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Sample records for improved particle swarm

  1. An Improved Particle Swarm Optimization for Traveling Salesman Problem

    Science.gov (United States)

    Liu, Xinmei; Su, Jinrong; Han, Yan

    In allusion to particle swarm optimization being prone to get into local minimum, an improved particle swarm optimization algorithm is proposed. The algorithm draws on the thinking of the greedy algorithm to initialize the particle swarm. Two swarms are used to optimize synchronously. Crossover and mutation operators in genetic algorithm are introduced into the new algorithm to realize the sharing of information among swarms. We test the algorithm with Traveling Salesman Problem with 14 nodes and 30 nodes. The result shows that the algorithm can break away from local minimum earlier and it has high convergence speed and convergence ratio.

  2. Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.

    Science.gov (United States)

    Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

    2013-01-01

    The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

  3. Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization

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    Zeng-Shun Zhao

    2013-01-01

    Full Text Available The Rao-Blackwellized particle filter (RBPF algorithm usually has better performance than the traditional particle filter (PF by utilizing conditional dependency relationships between parts of the state variables to estimate. By doing so, RBPF could not only improve the estimation precision but also reduce the overall computational complexity. However, the computational burden is still too high for many real-time applications. To improve the efficiency of RBPF, the particle swarm optimization (PSO is applied to drive all the particles to the regions where their likelihoods are high in the nonlinear area. So only a small number of particles are needed to participate in the required computation. The experimental results demonstrate that this novel algorithm is more efficient than the standard RBPF.

  4. An Improved Particle Swarm Optimization for Feature Selection

    Institute of Scientific and Technical Information of China (English)

    Yuanning Liu; Gang Wang; Huiling Chen; Hao Dong; Xiaodong Zhu; Sujing Wang

    2011-01-01

    Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO (MSPSO) to solve discrete problems,which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method. The IFS method aims to achieve higher generalization capability through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm (GA) based and the grid search based methods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.

  5. Optimal PMU Placement By Improved Particle Swarm Optimization

    DEFF Research Database (Denmark)

    Rather, Zakir Hussain; Liu, Leo; Chen, Zhe;

    2013-01-01

    This paper presents an improved method of binary particle swarm optimization (IBPSO) technique for optimal phasor measurement unit (PMU) placement in a power network for complete system observability. Various effective improvements have been proposed to enhance the efficiency and convergence rate...... of conventional particle swarm optimization method. The proposed method of IBPSO ensures optimal PMU placement with and without consideration of zero injection measurements. The proposed method has been applied to standard test systems like 17 bus, IEEE 24-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus system...

  6. An Improved Particle Swarm Optimization Algorithm Based on Ensemble Technique

    Institute of Scientific and Technical Information of China (English)

    SHI Yan; HUANG Cong-ming

    2006-01-01

    An improved particle swarm optimization (PSO) algorithm based on ensemble technique is presented. The algorithm combines some previous best positions (pbest) of the particles to get an ensemble position (Epbest), which is used to replace the global best position (gbest). It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.

  7. Particle Swarm Optimization

    Science.gov (United States)

    Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

    2002-01-01

    The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

  8. Improvement of Interior Ballistic Performance Utilizing Particle Swarm Optimization

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    Hazem El Sadek

    2014-01-01

    Full Text Available This paper investigates the interior ballistic propelling charge design using the optimization methods to select the optimum charge design and to improve the interior ballistic performance. The propelling charge consists of a mixture propellant of seven-perforated granular propellant and one-hole tubular propellant. The genetic algorithms and some other evolutionary algorithms have complex evolution operators such as crossover, mutation, encoding, and decoding. These evolution operators have a bad performance represented in convergence speed and accuracy of the solution. Hence, the particle swarm optimization technique is developed. It is carried out in conjunction with interior ballistic lumped-parameter model with the mixture propellant. This technique is applied to both single-objective and multiobjective problems. In the single-objective problem, the optimization results are compared with genetic algorithm and the experimental results. The particle swarm optimization introduces a better performance of solution quality and convergence speed. In the multiobjective problem, the feasible region provides a set of available choices to the charge’s designer. Hence, a linear analysis method is adopted to give an appropriate set of the weight coefficients for the objective functions. The results of particle swarm optimization improved the interior ballistic performance and provided a modern direction for interior ballistic propelling charge design of guided projectile.

