Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,th...Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.展开更多
Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence spee...Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.展开更多
In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assesse...In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assessed.Two dispatch algorithms,named Cycle Charging and Load Following,are implemented to find the optimal solution(i.e.,net cost,operation cost,carbon emission.energy cost,component sizing,etc.)of the hybrid system.The microgrid is also modeled in the DIgSILENT Power Factory platform,and the respective power system responses are then evaluated.The development of dispatch algorithms specifically tailored for agricultural applications has enabled to dynamically manage energy flows,responding to fluctuating demands and resource availability in real-time.Through careful consideration of factors such as seasonal variations and irrigation requirements,these algorithms have enhanced the resilience and adaptability of the microgrid to dynamic operational conditions.However,it is revealed that both approaches have produced the same techno-economic results showing no significant difference.This illustrates the fact that the considered microgrid can be implemented with either strategy without significant fluctuation in performance.The study has shown that the harmful gas emission has also been limited to only 17,928 kg/year of CO_(2),and 77.7 kg/year of Sulfur Dioxide.For the proposed microgrid and load profile of 165.29 kWh/day,the net present cost is USD 718,279,and the cost of energy is USD 0.0463 with a renewable fraction of 97.6%.The optimal sizes for PV,Bio,Grid,Electrolyzer,and Converter are 1494,500,999,999,500,and 495 kW,respectively.For a hydrogen tank(HTank),the optimal size is found to be 350 kg.This research work provides critical insights into the techno-economic feasibility and environmental impact of integrating biomass-PV-hydrogen storage-Grid hybrid renewable microgrids into agricultural settings.展开更多
Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and eco...Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm.展开更多
The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface inject...The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×10^(4) CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions an...As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.展开更多
Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been prop...Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.展开更多
Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositori...Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.展开更多
This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is ...This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is employed,incorporating key performance metrics such as fuel efficiency and emissions to develop data-driven surrogate models of the EMS.These models are optimized using various algorithms targeting parameters such as engine idle speed,thermostat temperature fraction,regeneration load factor,and battery stateof-charge thresholds.Correlation analysis highlights the significant impact of the lower state-of-charge threshold and thermostat temperature fraction on fuel efficiency and emissions.Among the optimization methods,the combination of a backpropagation neural network(BPNN)and a multi-objective genetic algorithm(MOGA)proves most effective,achieving fuel consumption reductions of 5.26%and 5.01%in charge-sustaining and charge-depletion modes,respectively.Additionally,the BPNN-based MOGA demonstrates notable improvements in emission reduction.These findings suggest that optimizing rule-based EMS parameters without altering underlying management rules can significantly enhance performance under diverse and unanticipated driving conditions.展开更多
Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle,few researches about the series-parallel hybrid electric vehicle have been ...Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle,few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy.In this paper,a series-parallel hybrid electric bus as well as its control strategy is revealed,and a control parameter optimization approach using the real-valued genetic algorithm is proposed.The optimization objective is to minimize the fuel consumption while sustain the battery state of charge,a tangent penalty function of state of charge(SOC)is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem.For this strategy,the vehicle operating mode is switched based on the vehicle speed,and an"optimal line"typed strategy is designed for the parallel control.The optimization parameters include the speed threshold for mode switching,the highest state of charge allowed,the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed.They are optimized through numerical experiments based on real-value genes,arithmetic crossover and mutation operators.The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test,in which a control area network-based monitor system was used to trace the driving schedule.The test result shows that this approach is feasible for the control parameter optimization.This approach can be applied to not only the novel construction presented in this paper,but also other types of hybrid electric vehicles.展开更多
Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters...Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,展开更多
The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper prop...The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.展开更多
A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems a...A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.展开更多
Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome...Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.展开更多
The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was ...The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.展开更多
The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.Th...The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.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
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
基金received funding from the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633)2023 University Student Innovation and Entrepreneurship Training Program(202311463009Z)+1 种基金Changzhou Science and Technology Support Project(CE20235045)Open Project of Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology(2021JSSPD12).
文摘Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.
基金funded by the National Key Research and Development Program(Grant No.2022YFB3706904).
文摘Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.
