A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its referenc...A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle.The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence.In order to reduce the probability of trapping into a local optimal value,an extremum mutation was introduced into simplex-PSO and simplex-PSO-t(simplex-PSO with turbulence) was devised.Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t,and the experimental results confirmed the conclusions:(1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality;(2) compared PSO with chaos PSO(CPSO),the best optimum index increases by a factor of 1×102-1×104.展开更多
In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage. To solve this problem, an optimal allocation mo...In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage. To solve this problem, an optimal allocation model based on energy transfer mode was proposed after objective function for optimizing energy using efficiency was established, and then, a new Tabu search and particle swarm hybrid optimizing algorithm was proposed to find solutions. While actual data of energy demand and distribution in China were selected for analysis, the economic critical value in comparison between the long-distance coal transfer and electric power transmission was gained. Based on the above discussion, some proposals were put forward for optimal allocation of energy transfer modes in China. By comparing other three traditional methods that are based on regional price differences, freight rates and annual cost with the proposed method, the result indicates that the economic efficiency of the energy transfer can be enhanced by 3.14%, 5.78% and 6.01%, respectively.展开更多
Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of...Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.展开更多
This paper proposes a systematic method, integrating the uniform design (UD) of experiments and quantum-behaved particle swarm optimization (QPSO), to solve the problem of a robust design for a railway vehicle suspens...This paper proposes a systematic method, integrating the uniform design (UD) of experiments and quantum-behaved particle swarm optimization (QPSO), to solve the problem of a robust design for a railway vehicle suspension system. Based on the new nonlinear creep model derived from combining Hertz contact theory, Kalker's linear theory and a heuristic nonlinear creep model, the modeling and dynamic analysis of a 24 degree-of-freedom railway vehicle system were investigated. The Lyapunov indirect method was used to examine the effects of suspension parameters, wheel conicities and wheel rolling radii on critical hunting speeds. Generally, the critical hunting speeds of a vehicle system resulting from worn wheels with different wheel rolling radii are lower than those of a vehicle system having original wheels without different wheel rolling radii. Because of worn wheels, the critical hunting speed of a running railway vehicle substantially declines over the long term. For safety reasons, it is necessary to design the suspension system parameters to increase the robustness of the system and decrease the sensitive of wheel noises. By applying UD and QPSO, the nominal-the-best signal-to-noise ratio of the system was increased from -48.17 to -34.05 dB. The rate of improvement was 29.31%. This study has demonstrated that the integration of UD and QPSO can successfully reveal the optimal solution of suspension parameters for solving the robust design problem of a railway vehicle suspension system.展开更多
In this paper, we address one of the issues in the frequency assignment problem for cellular mobile networks in which we intend to minimize the interference levels when assigning frequencies from a limited frequency s...In this paper, we address one of the issues in the frequency assignment problem for cellular mobile networks in which we intend to minimize the interference levels when assigning frequencies from a limited frequency spectrum. In order to satisfy the increasing demand in such cellular mobile networks, we use a hybrid approach consisting of a Particle Swarm Optimization(PSO) combined with a Tabu Search(TS) algorithm. This approach takes both advantages of PSO efficiency in global optimization and TS in avoiding the premature convergence that would lead PSO to stagnate in a local minimum. Moreover, we propose a new efficient, simple, and inexpensive model for storing and evaluating solution's assignment. The purpose of this model reduces the solution's storage volume as well as the computations required to evaluate thesesolutions in comparison with the classical model. Our simulation results on the most known benchmarking instances prove the effectiveness of our proposed algorithm in comparison with previous related works in terms of convergence rate, the number of iterations, the solution storage volume and the running time required to converge to the optimal solution.展开更多
A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly dec...A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.展开更多
Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. Ho...Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. The sufficient conditions for asymptotic stability of an acceleration factor and inertia weight are deduced in this paper. The value of the inertia weight w is enhanced to （ - 1, 1）. Furthermore a new adaptive PSO algorithm--harmonious PSO （HPSO） is proposed and proved that HPSO is a global search algorithm. Finally it is focused on a design task of a servo system controller. Considering the existence of model uncertainty and noise from sensors, HPSO are applied to optimize the parameters of fuzzy PID controller. The experiment results demonstrate the efficiency of the methods.展开更多
