A novel hybrid metaheuristic optimization method: hypercube natural aggregation algorithm

Maciel, Oscar; Valdivia, Arturo; Oliva, Diego; Cuevas, Erik; Zaldívar, Daniel; Pérez‐Cisneros, Marco

doi:10.1007/s00500-019-04416-2

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…In this study, we tried to generate optimal number of synthetic samples by hybridizing the HYNAA with Siamese network. The key advantages of HNAA algorithm such as; highly effective and flexible clustering algorithm that is suitable for a wide range of applications, its scalability, accuracy, robustness, speed, and minimal user intervention (Luo et al, 2016;Maciel et al, 2020;Tunay & Abiyev, 2022; are explored for this work.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…The limitations of this algorithm such as; (a) getting trapped in the local optima and; (b) poor steadiness between exploitation and exploration of search space have been improvised by merging the HO algorithm (Luo et al, 2016). The searching capabilities of HYNAA have been enhanced by HO to create shelters in the aggregation process of the NAA and facilitate a deep search in high-dimensions (Maciel et al, 2020;Tunay & Abiyev, 2022;.…”

Section: Model Lossmentioning

confidence: 99%

“…In this study we made an attempt to propose and empirically develop a hybridized strategy by exploring a class of neural network architecture with two identical sub-networks using Siamese network with a population based optimizer, hypercube natural aggregation algorithm (HYNAA) (Luo et al, 2016;Maciel et al, 2020;Tunay & Abiyev, 2022; to generate samples for augmenting the minority class. The first phase of this experiment is carried out with SVM to collect the instances which are correctly predicted.…”

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confidence: 99%

“…a. Initialize the network, loss function and HYNAA optimizer; b. Input the first candidate solution of the pair through the network; c. Input the second candidate solution of the pair through the network; d. Compute the loss using the outputs of first and second pair; e. Back-propagate the computed loss to calculate the gradient of the proposed model; f. Update the weight using HYNAA optimizer; F I G U R E 5 Cluster view of original dataset, SMOTE, SMOTE-PSOEV and Siamese-HYNAA for Ecoli1 dataset.The HYNAA optimizer is the improvised form of natural aggregation algorithm (NAA) proposed byLuo et al (2016), in which ample balance between the exploitation and exploration mechanisms of NAA were proposed by OscarMaciel et al in 2019(Maciel et al, 2020 by intermixing the hypercubes optimization (HO) algorithm proposed by Abiye and Tunay in 2015(Tunay & Abiyev, 2022; which addressed the challenges of premature convergence and high-dimensionality. Basically, the NAA is inspired by the biological behaviour of the cockroach's aggregation behaviour of inter-individual attraction effect and crowding effect to find high quality resources in a limited resource environment.…”

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confidence: 99%

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An empirical hybridized Siamese network using hypercube natural aggregation algorithm for handling imbalance data learning

Rout

Mallick

Reddy³

et al. 2023

Expert Systems

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Dealing with imbalanced data is a common challenge in machine learning, where one class has significantly fewer examples than another. Successfully addressing this challenge requires careful consideration of the data, algorithm, and evaluation metrics to ensure that the model accurately predicts the minority class. In this study, we present a hybrid approach called Siamese‐HYNAA, which combines a Siamese network and a population‐based optimizer hypercube natural aggregation algorithm (HYNAA) to generate candidate solutions for augmenting the minority class. We collected 10 imbalanced datasets ranging from 1.81 to 8.78 imbalanced ratios and built solution pairs based on correctly predicted candidate solutions using support vector machine (SVM). We then fed these solutions to the Siamese network, which employs a one‐shot learning approach to improve predictions with fewer candidate solutions. However, we found that SVM predicted only a small number of minority class samples accurately, prompting us to optimize the number of candidate solution pairs using HYNAA to generate more synthetic samples for the Siamese network. We evaluated our proposed strategy against basic SMOTE and our previous work, SMOTE‐PSOEV, using various performance measures, including ROC‐AUC learning curves, sensitivity, specificity, accuracy, Characteristic stability index, balanced accuracy, F1‐score, informedness, markedness, and execution time. Our results indicate that Siamese‐HYNAA generates promising results for imbalanced data.

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Section: Literature Reviewsmentioning

confidence: 99%

Section: Model Lossmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An empirical hybridized Siamese network using hypercube natural aggregation algorithm for handling imbalance data learning

Rout

Mallick

Reddy³

et al. 2023

Expert Systems

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“…e purpose of this study is to introduce a new optimization technique, called the hypercube optimization search (HOS) algorithm, for training MLP to present an improved classification approach for medical data by optimizing the MLP's weights and biases parameters. e HOS is recommended for training MLP to overcome the aforementioned challenges due to its outstanding performance in escaping local optima and fast convergence speed [31,32]. Also, HOS have fewer parameters and is easy to use, simple in principle, and adaptable when compared to other swarmbased OAs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification

Tunay

Pashaei

2022

Computational Intelligence and Neuroscience

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The hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove’s movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS’s success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases.

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Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

et al. 2020

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A novel hybrid metaheuristic optimization method: hypercube natural aggregation algorithm

Cited by 5 publications

References 31 publications

An empirical hybridized Siamese network using hypercube natural aggregation algorithm for handling imbalance data learning

An empirical hybridized Siamese network using hypercube natural aggregation algorithm for handling imbalance data learning

Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

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