As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid ...As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.展开更多
An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from its...An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.展开更多
This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution syste...This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems.The proposed GUI based toolbox,allows the user to choose between single and multiple DFACTS allocations,followed by the type and number of them to be allocated.The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS.This is found to be faster and provides more accurate results compared to the usual PSO and BFO.Results obtained with MATLAB/Simulink simulations are compared with PSO,BFO and enhanced BFO.It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality.Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC,APC and DSTATCOM is preferable to reduce power losses,improve load balancing and enhance voltage deviation index to 70%,38% and 132% respectively and also it can improve loading factor without additional power loss.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
The problems associated with vibrations of viaducts and low-frequency structural noise radiation caused by train excitation continue to increase in importance.A new floating-slab track vibration isolator-non-obstructi...The problems associated with vibrations of viaducts and low-frequency structural noise radiation caused by train excitation continue to increase in importance.A new floating-slab track vibration isolator-non-obstructive particle damping-phononic crystal vibration isolator is proposed herein,which uses the particle damping vibration absorption technology and bandgap vibration control theory.The vibration reduction performance of the NOPD-PCVI was analyzed from the perspective of vibration control.The paper explores the structure-borne noise reduction performance of the NOPD-PCVIs installed on different bridge structures under varying service conditions encountered in practical engineering applications.The load transferred to the bridge is obtained from a coupled train-FST-bridge analytical model considering the different structural parameters of bridges.The vibration responses are obtained using the finite element method,while the structural noise radiation is simulated using the frequency-domain boundary element method.Using the particle swarm optimization algorithm,the parameters of the NOPD-PCVI are optimized so that its frequency bandgap matches the dominant bridge structural noise frequency range.The noise reduction performance of the NOPD-PCVIs is compared to the steel-spring isolation under different service conditions.展开更多
A new multi-species particle swarm optimization with a two-level hierarchical topology and the orthogonal learning strategy(OMSPSO) is proposed, which enhances the global search ability of particles and increases thei...A new multi-species particle swarm optimization with a two-level hierarchical topology and the orthogonal learning strategy(OMSPSO) is proposed, which enhances the global search ability of particles and increases their convergence rates. The numerical results on 10 benchmark functions demonstrated the effectiveness of our proposed algorithm. Then, the proposed algorithm is presented to design a butterfly-shaped microstrip patch antenna. Combined with the HFSS solver, a butterfly-shaped patch antenna with a bandwidth of about 40.1% is designed by using the proposed OMSPSO. The return loss of the butterfly-shaped antenna is greater than 10 d B between 4.15 and 6.36 GHz. The antenna can serve simultaneously for the high-speed wireless computer networks(5.15–5.35 GHz) and the RFID systems(5.8 GHz).展开更多
By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) for...By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.展开更多
A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning fre- quency points are fixed in each frequency interval in the standard Oustaloup approximation. In th...A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning fre- quency points are fixed in each frequency interval in the standard Oustaloup approximation. In the improved Oustaloup method, the turning frequency points are determined by the adaptive chaotic particle swarm optimization （PSO）. The average velocity is proposed to reduce the iterations of the PSO. The chaotic search scheme is combined to reduce the opportunity of the premature phenomenon. Two fitness functions are given to minimize the zero-pole and amplitude-phase frequency errors for the underlying optimization problems. Some numerical examples are compared to demonstrate the effectiveness and accuracy of this proposed rational approximation method.展开更多
This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations amo...This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem （RACP） by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion （PPSO） combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.展开更多
This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,le...This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.展开更多
Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors ...Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.展开更多
This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he...This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.展开更多
This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state t...This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.展开更多
A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems....A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.展开更多
A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm...A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.展开更多
A new version of particle swarm optimization(PSO) called discontinuous flying particle swarm optimization(DFPSO) was proposed,where not all of the particles refreshed their positions and velocities during each iterati...A new version of particle swarm optimization(PSO) called discontinuous flying particle swarm optimization(DFPSO) was proposed,where not all of the particles refreshed their positions and velocities during each iteration step and the probability of each particle in refreshing its position and velocity was dependent on its objective function value.The effect of population size on the results was investigated.The results obtained by DFPSO have an average difference of 6% compared with those by PSO,whereas DFPSO consumes much less evaluations of objective function than PSO does.展开更多
As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of c...As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.展开更多
