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AC security-constrained transmission network expansion planning problem using an improved binary particle swarm optimization

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 241卷

作者： Garcia-Mercado, Josue Isai Gutierrez-Alcaraz, Guillermo Gonzalez-Cabrera, N. Hinojosa, Victor H. Tecnol Nacl Mexico IT Morelia Dept Elect Engn Morelia Mich Mexico Natl Autonomous Univ Mexico UNAM Dept Elect Energy Mexico City Mexico Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso Chile

Traditionally, linearized AC approximation is considered for network representation, which fails to offer an operational solution to cope with the reactive power problem in the planning stage. The lack of consideration of voltage and reactive power affects the implementation of the transmission network investment plan. This paper uses an improved binary particle swarm algorithm to solve the multi-stage Security-Constrained Transmission Network Expansion Planning problem based on the AC power flow problem. The Reactive Power Planning stage uses a load shedding formulation by considering all load buses as candidate locations for installing additional reactive power devices. Different intelligent logical and operational strategies are developed to reduce the computational burden of the optimization algorithm by eliminating AC-infeasible solutions. The proposed approach is validated with numerical simulations applied to the classic Garver's system, and its effectiveness is corroborated using the IEEE RTS 24- and 118-bus system for a 10-year planning horizon, respectively. Also, simulation results are compared with other metaheuristic techniques reported in the literature.

关键词： AC security-constrained transmission network expansion planning binary particle swarm optimization Reactive power planning

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binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces

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BIO-MEDICAL MATERIALS AND ENGINEERING 2015年第Sup1期26卷 S1523-S1532页

作者： Wei, Qingguo Wei, Zhonghai Nanchang Univ Dept Elect Engn Nanchang 330031 Peoples R China

A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.

关键词： Brain-computer interface motor imagery common spatial pattern binary particle swarm optimization frequency band selection

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binary particle swarm optimization-based T-S fuzzy predictive controller for nonlinear automotive application

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NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2803-2818页

作者： Abdelrahim, Elsaid Md Northern Border Univ Fac Sci Ar Ar Saudi Arabia Tanta Univ Fac Sci Tanta Egypt

In this paper, a robust evolutionary computing-assisted Takagi-Sugeno fuzzy predictive controller (T-S FPC) has been developed for nonlinear vehicle fuel injection and emission control. To strengthen the performance of T-S FPC, we have applied an enhanced evolutionary computing algorithm named binary particle swarm optimization (BPSO) that achieves optimal control variable by performing minimization of the cost function iteratively, where the cost function signifies the mean square error between reference data and the actual predicted data. To examine the efficacy of the proposed system, a case study was performed for an automotive vehicle to control its fuel injection, throttle angle, and emission control under nonlinear conditions. The simulation results affirmed that the proposed BPSO-based T-S FPC model exhibits optimal performance by achieving target performance with low mean square error between expected functions and prediction outcomes. The efficiency of the proposed BPSO T-S FPC model enables it to be used for online nonlinear control purposes for any type of the vehicle systems.

关键词： Takagi-Sugeno fuzzy system Evolutionary computing binary particle swarm optimization Constrained optimization Fuel injection control

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binary particle swarm optimization as a detection tool for influential subsets in linear regression

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JOURNAL OF APPLIED STATISTICS 2021年第13-15期48卷 2441-2456页

作者： Deliorman, G. Inan, D. Beykoz Univ Fac Engn & Architecture Software Engn Istanbul Turkey Marmara Univ Fac Arts & Sci Dept Stat Istanbul Turkey

An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.

关键词： Influential subsets binary particle swarm optimization heuristic algorithms linear regression diagnostics

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binary particle swarm optimization for Black-Scholes option pricing

Binary particle swarm optimization for Black-Scholes option ...

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11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks

作者： Lee, Sangwook Lee, Jusang Shim, D. Jeon, Moongu GIST Sch Informat & Mechatron Kwangju 500712 South Korea Samsung SDS Consulting Div Seoul South Korea Kwandong Univ Dept Traff Engn Kangnung 201701 South Korea

ISBN: (纸本)9783540748175

The Black-Scholes option pricing (BS) model is a landmark in contingent claim theory and has been widely accepted in financial markets. However, it has a difficulty in the use of the model, because the volatility which is a nonlinear function of the other parameters must be estimated. The more accurately the volatility is estimated, the more accurate estimates of theoretical option prices will be. Thus, we propose a new model based on particle swarm optimization (PSO), which finds more precise theoretical values of options with estimates of the implied volatility than genetic algorithms (GAs).

关键词： Black-Scholes option pricing binary particle swarm optimization bit change mutation

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binary particle swarm optimization for Feature Selection in Detection of Infants with Hypothyroidism

Binary Particle Swarm Optimization for Feature Selection in ...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Zabidi, A. Khuan, L. Y. Mansor, W. Yassin, I. M. Sahak, R. Univ Teknol Mara Fac Elect Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9781424441228

Hypothyroidism in infants is caused by the insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity as a result of the enlarged liver, their cry signals are unique and can be distinguished from the healthy infant cries. This study investigates the effect of feature selection with binary particle swarm optimization on the performance of MultiLayer Perceptron classifier in discriminating between the healthy infants and infants with hypothyroidism from their cry signals. The feature extraction process was performed on the Mel Frequency Cepstral coefficients. Performance of the MLP classifier was examined by varying the number of coefficients. It was found that the BPSO enhances the classification accuracy while reducing the computation load of the MLP classifier. The highest classification accuracy of 99.65% was achieved for the MLP classifier, with 36 filter banks, 5 hidden nodes and 11 BPS optimised MFC coefficients.

