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Internet of medical things-load optimization of power flow based on hybrid enhanced grey wolf optimization and dragonfly algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 98卷 319-330页

作者： Shilaja, C. Arunprasath, T. Kalasalingam Acad Res & Educ Dept Elect & Elect Engn Krishnankoil India

In hybrid renewable energy system, optimization and control are more complex and non-linearity in nature. This research presented a hybrid algorithm based on Enhance grey wolf optimization and algorithm of dragonfly algorithm for handling OPF (optimal power flow) issues. The hybrid algorithm is proposed for solving the minimization of fuel cost, power loss and voltage deviation. The Renewable energy are incorporated with solar and wind energy. To forecast the wind and solar photovoltaic power output the weibull distribution function are modelled. The traditional method is slow and incapable to solve non-linearity problems. The proposed method is fast and effective and it is experimented on IEEE 30 bus system by comparing with recent existing methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hybrid renewable energy system Enhance grey wolf optimization dragonfly algorithm Optimal power flow IEEE30 bus system

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A Novel Method for Multilevel Color Image Segmentation Based on dragonfly algorithm and Differential Evolution

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IEEE ACCESS 2019年 7卷 19502-19538页

作者： Xu, Lang Jia, Heming Lang, Chunbo Peng, Xiaoxu Sun, Kangjian Northeast Forestry Univ Coll Engn & Technol Harbin 150040 Heilongjiang Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

Multilevel thresholding is a simple and powerful image segmentation method that has received widespread attention in recent years. However, the accuracy and stability of thresholding techniques are still not ideal and cannot meet the needs of engineering problems. Therefore, this paper presents a memetic algorithm of dragonfly algorithm (DA) and differential evolution (DE) for color image segmentation, which is known as improved DA (IDA). On the one hand, DA algorithm has a satisfied capability of avoiding convergence to the local optimum, thus it is served as a global search technique. On the other hand, the DE algorithm is adopted as a local search technique, which can increase the precision of solutions. In this paper, two thresholding techniques, namely, Otsu and minimum cross entropy (MCE) methods are used to determine the optimal threshold values. In order to evaluate the performance of the proposed method, we conduct a series of experiments on color images from the Berkeley database and the results are compared with five state-of-the-art meta-heuristic algorithms. Besides, a non-parametric Wilcoxon's rank sum test is also included for statistical analysis. From the experimental results, it is found that IDA-based method outperforms other compared methods in terms of average fitness values, standard deviation, peak signal to noise ratio, structural similarity index, and feature similarity index. The promising results indicate that the application of IDA-based thresholding technique is potential and meaningful.

关键词： Memetic algorithm dragonfly algorithm differential evolution image segmentation minimum cross entropy Otsu method

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Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3987-3998页

作者： Khalilpourazari, Soheyl Khalilpourazary, Saman Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran Urmia Univ Technol Dept Mech Engn Orumiyeh Iran

Grinding is one of the most commonly used operations in the industry. Optimization of the grinding process can significantly improve the product quality and minimize operational costs and production time. Due to nonlinearity and complexity, optimization of the grinding process is one of the most challenging tasks in the field of mechanical engineering. This paper aims to optimize grinding process considering a tri-objective mathematical model to simultaneous optimization of final surface quality, grinding cost and total process time. To obtain non-dominated Pareto optimal solutions, a novel meta-heuristic algorithm named multi-objective dragonfly algorithm is utilized. Besides, an efficient constraint handling technique is implemented to handle complex operational constraints. Furthermore, an experimental case study is solved using the proposed algorithm and the results are compared to NSGA-II in the literature. The results revealed that the proposed algorithm is able to find non-dominated Pareto optimal solutions and make a significant improvement over the existing approaches.

关键词： dragonfly algorithm Grinding process Constrained optimization Multi-objective optimization Taguchi method Robust design

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An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

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IET IMAGE PROCESSING 2021年第9期15卷 2080-2096页

作者： Narasimhulu, C. Venkata Lords Inst Engn & Technol Elect & Commun Engn Hyderabad Telangana India

In medical imaging, the automatic diagnosis of kidney carcinoma has become more difficult because it is not easy to detect by physicians. Pre-processing is the first identification method to enhance image quality, remove noise and unwanted components from the backdrop of the kidneys image. The pre-processing method is essential and significant for the proposed algorithm. The objective of this analysis is to recognize and classify kidney disturbances with an ultrasound scan by providing a number of substantial content description parameters. The ultrasound pictures are prepared to protect the interest pixels before extracting the feature. A series of quantitative features were synthesized of each images, the principal component analysis was conducted for minimizing the number of features to produce set of wavelet-based multi-scale features. dragonfly algorithm (DFA) was executed in this method. In the proposed work, the design and training of a random decision forest classifier and selected features are implemented. The classification of e-health information using ideal characteristics is used by the RF classifier. The proposed technique is activated in MATLAB/simulink work site and the experimental results show that the peak accuracy of the proposed technique is 95.6% using GWO-FFBN techniques compared to other existing techniques.

