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Improved gravitational search algorithm and novel power flow prediction network for multi-objective optimal active dispatching problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 223卷

作者： Qian, Jie Wang, Ping Chen, Gonggui Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China

Multi-objective optimal active dispatching (MO-OAD) is a high-profile topic in power grid optimizations. Traditional methods are insufficient for the MO-OAD problem due to its nonconvexity and strict constraints. Therefore, an improved multi-objective gravitational search algorithm (IMGSA) with better population diversity and search capability is put forward in this paper. Five MO-OAD experiments considering various user requirements demonstrate that the IMGSA achieves preferable Pareto-front (PF) over the non-dominated sorting genetic algorithm-II (NSGA-II) and other recently published algorithms. More importantly, to further explore potential elite schemes after obtaining the best compromise scheme (BCS) of IMGSA, an efficient power flow prediction model based on the radial basis function (RBF) network is proposed for the first time. By accurately mapping the nonlinear relationship between the control variables of MO-OAD and optimization objectives, more than five elite dispatching schemes are determined by the suggested RBF prediction model. These elite schemes greatly reduce the fuel cost, power loss and exhaust emission of different IEEE power systems. Quantitative evaluations such as generational distance (GD) and hyper-volume (HV) indicators prove that the proposed IMGSA with RBF prediction model (IMGSA-RBF) is more competitive than many existing algorithms. Detailed MO-OAD experiments show that IMGSA-RBF method has significant advantages in PF-uniformity, PF-diversity, and the quality of optimal dispatching schemes. In general, the innovative IMGSA-RBF method provides a valuable technology to realize desirable power grid operation with less carbon emission and better economy.

关键词： Optimal active dispatching Power flow prediction Multi -objective optimization gravitational search algorithm Power grid optimization

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gravitational search algorithm for Estimating the Kinetic Parameters of the Propane Pre-reforming Reaction 6

Gravitational Search Algorithm for Estimating the Kinetic Pa...

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6th International Conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Enikeeva, Leniza Enikeev, Marat Gubaydullin, Trek Shamshovich, Valentina Novosibirsk State Univ Novosibirsk Russia Ufa State Petr Technol Univ Ufa Russia Russian Acad Sci Inst Petrochem & Catalysis Moscow Russia

ISBN: (纸本)9781728170411

The article describes a gravitational search algorithm and its application to solving the inverse problem of chemical kinetics. The relevance of the study of metaheuristic algorithms, including the gravitational search algorithm, is given. It is shown that recently, these algorithms are becoming increasingly popular. The optimization problem is formulated on the example of solving the inverse kinetic problem. The process under study is propane pre-reforming into methane-rich gas over Ni catalyst, which is an industrially important chemical process. The description of the algorithm and its pseudo-code are presented, after which the performance of the gravitational search algorithm is compared with other metaheuristic methods. The algorithm demonstrated its competitiveness, as a result of which it was applied to solve a specific industrial problem. Using this algorithm, the inverse problem of chemical kinetics was solved, and the optimal values of the kinetic parameters of the reaction were found. It was proved that the model correctly describes the available experimental data.

关键词： mathematical modeling chemical kinetics gravitational search algorithm

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An image segmentation method using logarithmic kbest gravitational search algorithm based superpixel clustering

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EVOLUTIONARY INTELLIGENCE 2021年第3期14卷 1293-1305页

作者： Mittal, Himanshu Saraswat, Mukesh Jaypee Inst Informat Technol Noida India

Image segmentation partitions an image into coherent and non-overlapping regions. Due to variations of visual patterns in images, it is a challenging problem. This paper introduces a new superpixel-based clustering method to efficiently perform the image segmentation. In the proposed method, initially superpixels from an image are obtained. The superpixels are further clustered into the required number of regions by a newly proposed variant of gravitational search algorithm namely;logarithmic kbest gravitational search algorithm. Experiments are conducted on the Berkeley Segmentation Dataset and Benchmark (BSDS500). It is affirmed from both visual and numerical analyses that the proposed method is efficacious and accurate in segmenting an image than the other considered segmentation methods.

关键词： Superpixel clustering gravitational search algorithm Kmeans BSDS500

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Levy flight and chaos theory-based gravitational search algorithm for mechanical and structural engineering design optimization

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OPEN COMPUTER SCIENCE 2021年第1期11卷 509-529页

作者： Rather, Sajad Ahmad Bala, Perumal Shanthi Pondicherry Univ Sch Engn & Technol Dept Comp Sci Pondicherry India

The main aim of this article is to explore the real-life problem-solving potential of the proposed Levy flight-based chaotic gravitational search algorithm (LCGSA) for the minimization of engineering design variables of speed reducer design (SRD), three bar truss design (TBTD), and hydrodynamic thrust bearing design (HTBD) problems. In LCGSA, the diversification of the search space is carried out by Levy flight distribution. Simultaneously, chaotic maps have been utilized for the intensification of the candidate solutions towards the global optimum. Moreover, the penalty function method has been used to deal with the non-linear and fractional design constraints. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plot analysis. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, eleven heuristic algorithms were employed for comparative analysis of the simulation results. The simulation outcomes clearly show that LCGSA provides better values for TBTD and HTBD benchmarks than standard GSA and most of the competing algorithms. Besides, all the participating algorithms, including LCGSA, have the same results for the SRD problem. On the qualitative side, LCGSA has successfully resolved entrapment in local minima and convergence issues of standard GSA.

