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Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization

GENOMICS 2019年第4期111卷 669-686页

作者： Pashaei, Elnaz Pashaei, Elham Aydin, Nizamettin Yildiz Tech Univ Dept Comp Engn Istanbul Turkey

In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic binary Black Hole Algorithm (BBHA) and binary particle swarm optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) prefiltering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA);k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets.

关键词： Gene selection binary black hole algorithm binary particle swarm optimization Sparse partial least squares discriminant analysis Gene expression

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A New Affine Arithmetic-Based Optimal Network Reconfiguration to Minimize Losses in a Distribution System Considering Uncertainty Using binary particle swarm optimization

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第6-7期48卷 628-639页

作者： Raj, Vinod Kumar, Boddeti Kalyan Indian Inst Technol Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

In the present work, binary particle swarm optimization (BPSO) based optimal re-configuration for balanced and unbalanced radial distribution networks using Affine Arithmetic (AA), with uncertainty in generation and load, is proposed to minimize the system losses. An expression for three phase real affine power loss is derived with partial deviations of real power loss in lines with respect to power injections in other buses and also with respect to power injections in other phases in case of unbalanced distribution systems. The major contribution of the present work is the application of AA based optimal network reconfiguration, to both balanced and unbalanced radial distribution networks with uncertainty. The proposed method is tested on IEEE 16, 33, 85 and 119 bus balanced distribution systems and an unbalanced 123 bus system with Distributed Generation (DG) connected at some buses. The optimal loss intervals obtained by the proposed method are compared with that obtained by Interval Arithmetic (IA) and Monte Carlo (MC) simulations based methods. The simulation results show that proposed AA based analysis gives an optimal reconfiguration, for both balanced and unbalanced radial distribution systems with uncertainty as compared to existing IA based method.

关键词： affine arithmetic binary particle swarm optimization distribution system network reconfiguration real power loss minimization uncertainty

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Compact Pixelated Microstrip Forward Broadside Coupler Using binary particle swarm optimization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第12期70卷 5265-5274页

作者： Parsaei, Kourosh Keshavarz, Rasool Boroujeni, Rashid Mirzavand Shariati, Negin Univ Technol Sydney UTS Fac Engn & IT RF & Commun Technol RFCT Res Lab Ultimo NSW 2000 Australia Alberta Univ Edmonton AB T6G 1H9 Canada Univ Technol Sydney UTS RF & Commun Technol RFCT Res Lab Sydney NSW Australia Alberta Univ Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Alberta Univ Dept Mech Engn Edmonton AB T6G 1H9 Canada

In this paper, a compact microstrip forward broadside coupler (MFBC) with high coupling level is proposed in the frequency band of 3.5 similar to 3.8 GHz. The coupler is composed of two parallel pixelated transmission lines. To validate the design strategy, the proposed MFBC is fabricated and measured. The measured results demonstrate a forward coupler with 3 dB coupling, and a compact size of 0.12 lambda g x 0.10 lambda g. binary particle swarm optimization (BPSO) design methodology and flexibility of pixelation enable us to optimize the proposed MFBC with desired coupling level and operating frequency within a fixed dimension. Also, low sensitivity to misalignment between two coupled TLs makes the proposed coupler a good candidate for near-field Wireless Power Transfer (WPT) application and sensors.

关键词： binary particle swarm optimization broadside forward coupling flexible design method pixelated near field coupler

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Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing

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APPLIED INTELLIGENCE 2019年第9期49卷 3308-3330页

作者： Mapetu, Jean Pepe Buanga Chen, Zhen Kong, Lingfu Yanshan Univ Colleague Informat Sci & Engn Qinhuangdao Hebei Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066004 Hebei Peoples R China

With the increasing large number of cloud users, the number of tasks is growing exponentially. Scheduling and balancing these tasks amongst different heterogeneous virtual machines (VMs) under constraints such as, low makespan, high resource utilization rate, low execution cost and low scheduling time, become NP-hard optimization problem. So, due to the inefficiency of heuristic algorithms, many meta-heuristic algorithms, such as particle swarm optimization (PSO) have been introduced to solve the said problem. However, these algorithms do not guarantee that the optimal solution can be found, if they are not combined with other heuristic or meta-heuristic algorithms. Further, these algorithms have high time complexity, making them less useful in realistic scenarios. To solve the said NP-problem effectively, we propose an efficient binary version of PSO algorithm with low time complexity and low cost for scheduling and balancing tasks in cloud computing. Specifically, we define an objective function which calculates the maximum completion time difference among heterogeneous VMs subject to updating and optimization constraints introduced in this paper. Then, we devise a particle position updating with respect to load balancing strategy. The experimental results show that the proposed algorithm achieves task scheduling and load balancing better than existing meta-heuristic and heuristic algorithms.