关键词： automatic diagnosis interest pixels automatic feature selection kidney disturbances kidneys image image quality Patient diagnostic methods and instrumentation RF classifier random forest classifier identification method Sonic and ultrasonic radiation (medical uses) Biology and medical computing Computer vision and image processing techniques pre-processing method dragonfly algorithm Optical, image and video signal processing Sonic and ultrasonic radiation (biomedical imaging/measurement) wavelet-based multiscale features ultrasound pictures random decision forest classifier image classification principal component analysis quantitative features ultrasound kidney images wavelet transforms feature extraction biomedical ultrasonics medical imaging patient diagnosis kidney carcinoma medical image processing kidney Probability theory, stochastic processes, and statistics ultrasound scan unwanted components substantial content description parameters Other topics in statistics

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Modified dragonfly algorithm based multilevel thresholding method for color images segmentation

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2019年第6期16卷 6467-6511页

作者： Peng, Xiaoxu Jia, Heming Lang, Chunbo Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

Accurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In order to overcome this problem, an modified dragonfly algorithm (MDA) is proposed to determine the optimal combination of different levels of thresholds for color images. Chaotic mapping and elite opposition-based learning strategies (EOBL) are used to improve the randomness of the initial population. The hybrid algorithm of dragonfly algorithms (DA) and Differential Evolution (DE) is used to balance the two basic stages of optimization: exploration and development. Kapur entropy, minimum cross-entropy and Otsu method are used as fitness functions of image segmentation. The performance of 10 test color images is evaluated and compared with 9 different meta-heuristic algorithms. The results show that the color image segmentation method based on MDA is more effective and accurate than other competitors in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM). Friedman test and Wilcoxon's rank sum test are also performed to assess the significant difference between the algorithms.

关键词： dragonfly algorithm multilevel thresholding Kapur's entropy minimum cross entropy Otsu method elite opposition-based learning differential evolution

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Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils

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ENGINEERING WITH COMPUTERS 2021年第1期37卷 437-447页

作者： Moayedi, Hossein Abdullahi, Mu'azu Mohammed Nguyen, Hoang Rashid, Ahmad Safuan A. Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Univ Hafr Al Batin Civil Engn Dept Hafar al Batin 39524 Eastern Provinc Saudi Arabia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Teknol Malaysia Fac Engn Sch Civil Engn Ctr Trop Geoengn Geotrop Johor Baharu Malaysia

By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP) predictive tool for adjusting the connecting weights and biases in predicting the failure probability using seven settlement key factors, namely unit weight, friction angle, elastic modulus, dilation angle, Poisson's ratio, applied stress, and setback distance. As the first result, incorporating both HHO and DA metaheuristic algorithms resulted in higher efficiency of the MLP. Moreover, referring to the calculated area under the receiving operating characteristic curve (AUC), as well as the calculated mean square error, the DA-MLP (AUC = 0.942 and MSE = 0.1171) outperforms the HHO-MLP (AUC = 0.915 and MSE = 0.1350) and typical MLP (AUC = 0.890 and MSE = 0.1416). Furthermore, the DA surpassed the HHO in terms of time-effectiveness.

关键词： Bearing capacity analysis dragonfly algorithm Evolutionary science

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Heuristic dragonfly algorithm for optimal design of truss structures with discrete variables

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STRUCTURES 2021年 29卷 843-862页

作者： Jawad, Farqad K. J. Mahmood, Mohammed Wang, Dansheng AL-Azzawi, Osama Al-Jamely, Anas Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China Univ Diyala Dept Civil Engn Diyala Iraq Huazhong Univ Sci & Technol Sch Mech Engn Wuhan Peoples R China

The main purpose of this paper is to obtain more appropriate design for truss structures using a discrete novel nature-inspired optimization algorithm. The so-called dragonfly algorithm (DA) was employed to achieve the aim of this paper. The inspiration of the DA emerges from behaviors of static and dynamic swarming of dragonflies in the nature. The DA was originally developed for continuous optimization problems. This paper proposes some amendments to the DA for solving discrete functions of optimization problems and improving the performance of the algorithm. The modified algorithm was examined on five well-known planar and spatial steel trusses with discrete sizing variables. The effectiveness and robustness of the proposed algorithm were verified by comparing the new method with various widely-known metaheuristic algorithms. The numerical results demonstrate that the DA is more effective and accurate than other algorithms in terms of achieving the lightest weight with minimum number of structural analysis and satisfying the required structural constraints.

