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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 structures.The 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 used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The 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 structure.Four 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) device.Since 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 problems.The 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 structure.After 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 response.To examine the performance of the proposed PSO-GSA optimization method,it h

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

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gravitational search algorithm–based fuzzy control for a nonlinear ball and beam system

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Journal of Control and Decision 2018年第3期5卷 229-240页

作者： M.J.Mahmoodabadi N.Danesh Department of Mechanical Engineering Sirjan University of TechnologySirjanIran

In recent decades,fuzzy logic and its application for stabilising nonlinear systems have had a great development.In this paper,a novel optimal fuzzy controller is provided to control a ball and beam system.The fuzzy control force is calculated via a fuzzy system based on the singleton fuzzifier,the centre average defuzzifier and the product inference engine.To further improve the control performance,the gravitational search algorithm is applied to optimise the controller parameters.The obtained simulation results indicate that the proposed scheme can provide a better performance in the case of convergence rate and accuracy in comparison with those of other recently published works.

关键词： Fuzzy controller gravitational search algorithm optimal control nonlinear system ball and beam system

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A Multi-Layered gravitational search algorithm for Function Optimization and Real-World Problems

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 94-109页

作者： Yirui Wang Shangce Gao Mengchu Zhou Yang Yu the Faculty of Engineering University of ToyamaToyama 930-8555Japan the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

A gravitational search algorithm(GSA)uses gravitational force among individuals to evolve population.Though GSA is an effective population-based algorithm,it exhibits low search performance and premature convergence.To ameliorate these issues,this work proposes a multi-layered GSA called MLGSA.Inspired by the two-layered structure of GSA,four layers consisting of population,iteration-best,personal-best and global-best layers are constructed.Hierarchical interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population.Performance comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low,medium and high dimensions demonstrates that MLGSA is the most competitive one.It is also compared with four particle swarm optimization variants to verify its excellent performance.Moreover,the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its performance.The relationship between its population diversity and fitness diversity is analyzed to clarify its search performance.Its computational complexity is given to show its efficiency.Finally,it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality.

关键词： Artificial intelligence exploration and exploitation gravitational search algorithm hierarchical interaction hierarchy machine learning population structure

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An Intelligent Multi-robot Path Planning in a Dynamic Environment Using Improved gravitational search algorithm

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International Journal of Automation and computing 2021年第6期18卷 1032-1044页

作者： P.K.Das H.S.Behera P.K.Jena B.K.Panigrahi Department of Computer Sceime and Engineering and Information TechnologyVeer Surendra Sai University of TechnologyBurlaOdishaIndia Departmet of Mechanical Engineering Veer Surendra Sai University of TechnologyBurlaOdishaIndia Departmet of Electrical Engineering Indian Institute of TechnologyDelhiIndia

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization(PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution(DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.

关键词： gravitational search algorithm multi-robot path planning average total trajectory path deviation average uncovered trajectory target distance average path length

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An Improved gravitational search algorithm for Dynamic Neural Network Identification

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International Journal of Automation and computing 2014年第4期11卷 434-440页

作者： Bao-Chang Xu Ying-Ying Zhang Department of Automation China University of Petroleum (Beijing)

gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent s position further using the coordinate descent method. For the experimental verification of the proposed algorithm,both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous(NARX) recurrent neural network identification for a magnetic levitation system.Compared with the system identification based on gravitational search algorithm neural network(GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance.

关键词： gravitational search algorithm orbital change optimization neural network system identification

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Hypersonic reentry trajectory planning by using hybrid fractional-order particle swarm optimization and gravitational search algorithm

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Chinese Journal of Aeronautics 2021年第1期34卷 50-67页

作者： Khurram SHAHZAD SANA Weiduo HU School of Astronautics Beihang UniversityBeijing 102206China

This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization gravitational search algorithm(FPSOGSA)and applies it to the trajectory planning of the hypersonic lifting reentry flight vehicles.The proposed method is used to calculate the control profiles to achieve the two objectives,namely a smoother trajectory and enforcement of the path constraints with terminal accuracy.The smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide(QEG)scheme.The aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement scheme.The maximum heating rate path constraint is enforced through the angle of attack parameterization.The Common Aero Vehicle(CAV)flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization(PSO)method and the standard gravitational search algorithm(GSA).The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency.

