Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

Ye, Fu-Lan; Lee, Chou‐Yuan; Lee, Zne-Jung; Huang, Jian-Qiong; Tu, Jih-Fu

doi:10.3390/sym12020229

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…Ye et al reported "Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data" [7]. In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classify imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization.…”

Section: Lan Et Al Reported "Symmetric Modeling Of Communication Effectiveness and Satisfactionmentioning

confidence: 99%

Selected Papers from IIKII 2019 Conferences in Symmetry

Meen

Tijus

2020

Symmetry

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The International Institute of Knowledge Innovation and Invention (IIKII) is an institute that promotes the exchange of innovations and inventions, and establishes a communication platform for international innovations and researches. In 2019, IIKII cooperated with the Institute of Electrical and Electronics Engineers (IEEE) Tainan Section Sensors Council to hold IEEE conferences such as IEEE ICIASE 2019, IEEE ECBIOS 2019, IEEE ICKII 2019, ICUSA-GAME 2019, and IEEE ECICE 2019. This Special Issue entitled “Selected Papers from IIKII 2019 conferences” aims to select excellent papers from IIKII 2019 conferences, including symmetry in physics, chemistry, biology, mathematics, and computer science, etc. It selected 21 excellent papers from 750 papers presented in IIKII 2019 conferences on the topic of symmetry. The main goals of this Special Issue are to encourage scientists to publish their experimental and theoretical results in as much detail as possible, and to discover new scientific knowledge relevant to the topic of symmetry.

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Section: Lan Et Al Reported "Symmetric Modeling Of Communication Effectiveness and Satisfactionmentioning

confidence: 99%

Selected Papers from IIKII 2019 Conferences in Symmetry

Meen

Tijus

2020

Symmetry

Self Cite

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“…Ye et al 24 have integrated PSO in improved bacterial foraging optimization algorithm for classifying imbalanced data. Borderline‐SMOTE and Tomek link are first used to preprocess the dataset.…”

Section: Introductionmentioning

confidence: 99%

Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Priya¹,

Rajappan²,

Anitha³

et al. 2021

Computational Intelligence

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Real-world datasets used for classification often face many challenges when they are imbalanced in nature which is unavoidable and need to be handled by analysts. Many researchers have proposed methods for handling imbalanced datasets and they mostly concentrated on handling binary classification with only two class labels. Only very few research works have been carried out for treating highly imbalanced datasets and fail to handle multiclass datasets. To address imbalance problem in multiclass datasets, this paper proposes a Variable Population sized Particle Swarm Optimization (VPPSO) which is a modified version of Particle Swarm Optimization (PSO) which works based on clustering. PSO usually has fixed population size and has high computational complexity. In order to reduce this, the population size is varied over generations and the particles are loaded into the population iteratively by retaining the balance nature of solutions. PSO optimizes the selection of training and testing samples from each class label in imbalanced datasets for improved classification results. From the implementation results, it is evident that using VPPSO, highly imbalanced datasets with multiclass attributes are classified more efficiently

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“…The Bacterial Foraging Optimization (BFO) algorithm is a new swarm intelligence optimization technique that possesses a series of advantages including insensitivity to initial values and parameter selection, strong robustness, simplicity, ease of implementation, parallel processing and global search [31]. BFO has been applied in a wide range of optimization problems such as the energy forecasting [32], expert energy management considering the uncertainty [33], the imbalanced data classification [34], and robotic cell scheduling [35]. The advantages of BFO against conventional swarm intelligence approaches greatly encourages us to solve the problems in enhancing the train driving control profiles and evaluate the energy saving space of the train scheduling schemes.…”

Section: Introductionmentioning

confidence: 99%

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Liu

Cai

et al. 2020

Energies

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Energy conservation is attracting more attention to achieve a reduced lifecycle system cost level while enabling environmentally friendly characteristics. Conventional research mainly concentrates on energy-saving speed profiles, where the energy level evaluation of the timetable is usually considered separately. This paper integrates the train driving control optimization and the timetable characteristics by analyzing the achievable tractive energy conservation performance and the corresponding boundaries. A calculation method for energy efficient driving control solution is proposed based on the Bacterial Foraging Optimization (BFO) strategy, which is utilized to carry out batch processing with timetable. A boundary identification solution is proposed to detect the range of energy conservation capability by considering the relationships with average interstation speed and the passenger volume condition. A case study is presented using practical data of Beijing Metro Batong Line and two timetable schemes. The results illustrate that the proposed optimized energy efficient driving control approach is capable of saving tractive energy in comparison with the conventional traction calculation-based train operation solution. With the proposed boundary identification method, the capability space of the energy conservation profiles with respect to the energy reduction and energy saving rate is revealed. Moreover, analyses and discussions on effects from different passenger load conditions are given to both the weekday and weekend timetables. Results of this paper may assist the decision making of rail operators and engineers by enhancing the cost effectiveness and energy efficiency.

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Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

Cited by 7 publications

References 40 publications

Selected Papers from IIKII 2019 Conferences in Symmetry

Selected Papers from IIKII 2019 Conferences in Symmetry

Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

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