This paper presents a new approach based on the particle swarm optimization(PSO)algorithm for solving the drilling path optimization problem belonging to discrete space.Because the standard PSO algorithm is not guaran...This paper presents a new approach based on the particle swarm optimization(PSO)algorithm for solving the drilling path optimization problem belonging to discrete space.Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence,based on the mathematical algorithm model,the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution.And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator,the improved operator can satisfy the need of integer coding in drilling path optimization.The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize,fast convergence speed,and better global convergence characteris-tics.hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.展开更多
The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs.Quantum-behaved Particle Swarm Optimization(QPSO)algorithm is a kind of metaheuristic algorithms appli...The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs.Quantum-behaved Particle Swarm Optimization(QPSO)algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling.This paper analyzed the motion pattern of particles in a square potential well,given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well(BC-QSPSO).In this novel algorithm,the intrinsic cognitive link between particles’experience information and group sharing information was created by using normal Copula function.After that,the control parameters chosen strategy gives through experiments.Finally,the simulation results of the test functions show that the improved algorithms outperform the original QPSO algorithm and due to the error gradient information will not be over utilized in square potential well,the particles are easy to jump out of the local optimum,the BC-QSPSO is more suitable to solve the functions with correlative variables.展开更多
Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence optimization.However,like other evolutionary algorithms,PSO also suffers fr...Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence optimization.However,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems.Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO.Subsequently,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution stages.To be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution stage.Followed by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm diversity.In addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO method.Finally,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.展开更多
A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and r...A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight w is enhanced to （ 1, 1）. Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO （AFHPSO） is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.展开更多
In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the bla...In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the blade outlet width b2, blade outlet angle β2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0Qd and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.展开更多
Particle swarm optimizer(PSO),a new evolutionary computation algorithm,exhibits good performance for optimization problems,although PSO can not guarantee convergence of a global minimum,even a local minimum.However,th...Particle swarm optimizer(PSO),a new evolutionary computation algorithm,exhibits good performance for optimization problems,although PSO can not guarantee convergence of a global minimum,even a local minimum.However,there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm.In this paper,the algorithm are analyzed as a time-varying dynamic system,and the sufficient conditions for asymptotic stability of acceleration factors,increment of acceleration factors and inertia weight are deduced.The value of the inertia weight is enhanced to(-1,1).Based on the deduced principle of acceleration factors,a new adaptive PSO algorithm-harmonious PSO(HPSO)is proposed.Furthermore it is proved that HPSO is a global search algorithm.In the experiments,HPSO are used to the model identification of a linear motor driving servo system.An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters,but also determine the order of the model simultaneously.The results demonstrate the effectiveness of HPSO.展开更多
BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some met...BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer （PSO）, guaranteed convergence particle swarm optimizer （GCPSO）, an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.展开更多
In this work,the concepts of particle swarm optimization-based method,named non-Gaussian improved particle swarm optimization for minimizing the cost of energy(COE)of wind turbines(WTs)on high-altitude sites are intro...In this work,the concepts of particle swarm optimization-based method,named non-Gaussian improved particle swarm optimization for minimizing the cost of energy(COE)of wind turbines(WTs)on high-altitude sites are introduced.Since the COE depends on site specification constants and initialized parameters of wind turbine,the focus was on the design optimization of rotor radius,hub height and rated power.Based on literature,the COE is converted to the Saudi Arabia context.Thus,the constrained wind turbine optimization problem is developed.Then,non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving the optimization design in wind turbine efficiency under different altitudes ranging from 2500 to 4000 m.The results show that as altitude rises,the optimal rotor radius grows,but the optimal hub height and rated power drop,resulting in an increase in COE.Further,the non-Gaussian method display a faster convergence compared to the classical particle swarm optimization.These findings will be useful as a reference for wind turbine design at high altitudes.Thus,it could be employed to optimize the initialized parameter of wind turbine for the planned and largest wind farm in Saudi Arabia in Dumat Al-Jandal selected site.展开更多
