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Gradient based hybrid metaheuristics for robust tuning of power system stabilizers

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2018年第Feb.期95卷 47-72页

作者： Peres, Wesley Silva Junior, Ivo Chaves Passos Filho, Joao Alberto Fed Univ Sao Joao del Rei UFSJ Dept Elect Engn Sao Joao Del Rei Brazil Univ Fed Juiz de Fora Dept Elect Engn Juiz de Fora Brazil

Power System Stabilizers are controllers installed on synchronous generators to damp power system oscillations through the excitation control. These controllers can have either a conventional fixed structure composed by stages of gain and phase compensation or a flexible modern structure composed by three bands that correspond to a specific frequency range (low, intermediate and high frequency) in which each band is composed by two branches that are based on differential filters (with a gain, lead-lag blocks and a hybrid block). Power system stabilizers design is a hard and time consuming task and an alternative for tuning controllers is by using optimization methods. This paper presents three hybrid metaheuristics for the robust and coordinated design of power system stabilizers. The tuning procedure is modeled as an optimization problem which aims at maximizing the damping ratio coefficients in closed-loop operation. Robustness requirement is met by using multiple operating scenarios in the design stage. For solving the optimization problem, three metaheuristics (gravitational search algorithm, Bat algorithm and Particle Swarm Optimization) are combined with the Steepest Descent Method for local search capability enhancement. The proposed hybrid algorithms are applied to benchmark systems for validation. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hybrid metaheuristics gravitational search algorithm Bat algorithm Particle swarm optimization Robust design of conventional and modern power system stabilizers

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Enhanced Performance in Trajectory Tracking of a Ball and Plate System using Fractional Order Controller

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IETE JOURNAL OF REsearch 2018年第1期64卷 76-86页

作者： Borah, Manashita Roy, Prasanta Roy, Binoy Krishna NIT Silchar Dept Elect Engn Silchar Assam India Tezpur Univ Dept Elect Engn Tezpur Assam India

This paper presents a comparative analysis of performance between integer order proportional derivative (IOPD) and fractional order proportional derivative (FOPD) controllers for the trajectory tracking of a ball in ball and plate system. The controllers are designed minimising integral square error using Bat algorithm, gravitational search algorithm, and Firefly algorithm. Effectiveness of the controllers is tested by the ability of the ball to track a circular, square, cardioid, and star shaped trajectories. Results verify that FOPD outperforms IOPD when tuned by the same algorithm. Comparisons with reported works show that the proposed controllers provide fairly improved results.

关键词： Ball and plate system Bat algorithm Firefly algorithm Fractional order PD gravitational search algorithm Trajectory tracking

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Fractional gravitational search-radial basis neural network for bone marrow white blood cell classification

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The Journal of Photographic Science 2018年第2期66卷 106-124页

ABSTRACTABSTRACTAutomotive image segmentation systems are becoming an important tool in the medical field for disease diagnosis. The white blood cell (WBC) segmentation is crucial, because it plays an important role in the determination of the diseases and helps experts to diagnose the blood disease disorders. The precise segmentation of the WBCs is quite challenging because of the complex contents in the bone marrow smears. In this paper, a novel neural network (NN) classifier is proposed for the classification of the bone marrow WBCs. The proposed NN classifier integrates the fractional gravitation search (FGS) algorithm for updating the weight in the radial basis function mapping for the classification of the WBC based on the cell nucleus feature. The experimentation of the proposed FGS-RBNN classifier is carried on the images collected from the publically available dataset. The performance of the proposed methodology is evaluated over the existing classifier approaches using the measures accuracy, sensitivity, and specificity. The results show that the classification using the nucleus features alone can be utilized to achieve the classification with the better accuracy. Moreover, the classification performance of the proposed FGS-RBNN is better than the existing classifiers, and it is proved to be the efficacious classifier with a classification accuracy of 95%.

