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
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
During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was establis...To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.展开更多
In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using...In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.展开更多
This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users...This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users（MSU）. A group of atom services, each of which has its level of quality of service（QoS）, can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users＇ will. In this paper a method based on discrete particle swarm optimization（DPSO） is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU＇s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization（PSO） and genetic algorithm（GA）.展开更多
An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learnin...An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.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
A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, ...A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.展开更多
The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the orig...The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentum-PSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO （LDW-PSO） equipped with a boundary checking strategy is also discussed, which is denoted as LDWBC-PSO. LDW-PSO, LDWBC-PSO and momentum-PSO are compared in optimization on five test functions. The experimental results show that in some special cases LDW-PSO finds invalid solutions and LDWBC-PSO has poor performance, while momentum-PSO not only exhibits good performance but also reduces computational cost for updating velocities.展开更多
Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been ...Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial （NP）-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine （UCI） data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.展开更多
Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artifici...Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.展开更多
To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO al...To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and inte-grates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evo-lution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection opti-mization of paths, gradually reducing algorithmic space, accele-rating convergence, and enhances path cooperativity. Simula-tion experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demon-strate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. More-over it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collabora-tive path planning. The experiments are conducted in three col-laborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.展开更多
Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation dur...Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.展开更多
A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the...A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.展开更多
The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the ...The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the adoption of the improved PSO algorithm were solved and the results were analyzed. The results show that it is beneficial to obtaining the optimal solution by increasing the number of particles but that will also increase the operation time. On the aspects of solving continuous differentiable non-linear optimization model with equality and inequality constraints, the optimization result of PSO algorithm is the same as that of the interior point method. Compared with genetic algorithms (GA), PSO algorithm is more effective in the local optimization, and unlike GA, it will not be early maturity. Meanwhile, PSO algorithm is also more effective in the boundary optimization than genetic algorithm.展开更多
With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex ele...With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.展开更多
Rural vitalization is a major strategy for reform and development of agriculture and rural areas in China,the key task of which is improving rural living environment.Imperfect rural solid waste(RSW)collection and tran...Rural vitalization is a major strategy for reform and development of agriculture and rural areas in China,the key task of which is improving rural living environment.Imperfect rural solid waste(RSW)collection and transportation system exacerbates the pollution of RSW to rural living environment,while it has not been established and improved in the cold region of Northern China due to climate and economy.Through the analysis of the current situation of RSW source separation,collection,transportation and disposal in China,an RSW collection and transportation system suitable for the northern cold region was developed.Considering the low winter temperature in the northern cold region,different requirements for RSW collection,transportation and terminal disposal,scattered source points and single terminal disposal nodes in rural areas,the study focused on determining the number and location of transfer stations,established a model for transfer stations selection and RSW collection and transportation routes optimization for RSW collection and transportation system,and proposed the elite retention particle swarm optimization–genetic algorithm(ERPSO–GA).The rural area of Baiquan County was taken as a representative case,the collection and transportation scheme of which was given,and the feasibility of the scheme was clarified by simulation experiment.展开更多
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.
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(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
基金Project(2012B091100444)supported by the Production,Education and Research Cooperative Program of Guangdong Province and Ministry of Education,ChinaProject(2013ZM0091)supported by Fundamental Research Funds for the Central Universities of China
文摘To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.
基金Project(70671040) supported by the National Natural Science Foundation of China
文摘In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.
基金supported by the National Natural Science Foundation of China(61573283)
文摘This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users（MSU）. A group of atom services, each of which has its level of quality of service（QoS）, can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users＇ will. In this paper a method based on discrete particle swarm optimization（DPSO） is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU＇s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization（PSO） and genetic algorithm（GA）.
基金Project(50579101) supported by the National Natural Science Foundation of China
文摘An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.
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Project(50579101) supported by the National Natural Science Foundation of China
文摘A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
文摘The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentum-PSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO （LDW-PSO） equipped with a boundary checking strategy is also discussed, which is denoted as LDWBC-PSO. LDW-PSO, LDWBC-PSO and momentum-PSO are compared in optimization on five test functions. The experimental results show that in some special cases LDW-PSO finds invalid solutions and LDWBC-PSO has poor performance, while momentum-PSO not only exhibits good performance but also reduces computational cost for updating velocities.
基金supported by the National Natural Science Foundation of China (60873069 61171132)+3 种基金the Jiangsu Government Scholarship for Overseas Studies (JS-2010-K005)the Funding of Jiangsu Innovation Program for Graduate Education (CXZZ11 0219)the Open Project Program of Jiangsu Provincial Key Laboratory of Computer Information Processing Technology (KJS1023)the Applying Study Foundation of Nantong (BK2011062)
文摘Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial （NP）-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine （UCI） data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.
文摘Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.
基金supported by Hunan Provincial Natural Science Foundation(2024JJ5173,2023JJ50047)Hunan Provincial Department of Education Scientific Research Project(23A0494)Hunan Provincial Innovation Foundation for Postgraduate(CX20231221).
文摘To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and inte-grates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evo-lution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection opti-mization of paths, gradually reducing algorithmic space, accele-rating convergence, and enhances path cooperativity. Simula-tion experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demon-strate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. More-over it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collabora-tive path planning. The experiments are conducted in three col-laborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.
基金supported by the National Natural Science Foundation of China(No.61573286)the Aeronautical Science Foundation of China(No.20180753006)+2 种基金the Fundamental Research Funds for the Central Universities(3102019ZDHKY07)the Natural Science Foundation of Shaanxi Province(2020JQ-218)the Shaanxi Province Key Laboratory of Flight Control and Simulation Technology。
文摘Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.
基金Project(2008BA00400)supported by the Foundation of Department of Science and Technology of Jiangxi Province,China
文摘A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the adoption of the improved PSO algorithm were solved and the results were analyzed. The results show that it is beneficial to obtaining the optimal solution by increasing the number of particles but that will also increase the operation time. On the aspects of solving continuous differentiable non-linear optimization model with equality and inequality constraints, the optimization result of PSO algorithm is the same as that of the interior point method. Compared with genetic algorithms (GA), PSO algorithm is more effective in the local optimization, and unlike GA, it will not be early maturity. Meanwhile, PSO algorithm is also more effective in the boundary optimization than genetic algorithm.
文摘With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.
基金Supported by Heilongjiang Province Philosophy and Social Science Planning Research Project(22JYB232)。
文摘Rural vitalization is a major strategy for reform and development of agriculture and rural areas in China,the key task of which is improving rural living environment.Imperfect rural solid waste(RSW)collection and transportation system exacerbates the pollution of RSW to rural living environment,while it has not been established and improved in the cold region of Northern China due to climate and economy.Through the analysis of the current situation of RSW source separation,collection,transportation and disposal in China,an RSW collection and transportation system suitable for the northern cold region was developed.Considering the low winter temperature in the northern cold region,different requirements for RSW collection,transportation and terminal disposal,scattered source points and single terminal disposal nodes in rural areas,the study focused on determining the number and location of transfer stations,established a model for transfer stations selection and RSW collection and transportation routes optimization for RSW collection and transportation system,and proposed the elite retention particle swarm optimization–genetic algorithm(ERPSO–GA).The rural area of Baiquan County was taken as a representative case,the collection and transportation scheme of which was given,and the feasibility of the scheme was clarified by simulation experiment.
