & Murali, 2013;Xiao et al, 2013) and Artificial Neural Networks (Chandra et al, 2013;Devi & Kumar, 2014) have been applied successfully to automatic detection and to diagnosis. GA and PSO algorithms have been effectively used to select the attributes of interest (Karimova et al, 2004) and for pattern detection and classification tasks (Hewahi, 2017;Mjahed, 2010).…”

Section: Introductionmentioning

confidence: 99%

Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

Mjahed¹,

Hadaj²,

Bouzaachane³

et al. 2020

STUD INFORM CONTROL

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This paper presents an improvement in the recognition of faulty signals, encountered in the case of the Gazelle helicopter's main rotor, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. The main focus is on the distinction between faulty and healthy signals and then between the three subclasses of faulty signals, i.e. faulty bearings, joints problem and mechanical loosening. This research work is divided into three parts. The first part approaches the two above-mentioned classes of signals at the same time, and, to this purpose, the Linear Discriminant Analysis (LDA), Non Linear Discriminant Analysis (NLDA) and Back-propagation Neural Network (BPNN) are used. In the second and third part of the paper, GA and PSO are employed for optimizing the hyperplanes and hypersurfaces which separate the above-mentioned classes of signals, as well as the architecture and connection weights of a neural network (NN). Real data are used, which correspond to the vibration signals measured during periodic technical inspections, and are characterized by amplitudes and frequencies typical of the eight highest peaks of the Welch spectrum. The results obtained confirm the validity of the above-mentioned approaches and comparable favorably with those of other multivariate methods. The GA-or PSO-based neural networks` diagnosis can therefore be established for helicopter computers so that faults can be detected.

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A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification

Cited by 11 publications

References 19 publications

Exploring the Effect of Imaging Techniques Extension to PSO on Neural Networks

Exploring the Effect of Imaging Techniques Extension to PSO on Neural Networks

A Biology Inspired Algorithm to Mitigate the Local Minima Problem and Improve the Classification in Neural Networks

Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

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