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An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2018年第Jul.期114卷 123-134页

作者： Kaswan, Amar Tomar, Abhinav Jana, Prasanta K. Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

Existing studies on wireless sensor networks (WSNs) have revealed that the limited battery capacity of sensor nodes (SNs) hinders their perpetual operation. Recent findings in the domain of wireless energy transfer (WET) have attracted a lot of attention of academia and industry to cater the lack of energy in the WSNs. The main idea of WET is to restore the energy of SNs using one or more wireless mobile chargers (MCs), which leads to a new paradigm of wireless rechargeable sensor networks (WRSNs). The determination of an optimal order of charging the SNs (i.e., charging schedule) in an on-demand WRSN is a well-known NP-hard problem. Moreover, care must be taken while designing the charging schedule of an MC as requesting SNs introduce both spatial and temporal constraints. In this paper, we first present a Linear Programming (LP) formulation for the problem of scheduling an MC and then propose an efficient solution based on gravitational search algorithm (GSA). Our method is presented with a novel agent representation scheme and an efficient fitness function. We perform extensive simulations on the proposed scheme to demonstrate its effectiveness over two state-of-the-art algorithms, namely first come first serve (FCFS) and nearest job next with preemption (NJNP). The simulation results reveal that the proposed scheme outperforms both the existing algorithms in terms of charging latency. The virtue of our scheme is also proved by the well-known statistical test, analysis of variance (ANOVA), followed by post hoc analysis.

关键词： Wireless rechargeable sensor networks On-demand Wireless energy transfer gravitational search algorithm Charging schedule Charging latency

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An optimization algorithm for determining a point heat source position in a 2D domain using a hybrid metaheuristic

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JOURNAL OF INVERSE AND ILL-POSED PROBLEMS 2018年第3期26卷 335-347页

作者： Kurt, Mehmet Gunel, Korhan Bahcesehir Univ Fac Engn & Nat Sci Izmir Turkey Izmir Bahcesehir Sci & Technol High Sch Izmir Turkey Adnan Menderes Univ Fac Arts & Sci Dept Math Aydin Turkey

In this paper, we study an inverse problem of determining the unknown heat source position of a point-wise heat source in a two-dimensional steady-state heat conduction problem governed by a linear elliptic equation with the Dirichlet boundary conditions. The problem is solved by the hybridization of particle swarm optimization and the gravitational search algorithm with a newly defined mutation operator. Some empirical studies are also performed to gauge the accuracy of the proposed approach.

关键词： inverse heat conduction problem swarm intelligence particle swarm optimization gravitational search algorithm

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A novel hybrid model for two-phase image segmentation: GSA based Chan-Vese algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 73卷 22-30页

作者： Cataloluk, Hatice Celebi, Fatih Vehbi Ankara Yildirim Beyazit Univ Dept Comp Engn TR-06010 Ankara Turkey

The active contours without edges model of Chan and Vese (Chan and Vese, 2001), which has been accepted for two-phase image segmentation is one of the most widely-used methods. It is a region-based segmentation model that utilizes the techniques of curve evolvement and the level set method. Chan-Vese model is a strong and flexible method that is able to segment many types of images compared to other active contours. Nevertheless, improper initial contours may reveal the problem of the Chan-Vese model getting stuck in a local minimum. This situation often provides poor results for the Chan-Vese model. Particularly, this problem occurs in the images that have large intensity differences between local and global structures. In this paper, we present a novel hybrid approach to the Chan-Vese algorithm to bring a solution to the problem of segmentation of these images. The proposed approach is based on the gravitational search algorithm (GSA) developed in Rashedi et al. (2009). The idea is to arrange the fitting energy minimization problem according to a heuristic optimization technique and provide satisfactory segmentation outcomes regardless of the choice of the initial contour. The proposed model has been tested on both several images taken from Weizmann dataset and suitable medical images for the local minima problem. Experiments on the suitable test images prove that the proposed GSA based Chan-Vese model is more accomplished and more robust when compared to the conventional Chan-Vese algorithm. The test results also denote that the proposed algorithm requires much smaller number of iterations (%75 less) to converge than the conventional Chan-Vese algorithm.