关键词： Accuracy Classification algorithms Feature extraction Filter banks Mel frequency cepstral coefficients optimization particle swarm optimization Pediatrics binary particle swarm optimization cepstral analysis chest cavity cry signals diseases feature extra

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A binary particle swarm optimization for the Minimum Weight Dominating Set Problem

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Journal of Computer Science & Technology 2018年第2期33卷 305-322页

作者： Geng Lin Jian Guan Department of Mathematics Minjiang University Fuzhou 350121 China Modern Educational Technology Center Minjiang University Fuzhou 350121 China

The minimum weight dominating set problem （MWDSP） is an NP-hard problem with a lot of real-world applications. Several heuristic algorithms have been presented to produce good quality solutions. However, the solution time of them grows very quickly as the size of the instance increases. In this paper, we propose a binary particle swarm optimization （FBPSO） for solving the MWDSP approximately. Based on the characteristic of MWDSP, this approach designs a new position updating rule to guide the search to a promising area. An iterated greedy tabu search is used to enhance the solution quality quickly. In addition, several stochastic strategies are employed to diversify the search and prevent premature convergence. These methods maintain a good balance between the exploration and the exploitation. Experimental studies on 106 groups of 1 060 instances show that FBPSO is able to identify near optimal solutions in a short running time. The average deviation between the solutions obtained by FBPSO and the best known solutions is 0.441%. Moreover, the average solution time of FBPSO is much less than that of other existing algorithms. In particular, with the increasing of instance size, the solution time of FBPSO grows much more slowly than that of other existing algorithms.

关键词： metaheuristics binary particle swarm optimization tabu search dominating set problem combinatorial optimization

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Sensor management of LEO constellation using modified binary particle swarm optimization

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OPTIK 2018年 172卷 879-891页

作者： Qin, Zheng Liang, Yan-gang Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

In this paper, we are concerned with the problem of utilizing a low Earth orbit (LEO) infrared constellation in order to track the midcourse ballistic missile. Due to the limited sensor resources of LEO constellation, an appropriate sensor management strategy is necessary. The posterior Cramer-Rao lower bound (PCRLB) is used as the basis for sensor management of LEO constellation. Moreover, a modified binary particle swarm optimization (MBPSO) algorithm is presented for use in finding the optimal allocation scheme of sensors for maximizing the tracking accuracy. The proposed MBPSO algorithm adopts a different transfer function and a new method to generate initial population. The MBPSO's performance and the PCRLB-based sensor management strategy are verified by applying to a case study.

关键词： Sensor management PCRLB Target tracking accuracy LEO constellation binary particle swarm optimization

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Optimal feature selection in industrial foam injection processes using hybrid binary particle swarm optimization and Gravitational Search Algorithm in the Mahalanobis-Taguchi System

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SOFT COMPUTING 2020年第1期24卷 341-349页

作者： Resendiz-Flores, Edgar O. Alejandro Navarro-Acosta, Jesus Hernandez-Martinez, Agustin Technol Inst Saltillo Div Postgrad Studies & Res Blvd V Carranza 2400 Col Tecnol Saltillo 25280 Coahuila Mexico Autonomous Univ Coahuila Res Ctr Appl Math Saltillo Coahuila Mexico

The detection of variables that contribute to the variation of a system is one of the most important considerations in the industrial manufacturing processes. This work presents the combination of Mahalanobis-Taguchi system and a hybrid binary metaheuristic based on particle swarm optimization and gravitational search algorithm (BPSOGSA) to perform an optimal feature selection in order to detect the relevant variables in a real process of foam injection in automotive industry. The proposed method is compared with other feature selection approach based in binary PSO algorithm. The experimental results revealed that BPSOGSA is faster and successfully converge selecting a smallest subset of features than BPSO. Moreover, the feature selection effect is validated through other widely used machine learning algorithms which improve their accuracy performance when they are trained with the subset of detected variables by the proposed system.

关键词： Feature selection Mahalanobis-Taguchi System binary particle swarm optimization binary Gravitational Search Algorithm

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A binary particle swarm optimization algorithm for ship routing and scheduling of liquefied natural gas transportation

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2020年第4期12卷 223-232页

作者： Yazdi, Amir Karbassi Kaviani, Mohamad Amin Emrouznejad, Ali Sahebi, Hadi Islamic Azad Univ South Tehran Branch Young Researchers & Elite Club Tehran Iran Univ Cyprus KIOS Res & Innovat Ctr Excellence Dept Elect & Comp Engn Nicosia Cyprus Aston Univ Aston Business Sch Birmingham W Midlands England Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

With the increasing global demands for energy, fuel supply management is a challenging task of today's industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic binary particle swarm optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations.

关键词： binary particle swarm optimization ship routing scheduling transportation liquefied natural gas optimization

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