  9. Improved Quantum Particle Swarm Optimization for Mangroves Classification

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    Zhehuang Huang

    2016-01-01

    Full Text Available Quantum particle swarm optimization (QPSO is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

  10. Economic Dispatch Thermal Generator Using Modified Improved Particle Swarm Optimization

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    M. Natsir Rahman

    2012-07-01

    Full Text Available Fuel cost of a thermal generator is its own load functions. In this research, Modified Improved Particle Swarm Optimization (MIPSO is applied to calculate economic dispatch. Constriction Factor Approach (CFA is used to modify IPSO algorithm because of the advantage to improve the ability of global searching and avoid local minimum so that the time needed to converge become faster. Simulation results achieved by using  MIPSO method at the time of peak load of of 9602 MW, obtained generation cost is Rp 7,366,912,798,34 per hour, while generation cost of real system is Rp. 7,724,012,070.30 per hour. From the simulation result can be concluded that MIPSO can reduce the generation cost of  500 kV Jawa Bali transmission system of Rp 357,099,271.96 per hour or equal to 4,64%.

  11. Economic Dispatch Thermal Generator Using Modified Improved Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    M. Natsir Rahman

    2012-09-01

    Full Text Available Fuel cost of a thermal generator is its own load functions. In this research, Modified Improved Particle Swarm Optimization (MIPSO is applied to calculate economic dispatch. Constriction Factor Approach (CFA is used to modify IPSO algorithm because of the advantage to improve the ability of global searching and avoid local minimum so that the time needed to converge become faster. Simulation results achieved by using  MIPSO method at the time of peak load of of 9602 MW, obtained generation cost is Rp 7,366,912,798,34 per hour, while generation cost of real system is Rp. 7,724,012,070.30 per hour. From the simulation result can be concluded that MIPSO can reduce the generation cost of  500 kV Jawa Bali transmission system of Rp 357,099,271.96 per hour or equal to 4,64%.

  12. Improved quantum-behaved particle swarm optimization with local search strategy

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    Maolong Xi

    2017-03-01

    Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

  13. Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions

    Science.gov (United States)

    Basu, Mousumi

    2016-12-01

    Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.

  14. An Improved Particle Swarm Optimization Algorithm with Harmony Strategy for the Location of Critical Slip Surface of Slopes

    Institute of Scientific and Technical Information of China (English)

    LI Liang; CHU Xue-song

    2011-01-01

    The determination of optimal values for three parameters required in the original particle swarm optimization algorithm is very difficult. It is proposed that two new parameters simulating the harmony search strategy can be adopted instead of the three parameters which are required in the original particle swarm optimization algorithm to update the positions of all the particles. The improved particle swarm optimization is used in the location of the critical slip surface of soil slope, and it is found that the improved particle swarm optimization algorithm is insensitive to the two parameters while the original particle swarm optimization algorithm can be sensitive to its three parameters.

  15. Particle Swarm Optimization Toolbox

    Science.gov (United States)

    Grant, Michael J.

    2010-01-01

    The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry

  16. An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization

    Directory of Open Access Journals (Sweden)

    Zhen-Lun Yang

    2015-01-01

    Full Text Available An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.

  17. An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization.

    Science.gov (United States)

    Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing

    2015-01-01

    An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.

  18. An Improved Particle Swarm Optimization Algorithm and Its Application in the Community Division

    Directory of Open Access Journals (Sweden)

    Jiang Hao

    2016-01-01

    Full Text Available With the deepening of the research on complex networks, the method of detecting and classifying social network is springing up. In this essay, the basic particle swarm algorithm is improved based on the GN algorithm. Modularity is taken as a measure of community division [1]. In view of the dynamic network community division, scrolling calculation method is put forward. Experiments show that using the improved particle swarm optimization algorithm can improve the accuracy of the community division and can also get higher value of the modularity in the dynamic community

  19. Improved SpikeProp for Using Particle Swarm Optimization

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    Falah Y. H. Ahmed

    2013-01-01

    Full Text Available A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

  20. A BP Neural Network Based on Improved Particle Swarm Optimization and its Application in Reliability Forecasting

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    Heqing Li

    2013-07-01

    Full Text Available The basic Particle Swarm Optimization (PSO algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow full play to the approximation capability of the function of BP neural network and overcome the main shortcomings of its liability to fall into local extreme value and the study proposed a concept of applying improved PSO algorithm and BP network jointly to optimize the original weight and threshold value of network and incorporating the improved PSO algorithm into BP network to establish a improved PSO-BP network system. This method improves convergence speed and the ability to search optimal value. We apply the improved particle swarm algorithm to reliability prediction. Compared with the traditional BP method, this kind of algorithm can minimize errors and improve convergence speed at the same time.

