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 impr...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.展开更多
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 fligh...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.展开更多
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.T...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.展开更多
On the basis of the theoretical analysis of a single-machine infinite-bus （SMIB）, using the modified linearized Phil- lips-Heffron model installed with unified power flow controller （UPFC）, the potential of the UP...On the basis of the theoretical analysis of a single-machine infinite-bus （SMIB）, using the modified linearized Phil- lips-Heffron model installed with unified power flow controller （UPFC）, the potential of the UPFC supplementary controller to enhance the dynamic stability of a power system is evaluated by measuring the electromechanical controllability through singular value decomposition （SVD） analysis. This controller is tuned to simultaneously shift the undamped electromeehanical modes to a prescribed zone in the s-plane. The problem of robust UPFC based damping controller is formulated as an optimization problem according to the eigenvalue-based multi-objective function comprising the damping factor, and the damping ratio of the undamped electromechanical modes to be solved using gravitational search algorithm （GSA） that has a strong ability to find the most optimistic results. The different loading conditions are simulated on a SMIB system and the rotor speed deviation, internal voltage deviation, DC voltage deviation and electrical power deviation responses are studied with the effect of this flexible AC transmission systems （FACTS） controller. The results reveal that the tuned GSA based UPFC controller using the proposed multi-objective function has an excellent capability in damping power system with low frequency oscillations and greatly enhances the dynamic stability of the power systems.展开更多
This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential...This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential evolution （FSDE）. This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.展开更多
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 ther...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 has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.展开更多
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...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.展开更多
A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyr...A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.展开更多
Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have...Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have to overcome some shortcomings,for instance slow convergence or stagnancy in local minima.Therefore,optimization algorithms with a global search ability are used to enhance ANNs,i.e.to increase the rate of convergence and to reach a global minimum.This paper introduces a two-stage approach for failure identification in a steel beam.In the first step,the presence of defects and their positions are identified by modal indices.In the second step,a feedforward neural network,improved by a hybrid particle swarm optimization and gravitational search algorithm,namely FNN-PSOGSA,is used to quantify the severity of damage.Finite element(FE)models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method.For comparison,a traditional ANN is also used to estimate the severity of the damage.The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.展开更多
A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfigur...A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.展开更多
This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a no...This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.展开更多
The gravitational wave spacecraft is a complex multi-input multi-output dynamic system.The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precis...The gravitational wave spacecraft is a complex multi-input multi-output dynamic system.The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precision control.Establishing one spacecraftwith two laser links,compared to one spacecraft with a single laser link,requires an upgraded decoupling algorithmfor the link establishment.The decoupling algorithmwe designed reassigns the degrees of freedomand forces in the control loop to ensure sufficient degrees of freedomfor optical axis control.In addressing the distinct dynamic characteristics of different degrees of freedom,a transfer function compensation method is used in the decoupling process to further minimize motion coupling.The open-loop frequency response of the systemis obtained through simulation.The upgraded decoupling algorithms effectively reduce the open-loop frequency response by 30 dB.The transfer function compensation method efficiently suppresses the coupling of low-frequency noise.展开更多
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 control...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.展开更多
With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyz...With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms.First,the algorithm designated the cluster set as vacant,with the introduction of a new object.Subsequently,a new cluster based on the given object was constructed.The dissimilarities between it and each existing cluster were calculated using a defined difference measure.Theminimumdissimilaritywas selected.Through comparing the proposed algorithmwith the traditional Back Propagation(BP)neural network and nearest neighbor detection algorithm,the application of the Defense Advanced Research Projects Agency(DARPA)00 and Knowledge Discovery and Data Mining(KDD)Cup 99 datasets revealed that the performance of the proposed algorithmsurpassed that of both algorithms in terms of the detection rate,speed,false positive rate,and false negative rate.展开更多
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 solut...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.展开更多
基金supported by National Natural Science Foundationof China(No.2011ZX05021-003)Science Foundation of ChinaUniversity of Petroleum
文摘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.
文摘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.
基金supported by National Natural Science Foundation of China(61872271,61673403,61873105,11972115)the Fundamental Research Funds for the Central Universities(22120190208)JSPS KAKENHI(JP17K12751)。
文摘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.