关键词： Two-phase image segmentation Active contours Level set gravitational search algorithm Chan-Vese model Energy minimization

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PM_2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

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ATMOSPHERIC ENVIRONMENT 2018年 183卷 20-32页

作者： Zhu, Suling Lian, Xiuyuan Wei, Lin Che, Jinxing Shen, Xiping Yang, Ling Qiu, Xuanlin Liu, Xiaoning Gao, Wenlong Ren, Xiaowei Li, Juansheng Lanzhou Univ Sch Publ Hlth Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Sch Math & Stat Tianshuinanlu 222 Lanzhou Gansu Peoples R China Nanchang Inst Technol Sch Sci Nanchang 330099 Jiangxi Peoples R China

The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and wa

关键词： PM2.5 concentrations Support vector regression Grey correlation analysis Particle swarm optimization gravitational search algorithm

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Prediction of Slope Stability Using Four Supervised Learning Methods

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IEEE ACCESS 2018年 6卷 31169-31179页

作者： Lin, Yun Zhou, Keping Li, Jielin Cent South Univ Sch Resources & Safety Engn Changsha 410083 Hunan Peoples R China Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia

The analysis and prediction of slope stability are very important, because slope failure can lead to large disasters. This paper focused on a performance comparison of four supervised learning methods for slope stability prediction. Based on characteristics of slope instability and analysis of data availability, six typical slope parameters-the unit weight, cohesion, internal friction angle, slope inclination, slope height, and pore water ratio-were chosen to establish the evaluation index system. The gravitational search algorithm (GSA), random forest (RF), support vector machine, and naive Bayesian (Bayes) were proposed to establish classifiers. A data set from more than 10 domestic and abroad slope projects was established to train and test the four classifiers, and then, key parameters of the four models were optimized by using the method of 10-fold cross validation. The prediction performances of the four supervised learning methods were compared and analyzed. The results of accuracy, Kappa, and receiver operating characteristic curves reveal that both GSA and RF models can achieve satisfactory results, and the GSA model can obtain the best results when compared with the other three learning methods. Finally, seven models with varying indicators are investigated to obtain the parameter sensitivity based on RF and GSA models.

关键词： Slope stability supervised learning methods gravitational search algorithm 10-fold cross validation ROC curves

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A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting

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APPLIED ENERGY 2018年 215卷 131-144页

作者： Li, Chaoshun Xiao, Zhengguang Xia, Xin Zou, Wen Zhang, Chu Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Huaiyin Inst Technol Coll Automat Huaian 225003 Peoples R China

Wind speed forecasting plays an important role in estimating the power produced from wind farms. However, because of the non-linear and non-stationary characteristics of the wind speed time series, it is difficult to model and predict such series precisely by traditional wind speed forecasting models. In this paper, a novel hybrid modelling method is proposed, in which time series decomposition, feature selection, and basic forecasting model are combined in a synchronous optimisation framework. In this method, the above-mentioned modelling factors, which affect model performance, could make a concerted effort to improve the model. Specifically, variational mode decomposition, the Gram-Schmidt orthogonal, and extreme learning machine, are optimized synchronously by gravitational search algorithm in the proposed hybrid short-term wind speed forecasting model. First, variational mode decomposition is employed to decompose the original wind speed time series into a set of modes and into one bias series. Subsequently, the Gram-Schmidt orthogonal is used to select the important features. Next, the set of modes are forecasted using the ELM. Finally, the key parameters of the models in three stages are optimized synchronously by gravitational search algorithm. Seven data sets from the Sotavento Galicia wind farm and two wind farms in China have been adopted to evaluate the proposed method. The results show that the proposed method achieves significantly better performance than the traditional signal forecasting models both on one-step and multi-step wind speed forecasting with at least 40% average performance promotion over all the seven competitors.

关键词： Wind speed forecasting Variational mode decomposition gravitational search algorithm Extreme learning machine Gram-Schmidt orthogonal Synchronous optimisation

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Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization algorithms

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WATER 2019年第4期11卷 742页

作者： Naganna, Sujay Raghavendra Deka, Paresh Chandra Ghorbani, Mohammad Ali Biazar, Seyed Mostafa Al-Ansari, Nadhir Yaseen, Zaher Mundher Shri Madhwa Vadiraja Inst Technol & Management Dept Civil Engn Bantakal 574115 Udupi India Visvesvaraya Technol Univ Belagavi 590018 Karnataka India Natl Inst Technol Karnataka Dept Appl Mech & Hydraul Surathkal 575025 Mangalore India Near East Univ Dept Civil Engn POB 99138Mersin 10 Nicosia North Cyprus Cyprus Univ Tabriz Fac Agr Dept Water Engn Tabriz *** Iran Lulea Univ Technol Civil Environm & Nat Resources Engn S-97187 Lulea Sweden Ton Duc Thang Univ Fac Civil Engn Sustainable Dev Civil Engn Res Grp Ho Chi Minh City Vietnam

Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro-climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP-FFA and MLP-GSA) were authenticated against standard MLP tuned by a Levenberg-Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones.

