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Modeling of inverted pendulum system with gravitational search algorithm optimized controller

AIN SHAMS ENGINEERING JOURNAL 2019年第1期10卷 129-149页

作者： Magdy, Mohamed El Marhomy, Abdallah Attia, Mahmoud A. Ain Shams Univ Fac Engn Dept Engn Phys & Math Cairo Egypt Ain Shams Univ Fac Engn Dept Elect Power & Machines Cairo Egypt

The inverted pendulum (IP) is a common classical control theory problem. Many controllers are used to keep the pendulum rod upraise via the control the position of the cart. In this paper, Modelling of IP system is carried out and then verifies the model through comparison with other models in literature. The viscous damping effect on the IP system has been also considered in this work. The PID controller is used to control the IP and is tuned by using both gravitational search algorithm (GSA) and Genetic Algorism (GA). Examination of GSA efficiency has been demonstrated, and then comparison with Genetic Algorism (GA) in the tuning of PID controller is presented. The results proved the superiority of the controller which is tuned by using GSA (PID-GSA). Finally, the optimized controller (PID-GSA) performance is checked through carrying out the robustness test. (C) 2018 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Inverted pendulum PID controller Genetic algorithm gravitational search algorithm D'Alembert's principle

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gravitational search algorithm applied to fixed head hydrothermal power system with transmission line security constraints

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ENERGY 2018年 155卷 392-407页

作者： Ozyon, Serdar Yasar, Celal Dumlupmar Univ Elect & Elect Engn Dept TR-43100 Kutahya Turkey

Optimal operation and production planning are important problems for electric power generation systems. Optimal operation of an energy generation system conventionally refers to the minimization of total thermal fuel cost (TTFC). However, the presence of thermal and hydraulic generation units in a power system requires that hydraulic as well as thermal constraints be added to a problem, which is referred to as a short term hydrothermal coordination problem (STHCP). STHCPs assume that the amount of water in the reservoirs of a hydraulic generation unit has no effect on power generation when its net heads are fixed. This study applied a gravitational search algorithm (GSA) for the solution of a STHCP with fixed head. The probable electrical and hydraulic constraints of the problem have been provided by punishment function method. Line losses of the power system were calculated using the Newton-Raphson load flow method. Two different cases, where transfer capacities of transmission lines were neglected and included, were set up, and the problem was solved for both cases. Transfer capacities of transmission lines were included to make the problem more complex and realistic. Solutions for both cases were discussed. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Fixed head Short term hydrothermal coordination problem gravitational search algorithm Newton-Raphson AC load flow Line transfer capacity

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A modified gravitational search algorithm and its application in lifetime maximization of wireless sensor networks

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第6期27卷 4055-4069页

作者： Ebrahimi Mood, Sepehr Javidi, Mohammad Masoud Shahid Bahonar Univ Kerman Dept Comp Sci Kerman Iran

Recently, academic communities and industrial sectors have been affected by significant advancements in wireless sensor networks (WSNs). Employing clustering methods is the dominant method to maximize the WSN's lifetime, which is considered to be a major issue. Metaheuristic algorithms have attracted wide attention in the research area of clustering. In this paper, first a novel nature-inspired optimization algorithm based on the gravitational search algorithm (GSA) is defined. To control the exploitation and exploration capabilities of this algorithm, along with calculating the masses value, the tournament selection method is employed. Tournament size, the parameter of this method, is computed automatically using a function during the computational process of the algorithm. The abilities of the algorithm are balanced using this problem-independent parameter. Therefore, the performance of the proposed algorithm is improved in this paper. Moreover, a modified GSA is applied to an energy-efficient clustering protocol for WSNs to minimize the objective function defining the compact clusters that have cluster heads with high energy. The proposed search algorithm is evaluated in terms of some standard test functions. The results suggest that this method has better performance than other state-of-the-art optimization algorithms. In addition, simulation results indicate that the proposed method for the clustering problem in WSNs has better performance on network lifetime and delivery data packets in BS than other popular clustering methods.