关键词： Task scheduling binary particle swarm optimization Cloud computing Load balancing Completion time Time complexity

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Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2016年第4期61卷 431-441页

作者： Wang, Shuihua Phillips, Preetha Yang, Jianfei Sun, Ping Zhang, Yudong Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Jiangsu Key Lab 3D Printing Equipment & Mfg Nanjing 210042 Jiangsu Peoples R China Guangxi Key Lab Mfg Syst & Adv Mfg Technol Guilin 541004 Guangxi Peoples R China Shepherd Univ Sch Nat Sci & Math Shepherdstown WV 25443 USA CUNY City Coll New York Dept Elect Engn New York NY 10031 USA

Aim: To develop an automatic magnetic resonance (MR) brain classification that can assist physicians to make a diagnosis and reduce wrong decisions. Method: This article investigated the binary particle swarm optimization (BPSO) approach and proposed its three new variants: BPSO with mutation and time-varying acceleration coefficients (BPSO-MT), BPSO with mutation (BPSO-M), and BPSO with time-varying acceleration coefficients (BPSO-T). We first extracted wavelet entropy (WE) features from both approximation and detail sub-bands of eight-level decomposition. Afterwards, we used the proposed BPSO-M, BPSO-T, and BPSO-MT to select features. Finally, the selected features were fed into a probabilistic neural network (PNN). Results: The proposed BPSO-MT performed better than BPSO-T and BPSO-M. It finally selected two features of entropies of the following two sub-bands (V1, D1). The proposed system "WE + BPSO-MT + PNN" yielded perfect classification on Data160 and Data66. In addition, it yielded 99.53% average accuracy for the Data255, over 10 repetitions of k-fold stratified cross validation (SCV), higher than state-of-the-art approaches. Conclusions: The proposed method is effective for MR brain classification.

关键词： binary particle swarm optimization cross validation magnetic resonance imaging mutation probabilistic neural network time-varying acceleration coefficients wavelet entropy

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Cooperative coding and caching scheduling via binary particle swarm optimization in software-defined vehicular networks

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NEURAL COMPUTING & APPLICATIONS 2021年第5期33卷 1467-1478页

作者： Xiao, Ke Liu, Kai Xu, Xincao Feng, Liang Wu, Zhou Zhao, Qiangwei Chongqing Univ Coll Comp Sci Chongqing Peoples R China Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing Peoples R China Chongqing Univ Sch Automat Chongqing Peoples R China Wuhan Cabit Informat Co Wuhan Hubei Peoples R China

With recent development in vehicular communication technologies, much attention has been paid to data dissemination in vehicular networks. In particular, the infrastructure-to-vehicle (I2V) communication is one of the primary technologies to provide a variety of information services. To enhance the bandwidth efficiency of I2V communication, this work considers in a software-defined vehicular networks (SDVN), aiming at exploiting synergistic effects of network coding and vehicular caching. First, we consider a data service scenario in which roadside unites (RSUs) are connected with the controller, which exercises scheduling decisions based on service requests received from vehicles. On this basis, we formulate a cooperative coding and caching scheduling problem with the objective of maximizing the bandwidth efficiency of I2V communication. Then, we propose a binary particle swarm optimization (BPSO)-based coding scheduling (BPSO_CS) algorithm. Finally, we build the simulation model and give a comprehensive performance evaluation. The results conclusively demonstrate the superiority of the proposed solution.