One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize...One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize the Bidirectional Long Short⁃term Memory Network(BiLSTM)in order to improve the wind speed prediction accuracy,taking into account the highly stochastic and regular aspects of wind speed.Firstly,the wind speed time sequence is subjected to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information.The BiLSTM model,which incorporates the attention mechanism for prediction,is then fitted to the decomposed data,and its parameters are optimized using the particle swarm technique,reducing errors in predictive modeling.To get the final prediction,the components are finally superimposed.The empirical evidence shows that the CEEMDAN⁃PSO⁃BiLSTM⁃attention model decreases the RMSE(Root⁃Mean⁃Square⁃Error)by 15%-44%,the MAE by 18%-45%,the MAPE by 24%-52%,and the R2 by 1.4%-2.7%in comparison to the BiLSTM and other models.The validation of CEEMDAN⁃PSO⁃BiLSTM⁃attention model in short⁃term wind speed prediction is verified.展开更多
Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and dif...Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and different ratios of the bases of the trapezoidal outlet were generated based on curvature control regulation.Computational Fluid Dynamics(CFD)simulations have been conducted to obtain the flow field in the nozzle,and Forward-Backward Iterative Physical Optics(FBIPO)method was applied to study the electromagnetic scattering characteristics of the nozzle.Guarantee Convergence Particle Swarm Optimization(GCPSO)algorithm based on Radial Basis Function(RBF)neural network was used to optimize the geometry of the nozzle in consideration of its aerodynamic and electromagnetic scattering characteristics.The results show that the GCPSO method based on RBF can be used to optimize the aerodynamic characteristics of the internal flow and the scattering characteristics of the cavity of the serpentine nozzle with irregular outlet.The optimized model has a higher center offset and a lower ratio of the bases of the trapezoidal outlet after optimization compared to the original model.The optimized model leads to a slight change in aerodynamic performance,with a total pressure recovery coefficient increase of 0.31%and a discharge coefficient increase of 0.41%.In addition,the Radar Cross Section(RCS)decreases also by around 83.33%and the overall performance is significantly improved,with a decrease of the optimized objective function by around 38.74%.展开更多
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProject(20070533131) supported by Research Fund for the Doctoral Program of Higher Education of China
文摘A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle.The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence.In order to reduce the probability of trapping into a local optimal value,an extremum mutation was introduced into simplex-PSO and simplex-PSO-t(simplex-PSO with turbulence) was devised.Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t,and the experimental results confirmed the conclusions:(1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality;(2) compared PSO with chaos PSO(CPSO),the best optimum index increases by a factor of 1×102-1×104.
基金Project(20050079008) supported by the Specialized Research Fund for the Doctoral Program of Higher Education
文摘In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage. To solve this problem, an optimal allocation model based on energy transfer mode was proposed after objective function for optimizing energy using efficiency was established, and then, a new Tabu search and particle swarm hybrid optimizing algorithm was proposed to find solutions. While actual data of energy demand and distribution in China were selected for analysis, the economic critical value in comparison between the long-distance coal transfer and electric power transmission was gained. Based on the above discussion, some proposals were put forward for optimal allocation of energy transfer modes in China. By comparing other three traditional methods that are based on regional price differences, freight rates and annual cost with the proposed method, the result indicates that the economic efficiency of the energy transfer can be enhanced by 3.14%, 5.78% and 6.01%, respectively.
基金supported by the Aeronautical Science Fund of Shaanxi Province of China(20145596025)
文摘Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.
基金the Ministry of Science and Technology of Taiwan (Grants MOST 104-2221-E-327019, MOST 105-2221-E-327-014) for financial support of this study
文摘This paper proposes a systematic method, integrating the uniform design (UD) of experiments and quantum-behaved particle swarm optimization (QPSO), to solve the problem of a robust design for a railway vehicle suspension system. Based on the new nonlinear creep model derived from combining Hertz contact theory, Kalker's linear theory and a heuristic nonlinear creep model, the modeling and dynamic analysis of a 24 degree-of-freedom railway vehicle system were investigated. The Lyapunov indirect method was used to examine the effects of suspension parameters, wheel conicities and wheel rolling radii on critical hunting speeds. Generally, the critical hunting speeds of a vehicle system resulting from worn wheels with different wheel rolling radii are lower than those of a vehicle system having original wheels without different wheel rolling radii. Because of worn wheels, the critical hunting speed of a running railway vehicle substantially declines over the long term. For safety reasons, it is necessary to design the suspension system parameters to increase the robustness of the system and decrease the sensitive of wheel noises. By applying UD and QPSO, the nominal-the-best signal-to-noise ratio of the system was increased from -48.17 to -34.05 dB. The rate of improvement was 29.31%. This study has demonstrated that the integration of UD and QPSO can successfully reveal the optimal solution of suspension parameters for solving the robust design problem of a railway vehicle suspension system.