关键词： gravitational search algorithm chaotic maps Levy flight distribution engineering design optimization swarm intelligence

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Planning of distribution system considering residential roof top photovoltaic systems, distributed generations and shunt capacitors using gravitational search algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2023年第PartB期111卷

作者： Roy, Arindam Verma, Vimlesh Gampa, Srinivasa Rao Bansal, R. C. Natl Inst Technol Patna Dept Elect Engn Patna 800005 Bihar India Seshadri Rao Gudlavalleru Engn Coll Dept Elect & Elect Engn Gudlavalleru 521356 Andhra Prades India Univ Sharjah Dept Elect Engn POB 27272 Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

In this paper a gravitational search algorithm (GSA) based multiobjective approach is proposed for optimum sizing and allocation of distributed generations (DGs) and shunt capacitors (SCs) for distribution systems integrated with roof top photo voltaic systems (PV). The capacity of roof top Solar PV systems is designed based on the basic energy consumption requirements of a two-bed room apartment. A normalized multiobjective function is formulated considering the objectives of minimizing substation real power, reactive power, real power loss minimization and voltage profile improvement. The optimum DGs and SCs for three seasons summer, rainy and winter are obtained using GSA by minimizing the multiobjective function to reduce the burden on the substation and simultaneously improving the distribution system performance. A practical 52 -bus, 11 kV rural radial distribution system is used to demonstrate the simulation results to show the advantage of the proposed methodology in improving the distribution system performance.

关键词： Distributed generations Shunt capacitors Rooftop solar photovoltaic systems gravitational search algorithm Substation power supply reduction Emission reduction

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An adaptive position-guided gravitational search algorithm for function optimization and image threshold segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Guo, Anjing Wang, Yirui Guo, Lijun Zhang, Rong Yu, Yang Gao, Shangce Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Zhejiang Peoples R China Nanjing Univ Posts & Telecommun Coll Automat & Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan

gravitational search algorithm is a population-based optimization method. To address its low search per-formance and premature convergence, a novel variant called adaptive position-guided gravitational search algorithm is proposed. It utilizes the best, worst and other particles' position information to adaptively determine the Kbest particles which provide a good movement direction. The gravitational force is reinforced by Kbest particles and new constructed Dbest particles to improve the exploration and exploitation abilities. Various particles' position information jointly provide the effective search guideline and accelerate the convergence rate. Validations are conducted to firstly discuss the parameters and strategies of the proposed algorithm. Then, compared with several state-of-the-art gravitational search algorithm variants on CEC2017 benchmark functions, the proposed algorithm proves its superiority. Finally, the proposed algorithm exhibits the good segmentation effect on image threshold segmentation problems.

关键词： gravitational search algorithm Position Exploration and exploitation Function optimization Image threshold segmentation

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Locally informed gravitational search algorithm with hierarchical topological structure

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期123卷

作者： Xiao, Leyi Fan, Chaodong Ai, Zhaoyang Lin, Jie Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China Quanzhou Normal Univ Fujian Prov Key Lab Data Intens Comp Quanzhou 362000 Peoples R China AnHui Polytech Univ AnHui Prov Key Lab Detect Technol & Energy Saving Wuhu 241000 Peoples R China Xihua Univ Vehicle Measurement Control & Safety Key Lab Sichu Chengdu 610039 Peoples R China Hunan Univ Inst Cognit Control & Biophys Linguist Coll Foreign Languages Changsha 410082 Peoples R China

Recently, gravitational search algorithm (GSA) has been successfully applied to solve various optimization problems. However, GSA tends to fall into local optimum because it ignores the environmental heterogeneity of agents. Therefore, locally informed gravitational search algorithm (LIGSA) based on neighborhood structure is proposed to balance exploration and exploitation. However, LIGSA ignores the differences in the evolutionary states between agents in the same neighborhood, and also ignores the differences in the evolutionary states between neighbors, which affects the performance of the algorithm. Therefore, a hierarchical locally informed gravitational search algorithm (HLIGSA) is proposed in this paper. This algorithm designs a hierarchical topology. In the lower layer, there are several non-overlapping neighborhoods, which constitute the whole population. Each agent in the neighborhood adaptively adjusts the gravitational constant according to its evolutionary state, so as to fully search the region of the neighborhood. The upper layer is a population composed of the best agents in each neighborhood, which performs gravity strategy or merging strategy for those neighborhoods that have lost the evolutionary capability, so as to balance the algorithm's exploration and exploitation. The two layers work together to complete the entire search process. Experimental results show that HLIGSA outperforms many variants of GSA and many current state-of-the-art heuristic algorithms.

