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7th IEEE International Conference on Control System Computing and Engineering (ICCSCE)

作者： Lee, T. S. Loong, Y. T. Moslemipour, G. Multimedia Univ Fac Engn & Technol Melaka Malaysia PNU Dept Ind Engn Tehran Iran

ISBN: (纸本)9781538638972

A framework based on weighted dispatching rules is proposed in this study in order to obtain optimum job-machine allocation for a bi-objective flow shop system. In the proposed method, bi-objective based dispatching rules are integrated into a single weightage function. Unlike traditional dispatching rules (DRs) which only provide a fixed schedule for a particular job, this method iteratively improves the schedules by finding the optimum weightages for every stage. However, such weightage function is rather complex and attaining an optimum solution is a rather arduous task. Hence, gravitational search algorithm (GSA) optimization is applied to overcome this issue. The proposed method is indeed an improvement over the conventional dispatching rule method and it can be easily implemented in the semiconductor industry, in which flexible flow shop system is the standard practice. A number of random generated cases are used in this study to demonstrate the performance of the framework by introducing a series of weightages in the selected dispatching rules.

关键词： gravitational search algorithm Dispatching rules Flow shop scheduling Multiple Objectives Optimization

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gravitational search algorithm with linearly decreasing gravitational constant for parameter estimation of photovoltaic cells

Gravitational search algorithm with linearly decreasing grav...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Jordehi, A. Rezaee Islamic Azad Univ Dept Elect Engn Lashtenesha Zibakenar Branch Lashtenesha Iran

ISBN: (纸本)9781509046010

Due to undeniable environmental, economical and technical reasons, renewable energy-based power generation in electric power systems is continually increasing. Among renewables, photovoltaic (PV) power generation is a viable and attractive choice. For modeling photovoltaic systems, accurate modeling of PV cells is a must. PV cells are often modeled as single diode or double diode models. The process of estimating circuit model parameters of PV cells based on datasheet information or experimental I-V measurements is called PV cell parameter estimation problem and is being frequently researched in the last three decades. The research effort is being put to achieve more accurate circuit model parameters. In this paper, gravitational search algorithm (GSA) with linearly decreasing gravitational constant is proposed for solving PV cell parameter estimation problem. The results of application of the proposed GSA to PV cell parameter estimation problem vividly show its outperformance over GSA with constant gravitational constant, GSA with exponentially decreasing gravitational constant, genetic algorithm, evolutionary programming and Newton algorithm.

关键词： PV cells parameter estimation metaheuristics PV modeling gravitational search algorithm

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gravitational search algorithm (GSA) Optimized Fuzzy-PID controller Design for Load frequency control of an interconnected multi-area power system

Gravitational Search Algorithm (GSA) Optimized Fuzzy-PID con...

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IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Debnath, Manoj Kumar Mallick, Ranjan Kumar Das, Sadhana Aman, Aditya Siksha O Anusandhan Univ Dept EE Bhubaneswar Odisha India Siksha O Anusandhan Univ Dept EEE Bhubaneswar Odisha India

ISBN: (纸本)9781509012770

This paper focuses on the load frequency control of two area interconnected reheat thermal systemwith fuzzy-PID controller optimized by gravitational search algorithm(GSA). The gains of the fuzzy-PID controllers are optimized by providing a step load perturbation(SLP) of 1% to area 1in consideration of integral time absolute error(ITAE) as the objective function. The dynamic performances of the system are being analyzed with GSA and PSO optimized fuzzy-PID controllers. The supremacy of the proposed method is depicted in terms of settling time, peak overshoots and undershoots in comparison with a recently published results. Further the robustness of the system is proved by changing the system loading.

关键词： Automatic Generation Control Particle Swarm Optimization gravitational search algorithm Fuzzy Logic Controller Proportional-Integral-Derivative controller

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gravitational search algorithm: A Novel Optimization Approach for Economic Load Dispatch

Gravitational Search Algorithm: A Novel Optimization Approac...

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Annual International Conference on Emerging Research Areas (AICERA) / International Conference on Microelectronics, Communication and Renewable Energy (ICMiCR)

作者： Udgir, Mugdha Dubey, Hari Mohan Pandit, Manjaree Madhav Inst Sci & Technol Dept Elect Engg Gwalior India

ISBN: (纸本)9781467351492;9781467351508

The application of gravitational search algorithm a heuristic optimization approach for solution of economic load dispatch is presented here. GSA optimization approach is inspired by Newtonian gravity and law of motion. The efficacy and robustness of the GSA approach is analyzed on four different standard test cases. Comparison of computational results is made with the recent available methods. The computational results confirm superiority, faster convergence and adeptness of the algorithm.

关键词： gravitational search algorithm Economic Load Dispatch(ELD) Ramp Rate Limit(RRL) and Prohibited Operating zones(POZ)

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gravitational search algorithm based Economic Load Dispatch Problem Incorporating Wind Generating System 1

Gravitational Search Algorithm based Economic Load Dispatch ...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Nath, Monalisa Mishra, Sanhita Nath, Desalin KIIT Univ Bhubaneswar Odisha India KIIT Univ BPUT Bhubaneswar Odisha India

ISBN: (纸本)9781467385879

Now economic load dispatch, generation of the power is always matched to meet prevailing load condition, so as maintain the constant frequency and stability of the network. Here the main goal of the economic load dispatch, the energy should supply to the consumer at minimum lowest possible cost. Hence, the cost of power delivered should be minimum for any load condition thus the economic load dispatch is played vital problems of power system operation and control. But in current proposal increase in fuel cost and environmental pollution, wind power is incorporated with the other generator for the fulfillment of the required power generation. The main aim of this work is to find optimal cost estimation in accordance with the power demand and wind availability by using gravitational search algorithm for the minimization of cost.

