An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection

Gao, Yuanyuan; Zhou, Yongquan; Luo, Qifang

doi:10.1109/access.2020.3013617

Cited by 95 publications

(51 citation statements)

References 66 publications

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“…It has been observed from the literature that the performance of hyperbolic tangent V‐Shaped transfer function is better (Emary, Zawbaa, & Ella, 2016; Gao et al, 2020; Zhang et al, 2020). Therefore, in this article, hyperbolic tangent V‐shaped transfer function (Hussien et al, 2017) is used to modify Rao algorithms for feature selection.…”

Section: The Proposed Binary Version Of Rao Algorithmsmentioning

confidence: 99%

Classification of Parkinson disease using binary Rao optimization algorithms

2021

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Rao algorithms are recently proposed optimization algorithms used to solve optimization problems. These algorithms are based on the best and the worst solutions, which are computed during the optimization process. However, these algorithms apply to continuous problems only. In this article, the binary versions of Rao algorithms are proposed, which can be used for solving feature selection problems. These are applied to four publicly available Parkinson's disease datasets. Besides providing an optimal set of features, the k parameter of the k-nearest neighbour classifier is also optimized by the proposed approach. The performance of these algorithms has been measured taking an average of 30 independent runs using a 10-fold cross-validation procedure. Also, a comparison of the performance has been made with the other state of the art methods.Significance analysis of these algorithms has been made with the Friedman rank test.

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Section: The Proposed Binary Version Of Rao Algorithmsmentioning

confidence: 99%

Classification of Parkinson disease using binary Rao optimization algorithms

2021

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“…A.M. Shaheen used the EO for optimizing the configuration of multiple distribution networks [24]. In binary, EO was implemented to optimize the feature selection [25]. In [26], the optimal allocation problem of renewable distributed generators under uncertainties of the system was solved using EO.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

Ahmed¹,

Ebeed

Ali

et al. 2021

Electronics

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Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

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“…It is still necessary to have more optimization techniques to get additional enhanced results. To the best of the author's information, there are a few studies in the literature for the binary version of EO [83], [84]. This has motivated us in this study to propose a new binary version of EO and test its benefit in features selection problems as a binary optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

Elmanakhly¹,

Saleh²,

Rashed³

2021

IEEE Access

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In the last decade, data generated from different digital devices has posed a remarkable challenge for data representation and analysis. Because of the high-dimensional datasets and the rapid growth of data volume, a lot of challenges have been encountered in various fields such as data mining and data science. Conventional machine learning classifiers are of limited ability to handle the problems of high dimensionality that includes memory limitation, computational cost, and low accuracy performance. Consequently, there is a need to reduce the dimension of datasets by choosing the most significant features that would represent the data efficiently with minimum volume. This study proposes an improved binary version of the equilibrium optimizer algorithm (IBEO) to mitigate features selection problem. Two main enhancements are added to the original equilibrium optimizer (EO) to strengthen its performance. Opposition based learning is the first advancement added to the initialization stage of EO to enhance the diversity of the population in the search space. Local search algorithm is the second advancement added to enhance the exploitation of EO. Wrapper approaches can offer premium solutions. Thus, we used k-nearest neighbour classifier and support vector machine classifiers as the most popular wrapper methods. Moreover, dealing with the problem of over-fitting is an essential task that urges on applying k-fold cross-validation to split each dataset into training and testing data. Comparative tests with different well-known algorithms such as grey wolf optimization, grasshopper optimization, particle swarm optimization, whale optimization, dragonfly, and improved salp swarm algorithms are considered. The proposed algorithm is applied to the most commonly datasets used in the field to validate the performance. Statistical analysis studies demonstrate the effectiveness of the IBEO.

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An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection

Cited by 95 publications

References 66 publications

Classification of Parkinson disease using binary Rao optimization algorithms

Classification of Parkinson disease using binary Rao optimization algorithms

Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

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