关键词： gravitational search algorithm Hierarchical topology Self-adaptive mechanism gravitational constant Neighborhood learning

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Optimal Settings of Volt-Var Control of PV Smart Inverter in Active Distribution Networks Using gravitational search algorithm

Optimal Settings of Volt-Var Control of PV Smart Inverter in...

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IEEE International Future Energy Electronics Conference (IFEEC)

作者： Lin, Wei-Chen Huang, Wei-Tzer Yao, Kai-Chao Hsieh, Chien-Wen Tsai, Chin-Tang Ma, Chun-Chiang Natl Changhua Univ Educ NCUE Changhua Taiwan

ISBN: (纸本)9781665434485

The techniques of distribution system voltage control, such as on-load tap changer (OLTC), step voltage regulator (SVR), switched capacitor bank (SCB), smart inverter, etc., work in distribution system to maintain the voltage profile. Due to the increasing penetration of photovoltaic (PV) and its intermittent nature, the voltage fluctuation in distribution system becomes more severe and even arise the voltage violation if the controls of them are not coordinated well. The smart inverter with flexible reactive power control plays an important role to mitigate the voltage variation, but the settings of the volt-var control curve of each one which distributed in the feeder will determine the coordination of the voltage control. The tap changed times of SVR, switched times of SCB, and bus voltage variations can be improved if each control curve of PV smart inverters are set properly. In this paper, the settings of volt-var control curve will be optimized and analyzed by gravitational search algorithm, and the outcomes will be a good suggestion for distribution system operators to formulate the voltage control strategy.

关键词： voltage control coordination smart inverter OLTC SVR SCB gravitational search algorithm

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Controlled random initialization of search vectors for application of gravitational search algorithm to selective harmonic elimination PWM

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Chatterjee, Shantanu Kaku, Hage Das, Abhijit Dept of Electrical Engineering NIT Arunachal Pradesh India

The prime objective of SHE PWM is to eliminate selected lower order harmonic by solving a set of non-linear transcendental equation while maintaining the fundamental component at a desired satisfactory value. In order to solve these non-linear equations different metaheuristic optimization techniques has been used in recent past. Due to faster convergence rate increased precision and higher probability to calculus based mathematical approximation techniques. In this manuscript an improved initialized gravitational search algorithm has been proposed for a uni-polar 3-phase inverter. The algorithm is improved upon through a controlled random initialization of search agent, specifically for the purpose of solving the set of non-linear transcendental equation in SHEPWM for 3- switching angles. The simulation of the gravitational search algorithm and the proposed improved initialized GSA has been performed in MATLAB software and comparison of different performance parameter like Harmonic distortion, convergence rate etc. has been studied. The simulation results proves that a considerable improvement has been achieved by applying improved initialized GSA algorithm in terms of harmonic mitigation, convergence rate and accuracy in finding out the appropriate switching angles. Finally, the results obtained in simulation are verified through an appropriate experimental setup. © 2024 The Authors

关键词： gravitational search algorithm Harmonics Inverter Pulse width modulation Selective harmonic elimination Soft computing techniques

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Prediction and minimization of blasting flyrock distance, using deep neural networks and gravitational search algorithm, JAYA, and multi-verse optimization algorithms

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Heliyon 2024年第19期10卷 e37876页

作者： Ghojoghi, Eslam Ebrahimi Farsangi, Mohamad Ali Mansouri, Hamid Rashedi, Esmat Mining Engineering Department of Mining Engineering Shahid Bahonar University of Kerman Iran Communication Engineering Faculty of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran

Flyrock represents a significant and fundamental challenge in surface mine blasting, carrying inherent risks to humans and the environment. Consequently, accurate prediction, minimization, and identification of the factors influencing flyrock distance are imperative for effective control and mitigation of its destructive consequences. Machine learning and artificial intelligence methodologies have emerged as viable means to predict and simulate in different scientific fields. This study employs Deep Neural Network in conjunction with three optimization algorithms including the JAYA algorithm, Multi-Verse Optimization algorithm, and gravitational search algorithm to predict blasting flyrock distance. The developed model consists of a combination of seven input parameters, encompassing both blasting design parameters and rock geomechanical properties. The output of the Deep Neural Networks model is the flyrock distance. For the training and testing of the model, a dataset comprising of 245 blasting records, collected from Songun copper mine, Iran, was utilized. The DNN model yielded an R2 value of 0.96 and an MSE value of 34.11. These results demonstrate the high accuracy and predictive capability of the model. Furthermore, the application of three optimization algorithms resulted in similar optimized parameter values, which minimized flyrock distances. © 2024 The Authors

关键词： Blasting Deep neural Network Flyrock distance gravitational search algorithm JAYA algorithm Multi-verse optimization algorithm

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