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Flexible optimization of variables based on exponential and linear attenuation elimination-binary dragonfly algorithm in near infrared spectroscopic analysis

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INFRARED PHYSICS & TECHNOLOGY 2024年 140卷

作者： Wang, Honghong Zhang, Mingjin Xiong, Zhixin Yang, Wuye Wu, Ting Du, Yiping East China Univ Sci & Technol Sch Chem & Mol Engn Shanghai Key Lab Funct Mat Chem Shanghai 200237 Peoples R China Qinghai Normal Univ Sch Chem & Chem Engn Xining 810016 Qinghai Peoples R China Nanjing Forestry Univ Coll Light Ind & Food Engn Nanjing 210037 Peoples R China

Single binary dragonfly algorithm (Single-BDA) which is an intelligent optimization algorithm, normally needs more calculation time and often obtains unrobust variables. In this study, an Exponential and Linear Attenuation Elimination-binary dragonfly algorithm (ELAE-BDA) is proposed to overcome the problems by combining exponential and linear attenuation functions. The algorithm can quickly eliminate a part of the variables with large RMSECV in the iterative process through the exponential attenuation function first, and use the linear attenuation function at the end of the iteration for meticulous elimination of invalid variables. It avoids the important variables being deleted unexpectedly, greatly improves the running speed of the algorithm, reduces the randomness of results, and significantly improves the analysis ability of the model. Three near-infrared spectral datasets are used as research objects to evaluate the performance of the ELAE-BDA algorithm, and 6 wavelength selection algorithms including Single-BDA, GA, CARS, MC-UVE, SCARS, and fiPLS are used for comparison. The results show that the ELAE-BDA algorithm can obtain results with almost the smallest number of wavelengths and the highest stability. The established PLSR models have the best prediction with the lowest RMSECV and RMSEP, indicating that the proposed algorithm can provide a more accurate and effective results of wavelength optimization for near-infrared spectral analysis.

关键词： Near infrared spectroscopy Exponential and linear attenuation elimination binary dragonfly algorithm Variable selection Quantitative analysis

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A Novel Hybrid Gene Selection Based on Random Forest Approach and binary dragonfly algorithm 18

A Novel Hybrid Gene Selection Based on Random Forest Approac...

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18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Boroujeni, Sayed Pedram Haeri Pashaei, Elnaz Istanbul Aydin Univ Dept Artificial Intelligence Engn Istanbul Turkey Istanbul Aydin Univ Dept Software Engn Istanbul Turkey

ISBN: (纸本)9781665400299

Microarrays dataset contains a huge number of genes and a few samples. This issue can lead to the curse of dimensionality in large datasets. To overcome this challenge, gene selection is a method used for identifying the independent genes and removing redundant or noisy ones from the dataset. This study proposes a novel hybrid approach based on the combination of Random Forest Ranking (RFR) and binary dragonfly algorithm (BDA) to identify the significant genes. The proposed method comprises two steps. In the first step, RFR is employed to remove irrelevant genes and select the subsets of optimal genes. In the second step, BDA is applied to select the most informative genes that can lead to the accurate detection of cancer. The BDA optimizer is a recently proposed metaheuristic algorithm that utilizes Naive Bayes (NB) classifier as an evaluator. In this paper, four microarray datasets are used to evaluate the performance of the proposed hybrid approach. Experimental results illustrate that the proposed work significantly outperforms existing meta-heuristic methods regarding classification accuracy and the optimal number of selected genes.

关键词： Gene selection Random Forest Ranking binary dragonfly algorithm Naive Bayes classifier

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A Hyper Learning binary dragonfly algorithm for Feature Selection: A COVID-19 Case Study

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KNOWLEDGE-BASED SYSTEMS 2021年 212卷

作者： Too, Jingwei Mirjalili, Seyedali Univ Tekn Malaysia Melaka Fac Elect Engn Durian Tunggal 76100 Melaka Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

The rapid expansion of information science has caused the issue of "the curse of dimensionality", which will negatively affect the performance of the machine learning model. Feature selection is typically considered as a pre-processing mechanism to find an optimal subset of features from a given set of all features in the data mining process. In this article, a novel Hyper Learning binary dragonfly algorithm (HLBDA) is proposed as a wrapper-based method to find an optimal subset of features for a given classification problem. HLBDA is an enhanced version of the binary dragonfly algorithm (BDA) in which a hyper learning strategy is used to assist the algorithm to escape local optima and improve searching behavior. The proposed HLBDA is compared with eight algorithms in the literature. Several assessment indicators are employed to evaluate and compare the effectiveness of these methods over twenty-one datasets from the University of California Irvine (UCI) repository and Arizona State University. Also, the proposed method is applied to a coronavirus disease (COVID-19) dataset. The results demonstrate the superiority of HLBDA in increasing classification accuracy and reducing the number of selected features.(2) (C) 2020 Elsevier B.V. All rights reserved.

