EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection

Feng, Jinghui; Kuang, Haopeng; Zhang, Lihua

doi:10.3390/fi14060178

Cited by 8 publications

(8 citation statements)

References 47 publications

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“…This version incorporates three additional search mechanisms and operates in three sub-steps: (1) BBACO parameter initialization, which is based on the parameter design of the Taguchi method; (2) subpopulation initialization, which is done using the modified Latin hypercube Sampling (LHS) method; and (3) solution evaluation, where the best candidate solution in each subpopulation is selected concerning dependency measurement using the Rough Set theory (RST). The BBACO algorithm is used as an FS method for text data in three datasets: English, Arabic, and Malay texts [88].…”

Section: Reviewing the Applications Of Mas In Fsmentioning

confidence: 99%

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Parizi

2024

Preprint

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Recent technological advances in medical diagnosis have led to the generation of high-dimensional datasets. The presence of redundant and irrelevant features in these datasets can have adverse effects on the performance of machine learning (ML) methods and reduce the accuracy of their results. Therefore, feature selection (FS), i.e., a popular preprocessing method in ML, is used to select the optimal subsets of features to improve the accuracy of ML methods. This performance enhancement is more crucial while addressing high-dimensional medical issues. Since FS is a multiobjective binary optimization problem, it is necessary to develop efficient FS algorithms. Although metaheuristic algorithms (MAs) have been widely used for FS in medicine, they face different challenges in most applications, e.g., a lack of sufficient effectiveness and scalability to select the most effective features in small and large medical datasets. The cat and mouse-based optimizer (CMBO) is a novel MA based on the natural competitive behavior of cats and mice. Despite its acceptable performance in a variety of problems, the CMBO faces various challenges such as limited exploitation abilities, an unbalanced search mechanism, and high fluctuation in solutions to complex problems, e.g., FS. This paper proposes a modified and binary version of the CMBO called the BMCMBO to enhance the performance in selecting effective features from medical datasets. The BMCMBO involves significant modifications to the method of updating the positions of search agents, the method of selecting mice, the effect of the positional information of the most optimal member of the population, and the addition of the adaptive step size. These modifications are meant to improve the exploitation abilities, boost the accuracy of the solutions, and balance the search process when dealing with the FS problem in medical datasets. The performance of the proposed algorithm on 12 real medical datasets was compared with the performance of the most effective MA and CMBO variants. The statistical results demonstrated that BMCMBO was more effective than other evaluated methods. In addition, the BMCMBO algorithm was employed to select features and diagnose COVID-19 in a real case study. The proposed algorithm identified healthy and infected COVID-19 correctly samples with an accuracy of 98.4\%, demonstrating its superiority.

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Section: Reviewing the Applications Of Mas In Fsmentioning

confidence: 99%

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Parizi

2024

Preprint

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“…Their capacity for achieving optimal values through swarm optimization and prey-hunting behavior also contributed to their suitability for comparison. Previous research has investigated optimized scheduling using BBA [27], WOA [28], and GWO [29] techniques. Thus, this article opts for a comparative analysis of these three techniques.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Optimal Battery Charging Schedule for a Battery Swapping Station of an Electric Bus With a PV Integration Considering Energy Costs and Peak-to-Average Ratio

Boonraksa,

Pinthurat

et al. 2024

IEEE Access

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Across the globe, the adoption of electric vehicles (EVs), particularly in mass transit systems such as electric buses (E-bus), is on the rise in modern cities. This surge is attributed to their environmentally friendly nature, zero carbon emissions, and absence of engine noise. However, the charging of E-bus batteries could impact the peak demand on the main grid and its overall serviceability, especially when numerous batteries are charged simultaneously. This scenario may also lead to increased energy costs. To address the previously mentioned issue, battery swapping is employed at the charging station in lieu of conventional battery charging. In this paper, the battery swapping approach is utilized to establish the optimal battery charging schedule for E-buses, taking into account both energy costs and the peak-to-average ratio (PAR). The E-bus battery swapping stations incorporate photovoltaic (PV) power generation as their energy source. Three metaheuristic algorithms-namely, the binary bat algorithm (BBA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO)-are employed to identify the optimal conditions. The simulation results demonstrate that integrating the optimal battery charging schedule with a PV power generation system in an E-bus battery swapping station can effectively lower energy costs and the PAR when compared to traditional battery charging methods at charging stations. The optimal charging schedule derived through the GWO technique outperforms those obtained from the WOA and BBA techniques. This resulted in a notable reduction in peak demand from 758.41 to 580.73 kW, corresponding to a 23.43% decrease in peak demand. The integration of the GWO with battery charging scheduling and PV installation resulted in a significant 27.63% reduction in energy costs. As per the simulation results, an optimized battery swapping schedule has the potential to lower energy costs and enhance serviceability for the E-bus battery swapping station.INDEX TERMS Battery charging scheduling, battery swapping stations, electric buses, metaheuristic algorithm, peak-to-average ratio. I. INTRODUCTIONAdvancements in electric vehicle (EV) and battery technology have spurred increased usage, supported by governmental advocacy for clean energy, prompting a shift from internal combustion engine cars to electric vehicles. The development of smart cities includes the expansion of electric bus mass transit systems. Major cities are witnessing a growing preference for electric vehicles over traditional internal combustion engine vehicles, particularly in the form of electric buses (Ebus), offering reductions in diesel fuel usage and air pollution

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“…The authors report the best and worst solutions obtained, the average of the different executions performed, and their standard deviation. This metric has been used in [21,31,33,46,48,50,53,54,57,60,[78][79][80][81][82]84,85,[87][88][89][90][91][92][93][94][95][96][97][98][99][101][102][103][105][106][107][108]110,112,114,115,[142][143][144][145][146][147][148][149][151]…”

Section: Metaheuristic Metricsmentioning

confidence: 99%

“…It is more exhaustive and aims to find the optimal binarization method for specific scenarios. Cases of this approach are the articles [47,71,78,89,91,92,96,106,107,110,114,124,129,131,132,138,142,144,146,154,165,172,176,179].…”

mentioning

confidence: 99%

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

Barrera-García,

Cisternas-Caneo,

Crawford

et al. 2023

Biomimetics

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Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.

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EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection

Cited by 8 publications

References 47 publications

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Optimal Battery Charging Schedule for a Battery Swapping Station of an Electric Bus With a PV Integration Considering Energy Costs and Peak-to-Average Ratio

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

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