关键词： Fractional-order gravitational search algorithm Particle swarm optimization Reentry gliding vehicle Trajectory optimization

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Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid gravitational search and Particle Swarm Optimization algorithm

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Energy Engineering 2022年第6期119卷 2489-2511页

作者： Rongxin Zhang Chengying Yang Xuetao Li School of Economics and Management Hubei University of Automotive TechnologyShiyan442000China School of Management Universiti Sains MalaysiaPenang11800Malaysia School of Science Hubei University of Automotive TechnologyShiyan442002China

Buildings are the main energy consumers across the world,especially in urban communities.Building smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization too.By controlling the energy consumption of lighting,heating,and cooling systems,energy consumption can be optimized.All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources(RES),which mitigates environmental impacts and reduces peak demand for electrical energy.In this paper,a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes.In this way,large power stations that work with fossil fuels will no longer be developed.The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles(EVs)with bidirectional power exchangeability with the power grid,an energy storage system(ESS),and solar panels.Additionally,the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work.Different case studies,including the sales of electrical energy resulting from PV panels’generated power to the power grid,time-variable loads such as washing machines,and different demand response(DR)strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs,BESS,and solar panels.The proposed model was simulated in MATLAB.A hybrid particle swarm optimization(PSO)and gravitational search(GS)algorithm were utilized for optimization.Scenario generation and reduction were performed via LHS and backward methods,respectively.Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use(STOU)loads,EV,ESS,and PV system.Based on the results,the proposed model markedly reduced the electricity costs of the smart house.

关键词： Energy management smart house particle swarm optimization algorithm gravitational search algorithm demand response electric vehicle

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Investigation Effects of Selection Mechanisms for gravitational search algorithm

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Journal of Computer and Communications 2014年第4期2卷 117-126页

作者： Oguz Findik Mustafa Servet Kiran Ismail Babaoglu Computer Engineering Department Abant Izzet Baysal UniversityBoluTurkey Computer Engineering Department Selcuk UniversityKonyaTurkey

The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solution for the optimization problems by using interaction in all agents or masses in the population. This paper proposes and analyzes fitness-based proportional (rou- lette-wheel), tournament, rank-based and random selection mechanisms for choosing agents which they act masses in the GSA. The proposed methods are applied to solve 23 numerical benchmark functions, and obtained results are compared with the basic GSA algorithm. Experimental results show that the proposed methods are better than the basic GSA in terms of solution quality.

关键词： gravitational search algorithm Roulette-Wheel Selection Tournament Selection Rank-Based Selection Random Selection Continuous Optimization

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Parameter Identification for Fractional-order Chaotic System by Using Nelder–Mead Simplex gravitational search algorithm

Parameter Identification for Fractional-order Chaotic System...

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第三十九届中国控制会议

作者： Jiarong Wang Unit 92228 of PLA

Aiming at parameter identification for fractional-order chaotic systems, a Nelder-Mead simplex gravitational search algorithm（NMGSA） is proposed. The search and replacement mechanism in the simplex search method and the particles update method in gravitational search algorithm is combined together in the proposed algorithm. Simplex search method has powerful local searching capacity, which is helpful for particles to jump out from local optimum, thus avoiding the gravitational search algorithm plunging falling into local optimum. Standard test functions are used to test the NMGSA, and the test results show that the algorithm has good stability and global search capability. Then, NMGSA is applied in the parameter identification for fractional-order Lorenz system. The results prove the effectiveness and robustness of NMGSA, when solve the problem of parameter identification of fractional-order chaotic system.

关键词： fractional-order chaotic system parameter identification gravitational search algorithm Nelder-Mead simplex search method

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Parameter estimation of Hammerstein systems based on the gravitational search algorithm

Parameter estimation of Hammerstein systems based on the gra...

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第32届中国控制与决策会议

作者： Shanling Xu Junhong Li Juping Gu Liang Hua Liangliang Shang School of Electrical Engineering Nantong University

The Hammerstein nonlinear system is composed of a static nonlinear subsystem followed by a linear subsystem,and widely exists in chemical,thermal and other process industries.Therefore,it is significant to obtain parameter estimates of Hammerstein systems.This paper considers the identification problem of the Hammerstein finite impulse response moving average（FIR-MA） system using the gravitational search algorithm and the iterative identification technique.A particle swarm iterative identification algorithm is also proposed for comparison.The simulation results show that the proposed algorithms can effectively estimate the parameters of the Hammerstein FIR-MA system and the gravitational search algorithm behaves better than the particle swarm optimization approach in accuracy.

关键词： Hammerstein systems parameter estimation gravitational search algorithm

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