关键词： SDVN I2V communication Network coding binary particle swarm optimization

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Entropy based binary particle swarm optimization and classification for ear detection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 27卷 115-128页

作者： Ganesh, Madan Ravi Krishna, Rahul Manikantan, K. Ramachandran, S. MS Ramaiah Inst Technol Bangalore 560054 Karnataka India SJB Inst Technol Bangalore 560060 Karnataka India

Ear detection in facial images under uncontrolled environments with varying occlusion, pose, background and lighting conditions is challenging. In this paper, we propose a novel technique, namely Entropic binary particle swarm optimization (EBPSO) which generates an entropy map, the highest value of which is used to localize the ear in a face image. Also, Dual Tree Complex Wavelet Transform (DTCWT) based background pruning is used to eliminate most of the background in the face image. This is achieved as a result of DTCWT highlighting the strong curves in the foreground. The resulting preprocessed image contains the salient facial features and prepares the ground for ear detection. The Entropy based classifier successfully demarcates the ear regions from other facial features, based on observed patterns of entropy. Experimental results show the promising performance of EBPSO for ear detection on four benchmark face databases: CMU PIE, Pointing Head Pose, Color FERET and UMIST. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Entropy Ear detection binary particle swarm optimization Dual Tree Complex Wavelet Transform

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A hybrid binary particle swarm optimization for the obnoxious p-median problem

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INFORMATION SCIENCES 2018年 425卷 1-17页

作者： Lin, Geng Guan, Jian Minjiang Univ Dept Math Fuzhou 350121 Fujian Peoples R China Minjiang Univ Modem Educ Technol Ctr Fuzhou 350121 Fujian Peoples R China

The obnoxious p-median problem can be formulated as a constrained binary linear program. It is NP-hard, and has a lot of real world applications. In this paper, a hybrid binary particle swarm optimization is proposed to solve the obnoxious p-median problem. A new position updating rule is presented to inherit the good structure of previous high quality solutions. Furthermore, two tabu based mutation operators are used to avoid the premature convergence and guide the search to a promising area. A greedy repair procedure is developed to repair infeasible solutions. In addition, an iterated greedy local search procedure is utilized to enhance the exploitation ability. Extensive experiments are done on a set of 72 benchmark instances from the literature. Experimental results and comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm. In particular, the proposed algorithm finds new best solutions for 15 instances. Compared with existing algorithms, the proposed algorithm is able to find better average objective function value in a short average computing time. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Metaheuristics binary particle swarm optimization Iterated greedy local search Obnoxious p-median problem Combinatorial optimization

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Optimal placement and sizing from standpoint of the investor of Photovoltaics Grid-Connected Systems using binary particle swarm optimization

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APPLIED ENERGY 2010年第6期87卷 1911-1918页

作者： Gomez, M. Lopez, A. Jurado, F. Univ Jaen Dept Elect Engn Jaen 23700 Spain Univ Jaen Dept Elect Engn Jaen 23071 Spain

This paper introduces a binary particle swarm optimization based method to accomplish optimal location and size of a Photovoltaics Grid-Connected System (PVGCS) for distributed power generation. The main technical constraint is the maximum installed peak power, which is limited for utilities Power Distributor Company. The fitness function to be optimized is the profitability index. A fair comparison between the proposed algorithm and other methods is performed. For such goal, convergence curves of the average profitability index versus number of iterations are computed. The proposed algorithm reaches a better solution than genetic algorithms when considering similar computational cost (similar number of evaluations). (C) 2010 Elsevier Ltd. All rights reserved.

关键词： binary particle swarm optimization PVGCS Photovoltaic Distributed power generation Profitability index

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A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第4期37卷 3177-3183页

作者： Babaoglu, Ismail Findik, Oguz Ulker, Erkan Selcuk Univ Dept Comp Engn Konya Turkey

The aim of this study is to search the efficiency of binary particle swarm optimization (BPSO) and genetic algorithm (CA) techniques as feature selection models on determination of coronary artery disease (CAD) existence based upon exercise stress testing (EST) data. Also, increasing the classification performance of the classifier is another aim. The dataset having 23 features was obtained from patients who had performed EST and coronary angiography. Support vector machine (SVM) with k-fold cross-validation method is used as the classifier system of CAD existence in both BPSO and CA feature selection techniques. Classification results of feature selection technique using BPSO and CA are compared with each other and also with the results of the whole features using simple SVM model. The results show that feature selection technique using BPSO is more successful than feature selection technique using CA on determining CAD. Also with the new dataset composed by feature selection technique using BPSO, this study reached more accurate values of success on CAD existence research with more little complexity of classifier system and more little classification time compared with whole features used SVM. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： binary particle swarm optimization Genetic algorithm Support vector machine Exercise stress testing Coronary artery disease

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