关键词： Wireless sensor network energy-efficiency protocol clustering method network life gravitational search algorithm tournament selection

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Real-time data text mining based on gravitational search algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 137卷 117-129页

作者： Mosa, Mohamed Atef Inst Publ Adm Dept Informat Technol Riyadh Saudi Arabia Natl Author Remote Sensing & Space Sci Cairo Egypt

Short messages are one of the milestones on the web especially on social media (SM). Due to the widespread circulation of SM, it already turns into excessively painful capturing outmost relevant and significant information for certain users. One of the main motivations of this work is that many users may need an inclusive brief of all comments without reading the entire list of short messages for decision making. In this work, mining in big social media data is formulated for the first time into a multi objective optimization (MOO) task to extract the essence of a text. Since some users may demand the brief at any moment, several groups of dissimilar short messages are established based on graph coloring mechanism. Six interesting feature are formalized to exhibit more interactive messages. A gravitational search algorithm (GSA) is employed to satisfy several important objectives for generating a concise summary. The problem was picked by using the Normal Boundary Intersection (NBI) mechanism to trade-off among different features. Additionally, to satisfy real-time needs, an inventive incremental grouping task is modelled to update the existing colors. From exhaustive experimental results, the proposed approach outperformed other strong comparative methods. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Data text mining Swarm Intelligence Big-data gravitational search algorithm Normal boundary intersection

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Design and implementation of a hybrid MLP-GSA model with multi-layer perceptron-gravitational search algorithm for monthly lake water level forecasting

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STOCHASTIC ENVIRONMENTAL REsearch AND RISK ASSESSMENT 2019年第1期33卷 125-147页

作者： Ghorbani, Mohammad Ali Deo, Ravinesh C. Karimi, Vahid Kashani, Mahsa H. Ghorbani, Shahryar Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Near East Univ Engn Fac Mersin 10 TR-99138 Nicosia North Cyprus Turkey Univ Southern Queensland Sch Agr Computat & Environm Sci Ctr Sustainable Agr Syst Springfield Qld 4300 Australia Univ Southern Queensland Inst Life Sci & Environm Ctr Appl Climate Sci Springfield Qld 4300 Australia Univ Tabriz Dept Water Engn Tabriz Iran Univ Mohaghegh Ardabili Dept Water Engn Ardebil Iran Istanbul Gedik Univ Grad Sch Social Sci Istanbul Turkey

Lakes are primitive water holding geographicstructures containing most thefresh water on the Earth's surface, but the recent trends show that climate change can potentiallylead to a significant aberration in the Lake water level and itsoverall pristine state, and therefore, could alsothreaten the source of freshwater. The ability to forecast thelake water is a paramount decision-making and risk-reduction task, and this isrequired to retain the sustainability of the natural environment, and to reduce the risk tothe local and globalfood chain, recreationactivities, agriculture and ecosystems. In this study, we have designed and evaluated a new hybrid forecasting model, integrating the gravitational search algorithm (GSA), as a heuristic optimization tool, with the Multilayer Perceptron (MLP-GSA) algorithm to forecast water level in Winnipesaukee and Cypress Lakes in the United States of America. The performance of the resulting hybrid MLP-GSA model is benchmarked and compared with the traditional MLP trained with Levenberg-Marquadt back propagation learning algorithm, two other intelligent hybrid models (MLP-PSO and MLP-FFA) and also two stochastic models namely, ARMA and ARIMA models. In this case study, the monthly time scale water level data from 1938 to 2005 and 1942 to 2011 for the Lakes Winnipesaukee and Cypress, respectively, were applied to train and evaluate the MLP-GSA model. The best input combinations of the standalone (MLP) and the hybrid MLP-GSA forecasting models were determined by sensitivity analysis of historical water level training data for 1-month lead forecasting. The hybrid MLP-GSA model was evaluated independently with statistical score metrics: coefficient of correlation, coefficient of efficiency, the root mean square and relative root mean square errors, and the Bayesian Information Criterion. The results showed that the hybrid MLP-GSA4 and MLP-GSA5 model (where the 4 and 5months' of lagged input combinations of Lake water level data were ut

关键词： MLP gravitational search algorithm Hybrid models ARMA ARIMA Lake Winnipesaukee Lake Cypress Water level

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MIMR-DGSA: Unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm

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INFORMATION FUSION 2019年 51卷 189-200页

作者： Tschannerl, Julius Ren, Jinchang Yuen, Peter Sun, Genyun Zhao, Huimin Yang, Zhijing Wang, Zheng Marshall, Stephen Univ Strathclyde Ctr Signal & Image Proc Glasgow Lanark Scotland Cranfield Univ Ctr Elect Warfare Electroopt Image & Signal Proc Swindon Wilts England China Univ Petr Sch Geosci Qingdao Shandong Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Elect Informat Guangzhou Guangdong Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao Shandong Peoples R China

Band selection plays an important role in hyperspectral data analysis as it can improve the performance of data analysis without losing information about the constitution of the underlying data. We propose a MIMR-DGSA algorithm for band selection by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them. Subsets are generated with a modified Discrete gravitational search algorithm (DGSA) where we definine a neighbourhood concept for feature subsets. A fast algorithm for pairwise mutual information calculation that incorporates variable bandwidths of hyperspectral bands called VarBWFastMI is also developed. Classification results on three hyperspectral remote sensing datasets show that the proposed MIMR-DGSA performs similar to the original MIMR with Clonal Selection algorithm (CSA) but is computationally more efficient and easier to handle as it has fewer parameters for tuning.

