Binary Symbiotic Organism Search Algorithm for Feature Selection and Analysis

Cao, Han; Zhou, Guo; Zhou, Yongquan

doi:10.1109/access.2019.2953800

Cited by 33 publications

(17 citation statements)

References 48 publications

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“…Table 6 shows the classification results that reference the same UCI dataset to evaluate the effectiveness of the proposed algorithm in the field of feature selection. This study cites four state-of-the-art feature selection models, and their brief descriptions are as follows: the continuous symbiotic organism search algorithm uses adaptive S-shaped transfer function to convert into a binary symbiosis organism search algorithm, named BSOS [49]; the basic PSO introduces two dynamic correction coefficient and spiral-shaped mechanisms to improve the position update formula of PSO, and uses the logic diagram sequence to enhance the diversity, named HPSO-SSM [50]; a binary butterfly optimization algorithm based on sigmoid transfer function can better converge to the optimal solution, named s-bBOA [51]; the grasshopper optimization algorithm combined with the mutation operator with linearly decreasing mutation rate enhances the exploration stage, named BGOA-M [52].…”

Section: Comparison With State-of-the-art Modelsmentioning

confidence: 99%

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

Lee

Zhuo

2021

Mathematics

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The accurate localization of the rolling element failure is very important to ensure the reliability of rotating machinery. This paper proposes an efficient and anti-noise fault diagnosis model for rolling elements. The proposed model is composed of feature extraction, feature selection and fault classification. Feature extraction is composed of signal processing and signal noise reduction. Signal processing is carried out by local mean decomposition (LMD), and signal noise reduction is performed by product function (PF) selection and wavelet packet decomposition (WPD). Through the steps of signal noise reduction, high-frequency noise can be effectively removed, and the fault information hidden under the noise can be extracted. To further improve the effectiveness of the diagnostic model, an improved binary particle swarm optimization (IBPSO) is proposed to find the most important features from the feature space. In IBPSO, cycling time-varying inertia weight is introduced to balance exploitation and exploration and improve the capability to escape from local solutions, and crossover and mutation operations are also introduced to improve exploration and exploitation capabilities, respectively. The main contributions of this research are briefly described as follows: (1) The feature extraction process applied in this research can effectively remove noise and establish a high-accuracy feature set. (2) The proposed feature selection algorithm has higher accuracy than the other state-of-the-art feature selection algorithms. (3) In a strong noise environment, the proposed rolling element fault diagnosis model is compared with the state-of-the-art fault diagnosis model in terms of classification accuracy. Experimental results show that the model can maintain high classification accuracy in a strong noise environment. Therefore, it can be proved that the fault diagnosis model proposed in this paper can be effectively applied to the fault diagnosis of rotating machinery.

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Section: Comparison With State-of-the-art Modelsmentioning

confidence: 99%

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

Lee

Zhuo

2021

Mathematics

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“…The same approach is used in the next article. Cao Han et al proposed the binary symbiotic organism search algorithm using a timevarying function as the FS method based on the wrapper [54]. In the literature, [55], Yuefeng Zheng et al proposed a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improved whale optimization algorithm (MPMDIWOA).…”

Section: Metaheuristic Algorithm and Its Application In Feature Selectionmentioning

confidence: 99%

An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection

2020

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Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. An equilibrium optimizer (EO) is a novel physics-based optimization algorithm; it was inspired by controlled volume mass balance models for estimating dynamic and equilibrium states. This paper presents two binary equilibrium optimizer algorithm and for selecting the optimal feature subset for classification problems. The first algorithm maps the continuous EO into a discrete type using S-shaped and V-shaped transfer functions (BEO-S and BEO-V). The second algorithm is based on the position of the current optimal solution (target) and position vector (BEO-T). To verify the performance of the proposed algorithm, 19 well-known UCI datasets are tested and compared with other advanced FS methods. The experimental results show that among the proposed binary EO algorithms, BEO-V2 has the best comprehensive performance and has better performance than other state-of-the-art metaheuristic algorithms in terms of the performance measures.

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“…Both TLBO and SOS have superiority over many different population-based algorithms in terms of rate of convergence, global solution, and computational time [19], [20]. The optimal results obtained by the optimization framework are taken after comparing the output of TLBO and SOS for each loading and weather condition.…”

Section: The Employment Of the Candidate Optimization Algorithms mentioning

confidence: 99%

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

et al. 2020

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The complexity of achieving optimal power flow in the presence of renewable resources decreases the accuracy and optimality levels of the power system due to the associated intermittency and uncertainty. The increased challenge of large-scale deployment of wind energy necessitates the proper modeling of wind impact on power system security and reliability levels. This article discusses a new reliable power flow optimization tool that accounts for wind power availability and uncertainty. An accurate wind forecast model is created to maintain power system security considering wind power variability. The error of the forecasting phase is included in the proposed model to accurately predict the available wind power. In this work, the scattered wind data is converted into informative frequency distribution considering the effect of averaging around integers, halves, and quarters. The proposed method maximizes the utilization level of wind energy without deteriorating the system security. The accuracy of the new proposed work is presented by comparing its results with other models discussed in the literature. A complete and integrated formulation of the objective function has been accomplished. The cost function includes transmission losses, generation operating costs, generation gas emissions, and valve-point effects. Reliable and efficient optimization algorithms are adopted to minimize the established cost function of the system ⎯namely, teaching-learningbased optimization and symbiotic organisms search algorithms. The effectiveness of the proposed approach is validated using the IEEE 39-bus system. INDEX TERMS Generation gas emission, generation operating cost, optimal power flow, symbiotic organisms search algorithm, valve-point effects, wind speed forecast.

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Binary Symbiotic Organism Search Algorithm for Feature Selection and Analysis

Cited by 33 publications

References 48 publications

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

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