关键词： Radial basis network gravitational search algorithm bone marrow white blood cells fuzzy c-means clustering algorithm

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Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

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REMOTE SENSING 2019年第8期11卷 952-952页

作者： Zhang, Aizhu Sun, Genyun Ma, Ping Jia, Xiuping Ren, Jinchang Huang, Hui Zhang, Xuming China Univ Petr East China Sch Geosci Qingdao 266580 Shandong Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266071 Shandong Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

关键词： image classification coastal wetland morphological attribute profiles multilayer perceptron gravitational search algorithm

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Optimized deep learning neural network predictive controller for continuous stirred tank reactor

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 71卷 782-797页

作者： Deepa, S. N. Baranilingesan, I. Anna Univ Dept Elect & Elect Engn Reg Campus Coimbatore Coimbatore 641046 Tamil Nadu India

In this paper, a deep learning neural network model predictive controller (DLNNMPC) is designed to analyse the performance of a non-linear continuous stirred tank reactor (CSTR) that performs parallel and series reactions. The data generated employing the state space model of CSTR is used to train the designed deep learning neural network controller. Deep Learning Neural Network (DLNN) progresses the training with its weights tuned by the proposed hybrid version of evolutionary algorithms - Particle Swarm Optimization (PSO) and gravitational search algorithm (GSA). The developed hybrid PSO - GSA based DLNN model of continuous stirred tank reactor is employed in this paper for model predictive controller design. The effectiveness of the proposed DLNNMPC tuned by hybrid PSO - GSA for CSTR is validated for its performance on comparison with that of other designed Proportional - Integral (PI) and Proportional - Integral - Derivative (PID) controllers as available in early literatures for the same problem under consideration. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Continuous stirred tank reactor Deep learning neural network controller Model predictive control Particle swarm optimization gravitational search algorithm Controller model

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Adaptive jammer localization in wireless networks

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COMPUTER NETWORKS 2018年第Aug.4期141卷 17-30页

作者： Wang, Tongxiang Wei, Xianglin Fan, Jianhua Liang, Tao PLA Army Engn Univ Grad Sch Nanjing 210007 Jiangsu Peoples R China Nanjing Telecommun Technol Res Inst Nanjing 210007 Jiangsu Peoples R China

The shared nature of wireless communication, the open access to wireless medium, the tightly coupled design principle of wireless network protocol and the scarcity of radio spectrum make wireless networks vulnerable to different types of jammers, which prevents the normal communication among legitimate nodes by occupying wireless channel or corrupting network protocols' working process and poses a serious threat to the stability and security of the network. To tackle this security threat, a few anti-jamming schemes have been put forward including channel hopping, spatial retreat, etc. Among these strategies, jammer localization has attracted much attention in recent years since it is very helpful for jamming-avoidance routing and even jammer elimination. However, existing algorithms mainly focus on the localization of jammers equipped with omnidirectional antennas and fail to handle directional jammers, i.e., those attackers with directional antennas. In order to bridge this gap, this paper puts forward an Adaptive Jammer Localization algorithm (AJLA) which can locating both types of jammers. At first, an identification method is employed to estimate the jammer's antenna type. If a omnidirectional antenna is adopted, typical existing algorithm, such as Centroid Localization (CL), Virtual Force Iteration Localization (VFIL), can be utilized to locate the jammer. Otherwise, an Improved gravitational search algorithm (IGSA) is developed for directional jammer localization. A series of simulation experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that AJLA-IGSA could locate the directional jammer efficiently and accurately. Besides, the complexity of information collection and localization process are also analyzed by experiments. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Wireless network Directional jammer Localization gravitational search algorithm

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A gravitational-Double Layer Extreme Learning Machine and Its Application in Powerlifting Analysis

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IEEE ACCESS 2019年 7卷 143990-143998页

作者： Vinh Huy Chau Anh Thu Vo Ba Tuan Le Ho Chi Minh City Univ Phys Educ & Sport Ho Chi Minh City 700000 Vietnam Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Powerlifting is a strength sport that is quite popular in the world. Powerlifters have their power levels varied at different ages and body weights, and their power levels are closely related to their performance. Therefore, studying the impact of age and weight on the performance of powerlifters is an important work. The traditional method relies mainly on artificial experience to judge the performance, and often does not get the desired results. In recent years, machine learning has developed rapidly, and applying machine learning in sports is a very interesting topic. This study is based on a new machine learning algorithm to construct a prediction model for the best performance of powerlifters. We propose a doublelayer extreme learning machine based on affine transformation and two-layer extreme learning machine theory (AF-DELM). Then use a dynamic weight-gravitational search algorithm to improve the AF-DELM networks. The results show that the algorithm can better predict the performance and provide an effective predictive aid for the powerlifting competition.

