2019
DOI: 10.1007/s00500-019-04416-2
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A novel hybrid metaheuristic optimization method: hypercube natural aggregation algorithm

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Cited by 5 publications
(7 citation statements)
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References 31 publications
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“…The HYNAA optimizer is the improvised form of natural aggregation algorithm (NAA) proposed by Luo et al (2016), in which ample balance between the exploitation and exploration mechanisms of NAA were proposed by Oscar Maciel 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; Tunay et al, 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.…”
Section: Methodologies Adopted and Proposed Strategymentioning
confidence: 99%
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“…The HYNAA optimizer is the improvised form of natural aggregation algorithm (NAA) proposed by Luo et al (2016), in which ample balance between the exploitation and exploration mechanisms of NAA were proposed by Oscar Maciel 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; Tunay et al, 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.…”
Section: Methodologies Adopted and Proposed Strategymentioning
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; Tunay et al, 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.…”
Section: Overviewmentioning
confidence: 99%
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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%