关键词： binary dragonfly algorithm Feature selection Data mining Optimization Classification algorithm binary Optimization Particle Swarm Optimization Combinatorial Optimization Artificial Intelligence

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An enhanced binary dragonfly algorithm based on a V-shaped transfer function for optimization of pump scheduling program in water supply systems (case study of Iran)

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ENGINEERING FAILURE ANALYSIS 2021年 123卷

作者： Jafari-Asl, Jafar Azizyan, Gholamreza Monfared, Seyed Arman Hashemi Rashki, Mohsen Andrade-Campos, Antonio G. Univ Sistan & Baluchestan Fac Engn Dept Civil Engn Zahedan Iran Univ Sistan & Baluchestan Fac Arts & Architecture Dept Architecture Engn Zahedan Iran Univ Aveiro Ctr Mech Technol & Automat GRIDS Res Grp Campus Univ Santiago P-3810193 Aveiro Portugal

With the continual growth of population and shortage of energy resources, the optimal consumption of these resources is of particular importance. One of these energy sources is electricity, with a significant amount being used in pumping stations for water distribution systems (WDS). Determining the proper pumping schedule can make significant savings in energy consumption and particularly in costs. This study aims to present an improved population-based nature inspired optimization algorithm for pumping scheduling program in WDS. To address this issue, the binary dragonfly algorithm based on a new transfer-function coupled with the EPANET hydraulic simulation model is developed to reduce the energy consumption of pumping stations. The proposed model was firstly implemented and evaluated on a benchmark test example, then on a real water pumping station. Comparison of the proposed method and the genetic algorithm (GA), evolutionary algorithm (EA), ant colony optimization (ACO), artificial bee colony (ABC), particle swarm optimization (PSO), and firefly (FF) was conducted on the benchmark test example, while the obtained results indicate that the proposed framework is more computationally efficient and reliable. The results of the real case study show that while considering all different constraints of the problem, the proposed model can decrease the cost of energy up to 27% in comparison with the current state of operation.

关键词： Pump scheduling program Energy cost Transfer function binary dragonfly algorithm Water supply system

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A Hybrid binary dragonfly algorithm with an Adaptive Directed Differential Operator for Feature Selection

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REMOTE SENSING 2023年第16期15卷

作者： Chen, Yilin Gao, Bo Lu, Tao Li, Hui Wu, Yiqi Zhang, Dejun Liao, Xiangyun Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430073 Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430073 Peoples R China Wuhan Inst Technol Hubei Engn Res Ctr Intelligent Prod Line Equipment Wuhan 430073 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430073 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518052 Peoples R China

Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm's ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality.

关键词： feature selection binary dragonfly algorithm differential evolution algorithm multiobjective optimization classification

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Solving Graph Coloring Problem Using an Enhanced binary dragonfly algorithm

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第3期10卷 23-45页

作者： Baiche, Karim Meraihi, Yassine Hina, Manolo Dulva Ramdane-Cherif, Amar Mahseur, Mohammed Univ MHamed Bougara Boumerdes Appl Automat Lab Boumerdes Algeria Univ MHamed Bougara Boumerdes Dept Automat Appl Automat Lab Boumerdes Algeria ECE Paris Sch Engn Paris France Univ Versailles St Quentin Yvelines LISV Lab Versailles France Univ Sci & Technol Houari Boumediene Bab Ezzouar Algeria

The graph coloring problem (GCP) is one of the most interesting classical combinatorial optimization problems in graph theory. It is known to be an NP-Hard problem, so many heuristic algorithms have been employed to solve this problem. In this article, the authors propose a new enhanced binary dragonfly algorithm to solve the graph coloring problem. The binary dragonfly algorithm has been enhanced by introducing two modifications. First, the authors use the Gaussian distribution random selection method for choosing the right value of the inertia weight w used to update the step vector (Delta X). Second, the authors adopt chaotic maps to determine the random parameters s, a, c, f, and e. The aim of these modifications is to improve the performance and the efficiency of the binary dragonfly algorithm and ensure the diversity of solutions. The authors consider the well-known DIMACS benchmark graph coloring instances to evaluate the performance of their algorithm. The simulation results reveal the effectiveness and the successfulness of the proposed algorithm in comparison with some well-known algorithms in the literature.