关键词： Band selection Discrete optimisation Entropy Evolutionary computation Feature selection gravitational search algorithm Hyperspectral imaging Maximum-Information-Minimum-Redundancy Mutual information

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Hybrid Multiverse Optimization algorithm With gravitational search algorithm for Multithreshold Color Image Segmentation

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IEEE ACCESS 2019年 7卷 44903-44927页

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

Multithreshold segmentation of color images is an important part of modern image processing. Multithreshold segmentation techniques that use the ideas of optimization in mathematics to process the information contained in the three channels of RGB images have received much attention in recent years. In this paper, a hybrid algorithm of multiverse optimization algorithm with a gravitational search algorithm (GSMVO) is proposed. On the one hand, the gravitational mechanism among individuals in the universe enables particles to share information and move toward particles with large mass (the optimal solution), thus optimizing local optimization. On the other hand, the problem of a slow interpopulation update is improved. On the premise of improving the optimization precision, the convergence speed of the whole system is accelerated and the robustness can be improved. The time complexity of a proposed hybrid algorithm is analyzed. The quality of a segmented image is evaluated by using peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and Wilcoxon test. The experimental results illustrate that the proposed method has obvious advantages in objective function value, image quality measurement, convergence performance, and robustness.

关键词： Multiverse optimization algorithm gravitational search algorithm multithreshold segmentation Kapur's entropy Otsu method

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Association rule mining using chaotic gravitational search algorithm for discovering relations between manufacturing system capabilities and product features

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CONCURRENT ENGINEERING-REsearch AND APPLICATIONS 2019年第3期27卷 213-232页

作者： Kou, Zhicong Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm-based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm;the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm-based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm-based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm-based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm-based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

关键词： product features manufacturing system capabilities association rule mining gravitational search algorithm chaos map

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A Memetic Chaotic gravitational search algorithm for unconstrained global optimization problems

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APPLIED SOFT COMPUTING 2019年 79卷 14-29页

作者： Garcia-Rodenas, Ricardo Jimenez Linares, Luis Alberto Lopez-Gomez, Julio Univ Castilla La Mancha Higher Sch Comp Sci Paseo Univ 4 Ciudad Real Spain

Metaheuristic optimization algorithms address two main tasks in the process of problem solving: i) exploration (also called diversification) and ii) exploitation (also called intensification). Guaranteeing a trade-off between these operations is critical to good performance. However, although many methods have been proposed by which metaheuristics can achieve a balance between the exploration and exploitation stages, they are still worse than exact algorithms at exploitation tasks, where gradient-based mechanisms outperform metaheuristics when a local minimum is approximated. In this paper, a quasi-Newton method is introduced into a Chaotic gravitational search algorithm as an exploitation method, with the purpose of improving the exploitation capabilities of this recent and promising population-based metaheuristic. The proposed approach, referred to as a Memetic Chaotic gravitational search algorithm, is used to solve forty-five benchmark problems, both synthetic and real-world, to validate the method. The numerical results show that the adding of quasi-Newton search directions to the original (Chaotic) gravitational search algorithm substantially improves its performance. Also, a comparison with the state-of-the-art algorithms: Particle Swarm Optimization, Genetic algorithm, Rcr-JADE, COBIDE and RLMPSO, shows that the proposed approach is promising for certain real-world problems. (C) 2019 The Author(s). Published by Elsevier B.V.

关键词： Memetic algorithms gravitational search algorithm Quasi-Newton methods

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Extreme Learning Machine Model for State-of-Charge Estimation of Lithium-Ion Battery Using gravitational search algorithm

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2019年第4期55卷 4225-4234页

作者： Lipu, Molla S. Hossain Hannan, Mahammad A. Hussain, Aini Saad, Mohamad H. Ayob, Afida Uddin, Mohammad Nasir Univ Kebangsaan Malaysia Fac Engn & Built Environm Bangi 43600 Malaysia Univ Tenaga Nas Dept Elect Power Engn Kajang 43000 Malaysia Lakehead Univ Fac Engn Thunder Bay ON P7B 5E1 Canada

This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model.

关键词： Electric vehicle extreme learning machine gravitational search algorithm lithium-ion NMC battery state of charge (SOC)

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