  1. A new support vector machine optimized by improved particle swarm optimization and its application

    Institute of Scientific and Technical Information of China (English)

    LI Xiang; YANG Shang-dong; QI Jian-xun

    2006-01-01

    A new support vectormachine (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 improved 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.

  2. Research on Demand Prediction of Fresh Food Supply Chain Based on Improved Particle Swarm Optimization Algorithm

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    He Wang

    2015-04-01

    Full Text Available Demand prediction of supply chain is an important content and the first premise in supply management of different enterprises and has become one of the difficulties and hot research fields for the researchers related. The paper takes fresh food demand prediction for example and presents a new algorithm for predicting demand of fresh food supply chain. First, the working principle and the root causes of the defects of particle swarm optimization algorithm are analyzed in the study; Second, the study designs a new cloud particle swarm optimization algorithm to guarantee the effectiveness of particles in later searching phase and redesigns its cloud global optimization searching method and crossover operation; Finally, a certain fresh food supply chain is taken for example to illustrate the validity and feasibility of the improved algorithm and the experimental results show that the improved algorithm can improve prediction accuracy and calculation efficiency when used for demand prediction of fresh food supply chain.

  3. Improved particle swarm optimization algorithm for android medical care IOT using modified parameters.

    Science.gov (United States)

    Sung, Wen-Tsai; Chiang, Yen-Chun

    2012-12-01

    This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.

  4. Momentum particle swarm optimizer

    Institute of Scientific and Technical Information of China (English)

    Liu Yu; Qin Zheng; Wang Xianghua; He Xingshi

    2005-01-01

    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.

  5. Particle Swarm Optimization with Double Learning Patterns.

    Science.gov (United States)

    Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

    2016-01-01

    Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

  6. Distribution Grid Reactive Power Optimization Based on Improved Cloud Particle Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Hongsheng Su

    2013-01-01

    Full Text Available To resolve the problems that cloud particle swarm optimization(CPSO was easily trapped in local minimum and possessed slow convergence speed and early-maturing during distribution grid reactive power optimization, CPSO algorithm was improved based on cloud digital features in this paper. The method firstly combined Local search with global search together based on solution space transform, where the crossover and mutation operation of the particles were implemented based on normal cloud operator. And then the dramatic achievements were acquired in time-consuming and storage-cost using the improved algorithm. Finally, applied in bus IEEE30 system, the simulation results show that the better global solution is attained using the improved CPSO algorithm, and its convergence speed and accuracy possesses a dramatic improvement.

  7. State Feedback H∞ Control of Power Units Based on an Improved Particle Swarm Optimization

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    Zhongqiang Wu

    2015-01-01

    Full Text Available A new state feedback H∞ control scheme is presented used in the boiler-turbine power units based on an improved particle swarm optimizing algorithm. Firstly, the nonlinear system is transformed into a linear time-varying system; then the H∞ control problem is transformed into the solution of a Riccati equation. The control effect of H∞ controller depends on the selection of matrix P, so an improved particle swarm optimizing (PSO algorithm by introducing differential evolution algorithm is used to solve the Riccati equation. The main purpose is that mutation and crossover are introduced for a new population, and the population diversity is improved. It is beneficial to eliminate stagnation caused by premature convergence, and the algorithm convergence rate is improved. Finally, the real-time optimizing of the controller parameters is realized. Theoretical analysis and simulation results show that a state feedback H∞ controller can be obtained, which can ensure asymptotic stability of the system, and the double objectives of stabilizing system and suppressing the disturbance are got. The system can work well over a large range working point.

  8. Optimization of the reflux ratio for a stage distillation column based on an improved particle swarm algorithm

    DEFF Research Database (Denmark)

    Ren, Jingzheng; Tan, Shiyu; Dong, Lichun

    2010-01-01

    the searching ability of basic particle swarm algorithm significantly. An example of utilizing the improved algorithm to solve the mathematical model was demonstrated; the result showed that it is efficient and convenient to optimize the reflux ratio for a distillation column by using the mathematical model......A mathematical model relating operation profits with reflux ratio of a stage distillation column was established. In order to optimize the reflux ratio by solving the nonlinear objective function, an improved particle swarm algorithm was developed and has been proved to be able to enhance...

  9. Immunity clone algorithm with particle swarm evolution

    Institute of Scientific and Technical Information of China (English)

    LIU Li-jue; CAI Zi-xing; CHEN Hong

    2006-01-01

    Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algorithm lies in two aspects.Via immunity operation, the diversity of the antibodies was maintained, and the speed of convergent was improved by using particle swarm evolution equations. Simulation programme and three functions were used to check the effect of the algorithm. The advanced algorithm were compared with clonal selection algorithm and particle swarm algorithm. The results show that this advanced algorithm can converge to the global optimum at a great rate in a given range, the performance of optimization is improved effectively.