关键词： gravitational search algorithm gravitational-double layer extreme learning machine powerlifting performance prediction model.

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Extension of operating air-gap in electromagnetic levitation system by using intelligent controllers

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2018年第4期12卷 526-554页

作者： Banerjee, Subrata Natl Inst Technol Durgapur Dept Elect Engn Durgapur 713209 India

Electromagnetic levitation system (EMLS) is inherently unstable and strongly nonlinear in nature. The parameters of EMLS undergo considerable variation with change in air-gap. This makes the design and implementation of robust controllers difficult. This paper deals with intelligent controllers utilising gravitational search algorithm (GSA) based piecewise linear control and GSA based fuzzy control scheme to extend the range of operating air-gap for an EMLS. Extensive simulation has been carried out for the tuning of controller parameters and it is found the GSA based technique produces improved performance. Robust design of controllers for different zones is confirmed by Kharitonov/Nyquist enclosure. The controllers are tested in a small-scale system in the laboratory confirming the usefulness of the proposed technique. Such systems are useful for magnetic levitation (maglev) based applications those need to operate in a large operating air-gap.

关键词： electromagnetic levitation DC-DC power converters PWM fuzzy control gravitational search algorithm GSA piecewise linear control robust control

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Multi-modality image fusion based on enhanced fuzzy radial basis function neural networks

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2018年 48卷 11-20页

作者： Chao, Zhen Kim, Dohyeon Kim, Hee-Joung Yonsei Univ Coll Hlth Sci Dept Radiat Convergence Engn 1 Yonseidae Gil Wonju 220710 Gangwon South Korea Yonsei Univ Coll Hlth Sci Dept Radiol Sci 1 Yonseidae Gil Wonju 220710 Gangwon South Korea

In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. Recently, neural network technique was applied to medical image fusion by many researchers, but there are still many deficiencies. In this study, we propose a novel fusion method to combine multi-modality medical images based on the enhanced fuzzy radial basis function neural network (Fuzzy-RBFNN), which includes five layers: input, fuzzy partition, front combination, inference, and output. Moreover, we propose a hybrid of the gravitational search algorithm (GSA) and error back propagation algorithm (EBPA) to train the network to update the parameters of the network. Two different patterns of images are used as inputs of the neural network, and the output is the fused image. A comparison with the conventional fusion methods and another neural network method through subjective observation and objective evaluation indexes reveals that the proposed method effectively synthesized the information of input images and achieved better results. Meanwhile, we also trained the network by using the EBPA and GSA, individually. The results reveal that the EBPGSA not only outperformed both EBPA and GSA, but also trained the neural network more accurately by analyzing the same evaluation indexes.

关键词： Multi-modality medical images Image fusion Enhanced radial basis function neural network Error back propagation algorithm gravitational search algorithm A hybrid of error back propagation and gravity search algorithm

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CGSA scheduler: A multi-objective-based hybrid approach for task scheduling in cloud environment

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INFORMATION SECURITY JOURNAL 2018年第2期27卷 77-91页

作者： Pradeep, K. Jacob, T. Prem Sathyabama Univ St Josephs Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India Sathyabama Univ Dept Comp Sci & Engn Madras Tamil Nadu India

In cloud computing task scheduling is one of the important processes. The key problem of scheduling is how to allocate the entire task to a corresponding virtual machine while maximizing profit. The main objective of this paper is to execute the entire task with low cost, less resource use, and less energy consumption. To obtain the multi-objective function for scheduling, in this paper we propose a hybridization of cuckoo search and gravitational search algorithm (CGSA). The vital design of our approach is to exploit the merits of both cuckoo search (CS) and gravitational search algorithms (GSA) while avoiding their drawbacks. The performance of the algorithm is analyzed based on the different evaluation measures. The algorithms like GSA, CS, Particle swarm optimization (PSO), and genetic algorithm (GA) are used as a comparative analysis. The experimental results show that our proposed algorithm achieves the better result compare to the existing approaches.

关键词： CGSA energy consumption genetic algorithm gravitational search algorithm memory usage multi-objective profit task scheduling

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