关键词： Truss optimization dragonfly algorithm Discrete variables Structural design Size optimization

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Wrapper scan chain design algorithm for testing of embedded cores based on chaotic dragonfly algorithm

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 369-379页

作者： Zhou, Tian Hu, Cong Zhu, Aijun Xu, Chuanpei Wan, Chunting Guilin Univ Aerosp Technol Sch Elect Informat & Automat Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China

As embedded pre-designed and pre-validated cores in system-on-chip (SoC) designs have increased usage, the wrapper scan chain design (WSCD) for the embedded cores is one of the fundamental ways to reduce the SoC test time. In this paper, a chaotic dragonfly algorithm (CDA) for WSCD is proposed to minimize the test time of embedded cores by balancing the packaged scan chains (WSCs).Since the WSCD problem is non-continuous, we improve the dragonfly algorithm (DA) with integer coding to make it suitable for the WSCD problem. In order to improve population diversity and prevent falling into local optimum state, we introduce chaotic strategy into DA. Furthermore, a repaired operator that considers the specific knowledge is added to the DA. Since the CDA is a swarm intelligence method, it is expected to effectively solve the NP-hard problem. The experimental results on ITC'02 SoC benchmark show that the proposed algorithm can improve the balanced results and shorten the test time compared with the existing algorithms.

关键词： SoC testing Wrapper scan chain design Chaotic map dragonfly algorithm

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Hybridization of chaos theory and dragonfly algorithm to maximize spatial area coverage of swarm robots

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EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1327-1340页

作者： Singh, Amrit Pal Kumar, Gaurav Dhillon, Guneet Singh Taneja, Harsh Guru Gobind Singh Indraprastha Univ Univ Sch Automat & Robot New Delhi India Guru Gobind Singh Indraprastha Univ USICT New Delhi India SVKMs NMIMS Univ Sch Technol Management & Engn Navi Mumbai India

Swarm robotics show collective behavior in order to work in multi agent scenario, where spatial area coverage is an emergent area of research. This work introduces a hybrid technique for optimized spatial area coverage. A combination of dragonfly algorithm (DA) and chaotic mapping is proposed and the entire study is carried out in two phases. In the first phase, DA's parameters are measured on the basis of percentage of area covered, entropy and number of pop-up threats detected. In the second phase, Chaotic distribution is applied in swarm algorithms (i.e. DA along with Bat algorithm and Accelerated Particle Swarm Optimization) to implement hybridized models. Chaotic dragonfly algorithm along with Chaotic Bat algorithm and Chaotic Accelerated Particle Swarm Optimization) is implemented. To evaluate the performance of hybridized models, a comprehensive comparison is drawn among the Levy and Chaotic versions of all three swarm algorithms. It is concluded that the proposed DA outperforms the rest. DA not only showed better results for the mentioned metrics, but also displayed uniform behavior over multiple runs.

关键词： Swarm intelligence Swarm robots dragonfly algorithm Chaotic distribution Area coverage Bat algorithm

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Cross-layer optimization using Taylor-Multi-Verse dragonfly algorithm to support interoperability in heterogeneous Internet of Things network

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JOURNAL OF CONTROL AND DECISION 2024年

作者： Desai, Jyoti Ramachandra Dammur, Annapurna PES Inst Technol Dept CSE Bangalore South Campus Bangalore India PES Inst Technol ISE Dept Bangalore India

Interoperability acts as a major issue in heterogeneous Internet of Things (IoT), nowadays. In prior researches, the interacted IoT devices are not feasible without monitoring the services coordination as well as interoperability. Thus, the Taylor-Competitive Multi-Verse dragonfly algorithm (Taylor CMVDA) is developed to support interoperability in heterogeneous IoT for cross-layer optimisation. In this paper, an optimised path through routing is established in the physical layer, the gateway selection is established in the network layer and the Cluster head (CH) is employed in the data link layer. The CH node and gateway node are selected through an adaptive black hole (BH) dynamic clustering and neighbourhood approach. The routing is performed through the introduced Taylor CMVDA, which is the integration of Competitive Multi-Verse Optimizer (CMVO) and dragonfly algorithm (DA) with the Taylor series. The proposed model attains the throughput, energy, and delay of 0.5492, 0.6768 J and 0.3538 sec.

关键词： Interoperability dragonfly algorithm Multi-Verse Optimizer Taylor series blackhole dynamic clustering

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