基金financed by the Ministry of Science and Technology(MOST)Bangladesh under Special Research grant for the FY 2023-24(SRG 232410)Further,the authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabi for funding this research work through the project number“NBU-FFR-2025-3623-05”。
文摘In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assessed.Two dispatch algorithms,named Cycle Charging and Load Following,are implemented to find the optimal solution(i.e.,net cost,operation cost,carbon emission.energy cost,component sizing,etc.)of the hybrid system.The microgrid is also modeled in the DIgSILENT Power Factory platform,and the respective power system responses are then evaluated.The development of dispatch algorithms specifically tailored for agricultural applications has enabled to dynamically manage energy flows,responding to fluctuating demands and resource availability in real-time.Through careful consideration of factors such as seasonal variations and irrigation requirements,these algorithms have enhanced the resilience and adaptability of the microgrid to dynamic operational conditions.However,it is revealed that both approaches have produced the same techno-economic results showing no significant difference.This illustrates the fact that the considered microgrid can be implemented with either strategy without significant fluctuation in performance.The study has shown that the harmful gas emission has also been limited to only 17,928 kg/year of CO_(2),and 77.7 kg/year of Sulfur Dioxide.For the proposed microgrid and load profile of 165.29 kWh/day,the net present cost is USD 718,279,and the cost of energy is USD 0.0463 with a renewable fraction of 97.6%.The optimal sizes for PV,Bio,Grid,Electrolyzer,and Converter are 1494,500,999,999,500,and 495 kW,respectively.For a hydrogen tank(HTank),the optimal size is found to be 350 kg.This research work provides critical insights into the techno-economic feasibility and environmental impact of integrating biomass-PV-hydrogen storage-Grid hybrid renewable microgrids into agricultural settings.
基金supported by the National Natural Science Foundation of China[Grant No.12461035]Qinghai University Students Innovative Training Program Project[2024-QX-57].
文摘Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm.
基金the National Natural Science Foundation of China,grant numbers 51704253 and 52474084.
文摘The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×10^(4) CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金supported by the National Natural Science Foundation of China under Grant 62473328by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.
基金supported by the National Natural Science Foundation of China (52275480)the Guizhou Provincial Science and Technology Program of Qiankehe Zhongdi Guiding ([2023]02)+1 种基金the Guizhou Provincial Science and Technology Program of Qiankehe Platform Talent Project (GCC[2023]001)the Guizhou Provincial Science and Technology Project of Qiankehe Platform Project (KXJZ[2024]002).
文摘Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.
文摘Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.
文摘This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is employed,incorporating key performance metrics such as fuel efficiency and emissions to develop data-driven surrogate models of the EMS.These models are optimized using various algorithms targeting parameters such as engine idle speed,thermostat temperature fraction,regeneration load factor,and battery stateof-charge thresholds.Correlation analysis highlights the significant impact of the lower state-of-charge threshold and thermostat temperature fraction on fuel efficiency and emissions.Among the optimization methods,the combination of a backpropagation neural network(BPNN)and a multi-objective genetic algorithm(MOGA)proves most effective,achieving fuel consumption reductions of 5.26%and 5.01%in charge-sustaining and charge-depletion modes,respectively.Additionally,the BPNN-based MOGA demonstrates notable improvements in emission reduction.These findings suggest that optimizing rule-based EMS parameters without altering underlying management rules can significantly enhance performance under diverse and unanticipated driving conditions.
基金supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2006AA11A127)
文摘Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle,few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy.In this paper,a series-parallel hybrid electric bus as well as its control strategy is revealed,and a control parameter optimization approach using the real-valued genetic algorithm is proposed.The optimization objective is to minimize the fuel consumption while sustain the battery state of charge,a tangent penalty function of state of charge(SOC)is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem.For this strategy,the vehicle operating mode is switched based on the vehicle speed,and an"optimal line"typed strategy is designed for the parallel control.The optimization parameters include the speed threshold for mode switching,the highest state of charge allowed,the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed.They are optimized through numerical experiments based on real-value genes,arithmetic crossover and mutation operators.The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test,in which a control area network-based monitor system was used to trace the driving schedule.The test result shows that this approach is feasible for the control parameter optimization.This approach can be applied to not only the novel construction presented in this paper,but also other types of hybrid electric vehicles.
文摘Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,
文摘The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.
文摘A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.
基金the Open Fund(PLC201104)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology)the National Natural Science Foundation of China(No.61072073)the Key Project of Education Commission of Sichuan Province(No.10ZA072)
文摘Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.
基金National Natural Science Foundation of China(No.70971020)the Subject of Ministry of Education of Hunan Province,China(No.13C818)+3 种基金the Project of Industrial Science and Technology Support of Hengyang City,Hunan Province,China(No.2013KG63)the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science and Engineering,China(No.2012RYJ03)the Fund Project of Humanities and Social Sciences,Ministry of Education of China(No.13YJCZH147)the Special Fund for Shanghai Colleges' Outstanding Young Teachers' Scientific Research Projects,China(No.ZZGJD12033)
文摘The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.
基金Supported by the National Natural Science Foundation of China(1117202591116)
文摘The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.
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.