文摘On the basis of the theoretical analysis of a single-machine infinite-bus （SMIB）, using the modified linearized Phil- lips-Heffron model installed with unified power flow controller （UPFC）, the potential of the UPFC supplementary controller to enhance the dynamic stability of a power system is evaluated by measuring the electromechanical controllability through singular value decomposition （SVD） analysis. This controller is tuned to simultaneously shift the undamped electromeehanical modes to a prescribed zone in the s-plane. The problem of robust UPFC based damping controller is formulated as an optimization problem according to the eigenvalue-based multi-objective function comprising the damping factor, and the damping ratio of the undamped electromechanical modes to be solved using gravitational search algorithm （GSA） that has a strong ability to find the most optimistic results. The different loading conditions are simulated on a SMIB system and the rotor speed deviation, internal voltage deviation, DC voltage deviation and electrical power deviation responses are studied with the effect of this flexible AC transmission systems （FACTS） controller. The results reveal that the tuned GSA based UPFC controller using the proposed multi-objective function has an excellent capability in damping power system with low frequency oscillations and greatly enhances the dynamic stability of the power systems.
基金supported by the National Natural Science Foundation of China (70871081)the Shanghai Leading Academic Discipline Project of China (S1205YLXK)
文摘This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential evolution （FSDE）. This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.
基金University Putra Malaysia under Putra Grant No.9531200。
文摘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 has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.
文摘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.
基金Project(UKM-DLP-2011-059) supported by the National University of Malaysia
文摘A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.
基金the Vlaamse Interuniversitaire Raad University Development Cooperation(VLIR-UOS)Team Project(No.VN2018TEA479A103)the Flemish Government,Belgium。
文摘Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have to overcome some shortcomings,for instance slow convergence or stagnancy in local minima.Therefore,optimization algorithms with a global search ability are used to enhance ANNs,i.e.to increase the rate of convergence and to reach a global minimum.This paper introduces a two-stage approach for failure identification in a steel beam.In the first step,the presence of defects and their positions are identified by modal indices.In the second step,a feedforward neural network,improved by a hybrid particle swarm optimization and gravitational search algorithm,namely FNN-PSOGSA,is used to quantify the severity of damage.Finite element(FE)models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method.For comparison,a traditional ANN is also used to estimate the severity of the damage.The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.
基金Project(DIP-2012-30)supported by the Universiti Kebangsaan,Malaysia
文摘A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.
文摘This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.
基金supported by the National Key Research and Development Program of China(2022YFC2203700).
文摘The gravitational wave spacecraft is a complex multi-input multi-output dynamic system.The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precision control.Establishing one spacecraftwith two laser links,compared to one spacecraft with a single laser link,requires an upgraded decoupling algorithmfor the link establishment.The decoupling algorithmwe designed reassigns the degrees of freedomand forces in the control loop to ensure sufficient degrees of freedomfor optical axis control.In addressing the distinct dynamic characteristics of different degrees of freedom,a transfer function compensation method is used in the decoupling process to further minimize motion coupling.The open-loop frequency response of the systemis obtained through simulation.The upgraded decoupling algorithms effectively reduce the open-loop frequency response by 30 dB.The transfer function compensation method efficiently suppresses the coupling of low-frequency noise.
文摘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.
基金supported by the Fujian China University Education Informatization Project(FJGX2023013)National Natural Science Foundation of China Youth Program(72001126)+1 种基金Sanming University’s Research and Optimization of the Function of Safety Test Management and Control Platform Project(KH22097)Young and Middle-Aged Teacher Education Research Project of Fujian Provincial Department of Education(JAT200642,B202033).
文摘With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms.First,the algorithm designated the cluster set as vacant,with the introduction of a new object.Subsequently,a new cluster based on the given object was constructed.The dissimilarities between it and each existing cluster were calculated using a defined difference measure.Theminimumdissimilaritywas selected.Through comparing the proposed algorithmwith the traditional Back Propagation(BP)neural network and nearest neighbor detection algorithm,the application of the Defense Advanced Research Projects Agency(DARPA)00 and Knowledge Discovery and Data Mining(KDD)Cup 99 datasets revealed that the performance of the proposed algorithmsurpassed that of both algorithms in terms of the detection rate,speed,false positive rate,and false negative rate.
基金supported by Scientific Research Project of Selçuk University
文摘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.