文摘In this paper, we address one of the issues in the frequency assignment problem for cellular mobile networks in which we intend to minimize the interference levels when assigning frequencies from a limited frequency spectrum. In order to satisfy the increasing demand in such cellular mobile networks, we use a hybrid approach consisting of a Particle Swarm Optimization(PSO) combined with a Tabu Search(TS) algorithm. This approach takes both advantages of PSO efficiency in global optimization and TS in avoiding the premature convergence that would lead PSO to stagnate in a local minimum. Moreover, we propose a new efficient, simple, and inexpensive model for storing and evaluating solution's assignment. The purpose of this model reduces the solution's storage volume as well as the computations required to evaluate thesesolutions in comparison with the classical model. Our simulation results on the most known benchmarking instances prove the effectiveness of our proposed algorithm in comparison with previous related works in terms of convergence rate, the number of iterations, the solution storage volume and the running time required to converge to the optimal solution.
文摘A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.
基金Sponsored by the Teaching and Research Award Program for Outstanding Young Teacher in Higher Education Institute of MOE (20010248)Beijing Education Committee Coorperation Building Foundation
文摘Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. The sufficient conditions for asymptotic stability of an acceleration factor and inertia weight are deduced in this paper. The value of the inertia weight w is enhanced to （ - 1, 1）. Furthermore a new adaptive PSO algorithm--harmonious PSO （HPSO） is proposed and proved that HPSO is a global search algorithm. Finally it is focused on a design task of a servo system controller. Considering the existence of model uncertainty and noise from sensors, HPSO are applied to optimize the parameters of fuzzy PID controller. The experiment results demonstrate the efficiency of the methods.
基金Supported by science and technology development fund of Fuzhou university(2005-XQ-09).
文摘This paper presents a new approach based on the particle swarm optimization(PSO)algorithm for solving the drilling path optimization problem belonging to discrete space.Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence,based on the mathematical algorithm model,the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution.And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator,the improved operator can satisfy the need of integer coding in drilling path optimization.The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize,fast convergence speed,and better global convergence characteris-tics.hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
基金This research was funded by National Key Research and Development Program of China(Grant No.2018YFC1507005)China Postdoctoral Science Foundation(Grant No.2018M643448)+1 种基金Sichuan Science and Technology Program(Grant No.2019YFG0110)Fundamental Research Funds for the Central Universities,Southwest Minzu University(Grant No.2019NQN22).
文摘The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs.Quantum-behaved Particle Swarm Optimization(QPSO)algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling.This paper analyzed the motion pattern of particles in a square potential well,given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well(BC-QSPSO).In this novel algorithm,the intrinsic cognitive link between particles’experience information and group sharing information was created by using normal Copula function.After that,the control parameters chosen strategy gives through experiments.Finally,the simulation results of the test functions show that the improved algorithms outperform the original QPSO algorithm and due to the error gradient information will not be over utilized in square potential well,the particles are easy to jump out of the local optimum,the BC-QSPSO is more suitable to solve the functions with correlative variables.
基金sponsored by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01A16)the Program of the Applied Technology Research and Development of Kashi Prefecture(No.KS2021026).
文摘Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence optimization.However,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems.Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO.Subsequently,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution stages.To be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution stage.Followed by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm diversity.In addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO method.Finally,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.
基金The work was supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutes of MOE, PRC
文摘A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight w is enhanced to （ 1, 1）. Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO （AFHPSO） is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.
基金Supported by Jiangsu Provincical Natural Science Foundation of China(Grant No.BK20140554)National Natural Science Foundation of China(Grant No.51409123)+2 种基金China Postdoctoral Science Foundation(Grant No.2015T80507)Innovation Project for Postgraduates of Jiangsu Province,China(Grant No.KYLX15_1066)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD)
文摘In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the blade outlet width b2, blade outlet angle β2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0Qd and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.
基金This work was supported by the Teaching and Research Award Program for Outstanding Young Teacher in Higher Education Institute of Ministry ofEducation of China(NO.20010248).