A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented. Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune se...A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented. Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune selection mechanisms were used to prevent the undulate phenomenon during the evolutionary process. The algorithm was introduced through an application in the direct maintenance cost (DMC) estimation of aircraft components. Experiments results show that the algorithm can compute simply and run quickly. It resolves the combinatorial optimization problem of component DMC estimation with simple and available parameters. And it has higher accuracy than individual methods, such as PLS, BP and v-SVM, and also has better performance than other combined methods, such as basic PSO and BP neural network.展开更多
Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard...Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard particle swarm optimization(SPSO),and then the searching behavior of the particle swarm is discussed and the change of the particles’distribution during the iteration process is studied.The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively,thus PSO achieves remarkable results in the inversion of SP anomalies.Finally,six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions,inertia weights and learning factors are introduced for the inversion of the cylinder model,and the effectiveness of these algorithms is verified by numerical experiments.The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value,and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.展开更多
A numerical procedure for reliability analysis of earth slope based on advanced first-order second-moment method is presented,while soil properties and pore water pressure may be considered as random variables.The fac...A numerical procedure for reliability analysis of earth slope based on advanced first-order second-moment method is presented,while soil properties and pore water pressure may be considered as random variables.The factor of safety and performance function is formulated utilizing a new approach of the Morgenstern and Price method.To evaluate the minimum reliability index defined by Hasofer and Lind and corresponding critical probabilistic slip surface,a hybrid algorithm combining chaotic particle swarm optimization and harmony search algorithm called CPSOHS is presented.The comparison of the results of the presented method,standard particle swarm optimization,and selected other methods employed in previous studies demonstrates the superior successful functioning of the new method by evaluating lower values of reliability index and factor of safety.Moreover,the presented procedure is applied for sensitivity analysis and the obtained results show the influence of soil strength parameters and probability distribution types of random variables on the reliability index of slopes.展开更多
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education(20114307120032)the National Natural Science Foundation of China(71201167)
文摘As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.
基金supported by the National Natural Science Foundation of China (60873086)the Aeronautical Science Foundation of China(20085153013)the Fundamental Research Found of Northwestern Polytechnical Unirersity (JC200942)
文摘An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.
基金Project supported by Borujerd Branch,Islamic Azad University,Iran
文摘This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems.The proposed GUI based toolbox,allows the user to choose between single and multiple DFACTS allocations,followed by the type and number of them to be allocated.The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS.This is found to be faster and provides more accurate results compared to the usual PSO and BFO.Results obtained with MATLAB/Simulink simulations are compared with PSO,BFO and enhanced BFO.It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality.Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC,APC and DSTATCOM is preferable to reduce power losses,improve load balancing and enhance voltage deviation index to 70%,38% and 132% respectively and also it can improve loading factor without additional power loss.
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Project(51978585)supported by the National Natural Science Foundation,ChinaProject(2022YFB2603404)supported by the National Key Research and Development Program,China+1 种基金Project(U1734207)supported by the High-speed Rail Joint Fund Key Projects of Basic Research,ChinaProject(2023NSFSC1975)supported by the Sichuan Nature and Science Foundation Innovation Research Group Project,China。
文摘The problems associated with vibrations of viaducts and low-frequency structural noise radiation caused by train excitation continue to increase in importance.A new floating-slab track vibration isolator-non-obstructive particle damping-phononic crystal vibration isolator is proposed herein,which uses the particle damping vibration absorption technology and bandgap vibration control theory.The vibration reduction performance of the NOPD-PCVI was analyzed from the perspective of vibration control.The paper explores the structure-borne noise reduction performance of the NOPD-PCVIs installed on different bridge structures under varying service conditions encountered in practical engineering applications.The load transferred to the bridge is obtained from a coupled train-FST-bridge analytical model considering the different structural parameters of bridges.The vibration responses are obtained using the finite element method,while the structural noise radiation is simulated using the frequency-domain boundary element method.Using the particle swarm optimization algorithm,the parameters of the NOPD-PCVI are optimized so that its frequency bandgap matches the dominant bridge structural noise frequency range.The noise reduction performance of the NOPD-PCVIs is compared to the steel-spring isolation under different service conditions.
基金Project(61105067)supported by the National Natural Science Foundation of China
文摘A new multi-species particle swarm optimization with a two-level hierarchical topology and the orthogonal learning strategy(OMSPSO) is proposed, which enhances the global search ability of particles and increases their convergence rates. The numerical results on 10 benchmark functions demonstrated the effectiveness of our proposed algorithm. Then, the proposed algorithm is presented to design a butterfly-shaped microstrip patch antenna. Combined with the HFSS solver, a butterfly-shaped patch antenna with a bandwidth of about 40.1% is designed by using the proposed OMSPSO. The return loss of the butterfly-shaped antenna is greater than 10 d B between 4.15 and 6.36 GHz. The antenna can serve simultaneously for the high-speed wireless computer networks(5.15–5.35 GHz) and the RFID systems(5.8 GHz).