关键词： dew point temperature firefly algorithm gravitational search algorithm humid climate hybrid models nature-inspired optimization semi-arid region

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Potential position node placement approach via oppositional gravitational search for fulfill coverage and connectivity in target based wireless sensor networks

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WIRELESS NETWORKS 2017年第6期23卷 1875-1888页

作者： Jehan, C. Punithavathani, D. Shalini Tamizhan Coll Engn & Technol Dept CSE Chenbagaramanputhur Tamil Nadu India Govt Coll Engn Tirunelveli India

Wireless Sensor Network (WSN) has appeared as a powerful technological platform with tremendous and novel applications. Now-a-days, monitoring and target tracking are the most major application in WSNs. In target based WSN, coverage and connectivity are the two most important issues for definite data forwarding from every target to a remote base station. An NP entire issue is to find least number of potential or possible locations to set sensor nodes gratifying both coverage and connectivity from a given a group of target points. In this article, we propose an Oppositional gravitational search algorithm (OGSA) based approach to solve this problem. This approach helps that the sensor nodes are prone to failure, the proposed system provides l-coverage to all targets and n-connectivity to each sensor node. This OGSA based system is presented with agent representation, derivation of efficient fitness function along with the usual gravitational search algorithm operators. The approach is simulated broadly with various scenarios of Wireless Sensor Network. The experimentation results are compared with some relevant existing algorithms to demonstrate the efficiency of the proposed approach.

关键词： Wireless sensor networks Target Potential position n-connectivity l-coverage gravitational search algorithm

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Performance Comparison of Metaheuristic algorithms for the Optimal Design of Space Trusses

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第10期43卷 5265-5281页

作者： Sonmez, Mustafa Aksaray Univ Dept Civil Engn Fac Engn TR-68100 Aksaray Turkey

In this study, eight population-based metaheuristic algorithms were employed for the design of truss structures with continuous design variables. The selected algorithms were genetic, ant colony, particle swarm, artificial bee colony, gravitational search, firefly, gray wolf optimization and Jaya. The purpose was to objectively evaluate the performance of these algorithms under the same conditions and select the best efficient algorithm by assessing three example truss structures. The results obtained from the examples showed that the algorithms were both computationally efficient and robust when the number of design variables was approximately 10 and a significant number of iterations were performed. When the number of design variables was increased to 53, artificial bee colony, Jaya and gray wolf optimization were found to be computationally more effective than the remaining algorithms.

关键词： Genetic algorithm Ant colony optimization Artificial bee colony Gray wolf optimization Firefly algorithms gravitational search algorithm Jaya

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Motor imagery task classification using intelligent algorithm with prominent trial selection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第2期35卷 1501-1510页

作者： Ghosh, Rajdeep Kumar, Vikas Sinha, Nidul Biswas, Saroj Kumar NIT Silchar Comp Sci & Engn Dept Silchar 788010 Assam India

Brain Computer Interface (BCI) enables us to record and process the information generated by the brain and process them. Due to high variability of the Electroencephalogram (EEG) data, multiple trails are recorded for a particular task. The present work aims to improve the accuracy for motor imagery task classification by selecting the most prominent trail from the multiple trails recorded during motor imagery. In this paper, we propose a novel weight optimization algorithm for common spatial filtering (CSP) using evolutionary algorithms (i.e. cuckoo search algorithm (CSA), firefly algorithm (FA) and gravitational search algorithm (GSA)) to select the most prominent trial from the multiple trails recorded for feature extraction. The features extracted from the selected trials were thus used for motor imagery task classification. The performance was evaluated on the extracted features from the selected trials using two classifiers namely linear discriminant analysis (LDA) and support vector machines (SVM). It is observed that FA with band power as a feature gives the best performance in comparison to the earlier reported methods i.e. average, error based and alternating direction method of multipliers (ADMM).

关键词： Brain computer interface common spatial pattern cuckoo search algorithm electroencephalography firefly algorithm gravitational search algorithm linear discriminant analysis support vector machine

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