文摘Particle swarm optimizer(PSO),a new evolutionary computation algorithm,exhibits good performance for optimization problems,although PSO can not guarantee convergence of a global minimum,even a local minimum.However,there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm.In this paper,the algorithm are analyzed as a time-varying dynamic system,and the sufficient conditions for asymptotic stability of acceleration factors,increment of acceleration factors and inertia weight are deduced.The value of the inertia weight is enhanced to(-1,1).Based on the deduced principle of acceleration factors,a new adaptive PSO algorithm-harmonious PSO(HPSO)is proposed.Furthermore it is proved that HPSO is a global search algorithm.In the experiments,HPSO are used to the model identification of a linear motor driving servo system.An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters,but also determine the order of the model simultaneously.The results demonstrate the effectiveness of HPSO.
文摘BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer （PSO）, guaranteed convergence particle swarm optimizer （GCPSO）, an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.
基金The authors extend their appreciation to the Researchers Supporting Project number(RSP2022R515)King Saud University,Riyadh,Saudi Arabia for funding this research work。
文摘In this work,the concepts of particle swarm optimization-based method,named non-Gaussian improved particle swarm optimization for minimizing the cost of energy(COE)of wind turbines(WTs)on high-altitude sites are introduced.Since the COE depends on site specification constants and initialized parameters of wind turbine,the focus was on the design optimization of rotor radius,hub height and rated power.Based on literature,the COE is converted to the Saudi Arabia context.Thus,the constrained wind turbine optimization problem is developed.Then,non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving the optimization design in wind turbine efficiency under different altitudes ranging from 2500 to 4000 m.The results show that as altitude rises,the optimal rotor radius grows,but the optimal hub height and rated power drop,resulting in an increase in COE.Further,the non-Gaussian method display a faster convergence compared to the classical particle swarm optimization.These findings will be useful as a reference for wind turbine design at high altitudes.Thus,it could be employed to optimize the initialized parameter of wind turbine for the planned and largest wind farm in Saudi Arabia in Dumat Al-Jandal selected site.
基金Sponsored by Science Research Project of Liaoning Education Department(Grant No.LJKZ0143)Open Project of State Key Laboratory of Syn⁃thetical Automation for Process Industries(Grant No.2023⁃kfkt⁃01).
文摘One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize the Bidirectional Long Short⁃term Memory Network(BiLSTM)in order to improve the wind speed prediction accuracy,taking into account the highly stochastic and regular aspects of wind speed.Firstly,the wind speed time sequence is subjected to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information.The BiLSTM model,which incorporates the attention mechanism for prediction,is then fitted to the decomposed data,and its parameters are optimized using the particle swarm technique,reducing errors in predictive modeling.To get the final prediction,the components are finally superimposed.The empirical evidence shows that the CEEMDAN⁃PSO⁃BiLSTM⁃attention model decreases the RMSE(Root⁃Mean⁃Square⁃Error)by 15%-44%,the MAE by 18%-45%,the MAPE by 24%-52%,and the R2 by 1.4%-2.7%in comparison to the BiLSTM and other models.The validation of CEEMDAN⁃PSO⁃BiLSTM⁃attention model in short⁃term wind speed prediction is verified.
基金the financial support of the Fundamental Research Funds for the Central Universities(No.31020190MS708)。
文摘Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and different ratios of the bases of the trapezoidal outlet were generated based on curvature control regulation.Computational Fluid Dynamics(CFD)simulations have been conducted to obtain the flow field in the nozzle,and Forward-Backward Iterative Physical Optics(FBIPO)method was applied to study the electromagnetic scattering characteristics of the nozzle.Guarantee Convergence Particle Swarm Optimization(GCPSO)algorithm based on Radial Basis Function(RBF)neural network was used to optimize the geometry of the nozzle in consideration of its aerodynamic and electromagnetic scattering characteristics.The results show that the GCPSO method based on RBF can be used to optimize the aerodynamic characteristics of the internal flow and the scattering characteristics of the cavity of the serpentine nozzle with irregular outlet.The optimized model has a higher center offset and a lower ratio of the bases of the trapezoidal outlet after optimization compared to the original model.The optimized model leads to a slight change in aerodynamic performance,with a total pressure recovery coefficient increase of 0.31%and a discharge coefficient increase of 0.41%.In addition,the Radar Cross Section(RCS)decreases also by around 83.33%and the overall performance is significantly improved,with a decrease of the optimized objective function by around 38.74%.