关键词： Economic Load Dispatch gravitational search algorithm Wind System Probability Density Function Cost Function

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gravitational search algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty

Gravitational Search Algorithm based Long Short-term Memory ...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Reza, M.S. Hannan, M.A. Mansor, M. Ker, Pin Jern Tiong, Sieh Kiong Hossain, M.J. Universiti Tenaga Nasional Coe Department of Electrical and Electronic Engineering Kajang43000 Malaysia Sunway University School of Engineering and Technology Petaling Jaya Bandar Sunway47500 Malaysia Universiti Tenaga Nasional Institute of Sustainable Energy Kajang43000 Malaysia University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia

ISBN: (纸本)9781665471640

This paper presents a hybrid approach for predicting the remaining useful life (RUL) and future capacity of lithium-ion batteries (LIBs) using an improved long short-term memory (LSTM) deep neural network with a gravitational search algorithm (GSA). The proposed method address the challenges of nonlinear and dynamic battery behavior, battery aging uncertainty, the requirement for optimal hyperparameters tuning, and the importance of maintaining safe and efficient battery operation. The RUL prediction uncertainty with a 95% confidence interval (CI) is also analyzed. The GSA algorithm optimizes the hyperparameters of the LSTM network to construct an optimal model. The method proposed in this work is evaluated based on the aging data from the NASA battery dataset, and its effectiveness is compared with that of BiLSTM, baseline gated recurrent unit (GRU), and baseline LSTM using various error metrics. The results demonstrate that the LSTM-GSA model outperforms other methods in the context of prediction accuracy, achieving a minimum RMSE of 1.04% and 1.15% for both battery cases. Overall, this research provides a promising solution for predicting RUL and the future capacity of LIBs with uncertainty, which is essential for ensuring the safe and efficient operation of energy storage systems. © 2023 IEEE.

关键词： capacity prediction deep neural network gravitational search algorithm lithium-ion batteries long short-term memory remaining useful life

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Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

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Earthquake Engineering and Engineering Vibration 2022年第2期21卷 455-474页

作者： Najad Ayyash Farzad Hejazi Civil Engineering University Putra MalaysiaSeri Kembangan 43300Malaysia

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom *** current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be ***,there is a lack of studies regarding the optimum location for various viscous and wall *** main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the *** types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) *** many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned *** characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the *** that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure *** examine the performance

关键词： hybrid optimization algorithm structures earthquake seismic damper devices particle swarm optimization method gravitational search algorithm

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Multi-objective optimization of foundation using global-local gravitational search algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2014年第3期50卷 257-273页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan Eslami, Mahdiyeh Islamic Azad Univ Anar Branch Dept Civil Engn Anar Iran Univ Kebangsaan Malaysia Dept Civil & Struct Engn Bangi 43600 Selangor Malaysia Islamic Azad Univ Dept Elect Engn Sci & Res Branch Kerman Iran

This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and CO2 emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded CO2 emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

关键词： spread foundation cost optimization CO2 emissions optimization gravitational search algorithm

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A hybrid algorithm based on self-adaptive gravitational search algorithm and differential evolution

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 113卷 515-530页

作者： Zhao, Fuqing Xue, Feilong Zhang, Yi Ma, Weimin Zhang, Chuck Song, Houbin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Gansu Peoples R China Xijin Univ Sch Mech Engn Xian 710123 Shaanxi Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

The gravitational search algorithm (GSA) has excellent performance in solving various optimization problems. However, it has been demonstrated that GSA tends to trap into local optima and are easy to lose diversity in the late evolution process. In this paper, a new hybrid algorithm based on self-adaptive gravitational search algorithm (GSA) and Differential Evolution (DE) is proposed for solving single objective optimization, named SGSADE. Firstly, a self-adaptive mechanism based on GSA is proposed for improving the convergence speed and balancing exploration and exploitation. Secondly, the diversity of the population is maintained in the evolution process by using crossover and mutation operation from DE. Besides, to improve the performance of the algorithm, a new perturbation based on Levy flight theory is embedded to enhance exploitation capacity. The simulated results of SGSADE on 2017 CEC benchmark functions show that the SGSADE outperforms the state-of-the-art variant algorithms of the GSA. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Differential evolution Self-adaptive mechanism Crossover and mutation operation Exploration and exploitation

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Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm

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CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS 2014年第3期31卷 229-242页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan Eslami, Mahdiyeh Islamic Azad Univ Anar Branch Civil Engn Dept Anar Iran Natl Univ Malaysia Civil & Struct Engn Dept Bangi Selangor Malaysia Islamic Azad Univ Sci & Res Branch Elect Engn Dept Kerman Iran

This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retainingwalls. The proposed algorithm combines an adaptive gravitational search algorithm (AGSA) with pattern search (PS) called AGSA-PS. In the resulting hybrid approach, the PS algorithm is employed as a local search algorithm around the global solution found by AGSA. The proposed algorithm was tested on a set of five well-known benchmark functions and simulation results demonstrate the superiority of the new method compared with the standard algorithm. Thereafter, the proposed AGSA-PS is applied for multi-objective optimisation of RC retaining walls. Two objective functions include total cost and embedded CO2 emissions of retaining wall are considered. The reliability and efficiency of the AGSA-PS for multi-objective optimisation of retaining structures are investigated by considering two design examples of retaining walls. Experimental results demonstrate that the resulting algorithm has high viability, accuracy and significantly outperforms the original algorithm and some other methods in the literature.

关键词： retaining wall multi-objective optimisation gravitational search algorithm pattern search

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