关键词： binary dragonfly algorithm Chaotic Maps Combinatorial Optimization Problem dragonfly algorithm Graph Coloring Problem

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Performance Improvement for Optimum Positioning of PMUs using binary dragonfly algorithm and Loss Minimization of Transmission Network

Performance Improvement for Optimum Positioning of PMUs usin...

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Renewable Energy Integration into Smart Grids: A Multidisciplinary Approach to Technology Modelling and Simulation (ICREISG), International Conference on

作者： Sadasiva Behera N. B. Dev Choudhury Debasis Tripathy Rajesh Panda Sanjeev Kumar Bhagat National Institute of Technology Silchar Assam India C.V. Raman College of Enggineering Bhubaneswar India

ISBN: (数字)9781728170343

ISBN: (纸本)9781728170350

In recent years, Phasor measurement unit (PMU) is found to be one of the most promising technology in the field of monitoring and controlling of electrical grids. PMUs are to be used to achieve accurate measurements of current and voltage phasors at synchronized time stamp through global positioning system. The high installation costs associated with PMU have restricted its use and increased the requirement for optimum allocation of PMUs. This paper proposes a methodology to solve the optimal PMU placement (OPP) problem in order to obtain a completely observable network by using binary dragonfly algorithm (BDA). A number of simulations are carried out on various standard IEEE bus test systems to ensure the reliability and feasibility of the algorithm. The results obtained are compared between the proposed method and other algorithm. The comparison demonstrates the potential of the proposed algorithm with lower CPU time and better convergence. The OPP also conforms the reduction in transmission losses.

关键词： Phasor Measurment Units binary dragonfly algorithm Phasor measurement units transmission networks performance boost timestamp Anisoptera optimal allocation algorithms CPU time 4-(2-oxapentadeca-4-yne)phenylpropanoic acid

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binary dragonfly optimization for feature selection using time-varying transfer functions

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KNOWLEDGE-BASED SYSTEMS 2018年 161卷 185-204页

作者： Mafarja, Majdi Aljarah, Ibrahim Heidari, Ali Asghar Faris, Hossam Fournier-Viger, Philippe Li, Xiaodong Mirjalili, Seyedali Birzeit Univ Dept Comp Sci Birzeit Palestine Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran Harbin Inst Technol Shenzhen Sch Nat Sci & Humanities HIT Campus Shenzhen Peoples R China RMIT Univ Sch Sci Melbourne Vic Australia Griffith Univ Inst Integrated & Intelligent Syst Brisbane Qld 4111 Australia

The dragonfly algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the binary dragonfly algorithm (RDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches.

关键词： Feature selection Optimization binary dragonfly algorithm Classification Transfer functions

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HBDFA: An intelligent nature-inspired computing with high-dimensional data analytics

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第4期83卷 11573-11592页

作者： Dinc, Baris Kaya, Yasin Adana Alparslan Turkes Sci & Technol Univ Dept Comp Engn TR-01250 Adana Turkiye Adana Alparslan Turkes Sci & Technol Univ Dept Artificial Intelligence Engn TR-01250 Adana Turkiye

The rapid development of data science has led to the emergence of high-dimensional datasets in machine learning. The curse of dimensionality is a significant problem caused by high-dimensional data with a small sample size. This paper proposes a novel hybrid binary dragonfly algorithm (HBDFA) in which a distance-based similarity evaluation algorithm is embedded before the dragonfly algorithm (DA) searching behavior to select the most discriminating features. The two-step feature selection mechanism of HBDFA enables the method to explore the feature space reduced by the distance-based similarity evaluation algorithm. The model was evaluated on two datasets. The first dataset contained 200 reports from 4 evenly distributed categories of Daily Mail Online: COVID-19, economy, science, and sports. The second dataset was the publicly available Spam dataset. The proposed model is compared with binary versions of four popular metaheuristic algorithms. The model achieved an accuracy rate of 96.75% by reducing 66.5% of the top 100 features determined on the first dataset. Results on the Spam dataset reveal that HBDFA gives the best classification results with over 95% accuracy. The experimental results show the superiority of HBDFA in searching high-dimensional data, improving classification results, and reducing the number of selected features.

关键词： binary dragonfly algorithm COVID-19 Text mining Nature inspired algorithms Feature selection

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Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

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Computers, Materials & Continua 2024年第1期78卷 1207-1225页

作者： Zahid Farooq Khan Muhammad Ramzan Mudassar Raza Muhammad Attique Khan Khalid Iqbal Taerang Kim Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

关键词： Feature fusion Darknet-53 Xception binary dragonfly algorithm ensemble

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