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.
基金supported by the National Natural Science Foundation of China (10872030)
文摘A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning fre- quency points are fixed in each frequency interval in the standard Oustaloup approximation. In the improved Oustaloup method, the turning frequency points are determined by the adaptive chaotic particle swarm optimization （PSO）. The average velocity is proposed to reduce the iterations of the PSO. The chaotic search scheme is combined to reduce the opportunity of the premature phenomenon. Two fitness functions are given to minimize the zero-pole and amplitude-phase frequency errors for the underlying optimization problems. Some numerical examples are compared to demonstrate the effectiveness and accuracy of this proposed rational approximation method.
基金supported by the National Natural Science Foundation of China(7120116671201170)
文摘This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem （RACP） by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion （PPSO） combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.
基金supported by the National Natural Science Foundation of China(6167321461673217+2 种基金61673219)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(18KJB120011)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19_0299)
文摘This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.
文摘Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.
基金Project supported by Faculty of Technology,Department of Electrical Engineering,University of Batna,Algeria
文摘This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.
基金supported by the Chinese Ministry of Science and Intergovernmental Cooperation Project (2009DFA12870)the National Science Foundation of China (60974062,60972119)
文摘This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update stage.In the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model fitting.Furthermore,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process.There are three main contributions.Firstly,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy phenomenon.Secondly,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle diversity.Finally,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and accuracy.Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.
基金Projects(50275150,61173052) supported by the National Natural Science Foundation of ChinaProject(14FJ3112) supported by the Planned Science and Technology of Hunan Province,ChinaProject(14B033) supported by Scientific Research Fund Education Department of Hunan Province,China
文摘A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.
文摘A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.
基金Project(50874064) supported by the National Natural Science Foundation of ChinaKey Project(Z2007F10) supported by the Natural Science Foundation of Shandong Province,China
文摘A new version of particle swarm optimization(PSO) called discontinuous flying particle swarm optimization(DFPSO) was proposed,where not all of the particles refreshed their positions and velocities during each iteration step and the probability of each particle in refreshing its position and velocity was dependent on its objective function value.The effect of population size on the results was investigated.The results obtained by DFPSO have an average difference of 6% compared with those by PSO,whereas DFPSO consumes much less evaluations of objective function than PSO does.
基金Project(2011ZK2030)supported by the Soft Science Research Plan of Hunan Province,ChinaProject(2010ZDB42)supported by the Social Science Foundation of Hunan Province,China+1 种基金Projects(09A048,11B070)supported by the Science Research Foundation of Education Bureau of Hunan Province,ChinaProjects(2010GK3036,2011FJ6049)supported by the Science and Technology Plan of Hunan Province,China
文摘As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.
文摘A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented. Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune selection mechanisms were used to prevent the undulate phenomenon during the evolutionary process. The algorithm was introduced through an application in the direct maintenance cost (DMC) estimation of aircraft components. Experiments results show that the algorithm can compute simply and run quickly. It resolves the combinatorial optimization problem of component DMC estimation with simple and available parameters. And it has higher accuracy than individual methods, such as PLS, BP and v-SVM, and also has better performance than other combined methods, such as basic PSO and BP neural network.
基金Projects(41874145,72088101)supported by the National Natural Science Foundation of China。
文摘Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard particle swarm optimization(SPSO),and then the searching behavior of the particle swarm is discussed and the change of the particles’distribution during the iteration process is studied.The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively,thus PSO achieves remarkable results in the inversion of SP anomalies.Finally,six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions,inertia weights and learning factors are introduced for the inversion of the cylinder model,and the effectiveness of these algorithms is verified by numerical experiments.The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value,and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.
基金supported by the Ministry of Higher Education, Malaysia (Grant No.UKM-AP-PLW-04-2009/2)
文摘A numerical procedure for reliability analysis of earth slope based on advanced first-order second-moment method is presented,while soil properties and pore water pressure may be considered as random variables.The factor of safety and performance function is formulated utilizing a new approach of the Morgenstern and Price method.To evaluate the minimum reliability index defined by Hasofer and Lind and corresponding critical probabilistic slip surface,a hybrid algorithm combining chaotic particle swarm optimization and harmony search algorithm called CPSOHS is presented.The comparison of the results of the presented method,standard particle swarm optimization,and selected other methods employed in previous studies demonstrates the superior successful functioning of the new method by evaluating lower values of reliability index and factor of safety.Moreover,the presented procedure is applied for sensitivity analysis and the obtained results show the influence of soil strength parameters and probability distribution types of random variables on the reliability index of slopes.
