A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection

Zouache, Djaafar; Abdelaziz, Fouad Ben

doi:10.1016/j.cie.2017.10.025

Cited by 37 publications

(8 citation statements)

References 34 publications

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“…Based on the observation and analysis of the convergence graph, it is visible that the proposed enhancement on the BSO algorithm results in faster convergence. Finally, it is worth mentioning that we have also tested the proposed eBSO approach on other UCI and Keggle datasets as well, and compared the results to several other recent research papers published in the state-of-the-art journals that used the same datasets, including [32,33]. The obtained results are encouraging and indicate that the eBSO method could prove to be the superior optimizer for the feature selection problem.…”

Section: Simulation Results and Discussionmentioning

confidence: 64%

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Bačanin¹,

Alhazmi²,

Živković³

et al. 2022

Computers, Materials &Amp; Continua

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In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing gradient issue. As an alternative approach, stochastic optimization algorithms, such as nature-inspired metaheuristics are more reliable for complex optimization tax, such as finding the proper values of weights and biases for neural network training. In this work, we propose an enhanced brain storm optimization-based algorithm for training neural networks. In the simulations, ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm. The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed, due to the capability of balancing the intensification and diversification and avoiding the local minima. The proposed approach obtained the best accuracy on eight out of ten observed dataset, outperforming all other algorithms by 1-2% on average. When mean accuracy is observed, the proposed algorithm dominated on nine out of ten datasets.

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Section: Simulation Results and Discussionmentioning

confidence: 64%

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Bačanin¹,

Alhazmi²,

Živković³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Researchers in [22] proposed a hybrid swarm intelligence algorithm based on quantum computations and a combination of the firefly algorithm and PSO for feature selection. Quantum computations provided a good tradeoff between the intensification and diversification of search, while the combination of the firefly algorithm and PSO made it possible to efficiently investigate the generated subsets of features.…”

Section: Quantum Methodsmentioning

confidence: 99%

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

et al. 2019

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.

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“…Comparison of the algorithms showed that the BQI-GWO makes it possible to exclude a larger number of features, while increasing the classification accuracy. Researchers in Zouache and Ben Abdelaziz (2018) proposed a hybrid swarm intelligence algorithm based on quantum computations and a combination of the firefly algorithm and PSO for feature selection. Quantum computations provided a good tradeoff between the intensification and diversification of search, while the combination of the firefly algorithm and PSO made it possible to efficiently investigate the generated subsets of features.…”

Section: Feature Selectionmentioning

confidence: 99%

Binary PSO Variants for Feature Selection in Handwritten Signature Authentication

Hançer¹,

Bardamova²,

Hodashinsky³

et al. 2022

Informatica

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In this paper we propose modifications of the well-known algorithm of particle swarm optimization (PSO). These changes affect the mapping of the motion of particles from continuous space to binary space for searching in it, which is widely used to solve the problem of feature selection. The modified binary PSO variations were tested on the dataset SVC2004 dedicated to the problem of user authentication based on dynamic features of a handwritten signature. In the example of k-nearest neighbours (kNN), experiments were carried out to find the optimal subset of features. The search for the subset was considered as a multicriteria optimization problem, taking into account the accuracy of the model and the number of features.

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A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection

Cited by 37 publications

References 34 publications

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

Binary PSO Variants for Feature Selection in Handwritten Signature Authentication

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