  10. Voltage Profile Improvement in Distribution System Using Particle Swarm Optimization Algorithm

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    V.Veera Nagireddy

    2016-06-01

    Full Text Available The traditional method in electric power distribution is to have centralized plants distributing electricity through an extensive distribution network. Distributed generation (DG provides electric power at a site closer to the customer which reduces the transmission and distribution costs, reduces fossil fuel emissions, capital cost, reduce maintenance costs and improve the distribution feeder voltage profiles. In the case of small generation systems, the locations of DG and penetration level of DG are usually not priori known. In this paper, Particle Swarm Optimization (PSO algorithm attempts to calculate the boundaries of the randomly placed distributed generators in a distribution network. simulations are performed using MATLAB, and overall better improvements are determined with estimated DG size and location. The proposed PSO approach is compared with conventional method on IEEE 34 bus distribution feeder network

  11. Improved wavelet neural network combined with particle swarm optimization algorithm and its application

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    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.

  12. Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

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    Jude Hemanth Duraisamy

    2016-01-01

    Full Text Available Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA and Particle Swarm Optimization (PSO have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT and Finite Ridgelet Transform (FRIT are used in combination with GA and PSO to improve the efficiency of the image steganography system.

  13. Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

    Science.gov (United States)

    Jude Hemanth, Duraisamy; Umamaheswari, Subramaniyan; Popescu, Daniela Elena; Naaji, Antoanela

    2016-01-01

    Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.

  14. Improved cuckoo search with particle swarm optimization for classification of compressed images

    Indian Academy of Sciences (India)

    Vamsidhar Enireddy; Reddi Kiran Kumar

    2015-12-01

    The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.

  15. Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization

    Science.gov (United States)

    Modiri, Arezoo; Gu, Xuejun; Hagan, Aaron M.; Sawant, Amit

    2016-01-01

    Objective Evolutionary stochastic global optimization algorithms are widely used in large-scale, non-convex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. Methods We use particle swarm optimization (PSO) algorithm to solve a 4-dimensional radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally-used unconstrained, hard-constrained and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm- a popular RT optimization technique is also implemented and used. Results The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. Conclusion The proposed virtual search approach boosts the swarm search efficiency and, consequently, improves the optimization convergence rate and robustness for PSO. Significance RT planning is a large-scale, non-convex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems. PMID:27362755

  16. An Extended Particle Swarm Optimizer

    Institute of Scientific and Technical Information of China (English)

    XU Jun-jie; XIN Zhan-hong

    2005-01-01

    An Extended Particle Swarm Optimizer (EPSO) is proposed in this paper. In this new algorithm, not only the local but also the global best position will impact the particle's velocity updating process. EPSO is an integration of Local Best paradigm (LBEST) and Global Best paradigm (GBEST) and it significantly enhances the performance of the conventional particle swarm optimizers. The experiment results have proved that EPSO deserves to be investigated.

  17. An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line

    Directory of Open Access Journals (Sweden)

    Zhengwu Fan

    2017-01-01

    Full Text Available In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO. Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature.

  18. Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    Hai-bin Duan; Guan-jun Ma; De-lin Luo

    2008-01-01

    Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles (UCAVs) is a complicated global optimum problem. Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the Control Parameteri-zation and Time Discretization (CPTD) method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.

  19. Speed control of optimal designed PMBLDC motor using improved fuzzy particle swarm optimization

    Directory of Open Access Journals (Sweden)

    Reza Saravani

    2014-09-01

    Full Text Available Permanent brushless dc motors have been used in many areas. Considering to their vast advantages, researchers have studied extensively for speed control and reducing the torque ripple of this motors. But a little study was done for both speed control and optimum design of them. This paper presents for the optimal design of a PMBLDC motor with goal of reducing volume and building cost. In addition the speed control aim is considered using a multi-objective nonlinear cost function which is solved by fuzzy particle swarm optimization. First characteristics of motor are expressed as functions of motor geometries. Then cost function which combines the step response characteristic of motor speed, building cost and its volume is constructed and minimized. To reach this goal in this application the new improved fuzzy particle swam optimization is used for the first time. The results of simulations show that this method has good ability and efficiency in reaching global best point in compare of GA and PSO methods.

  20. MPPT for Photovoltaic System Using Multi-objective Improved Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Xuecheng Zhao

    2013-07-01

    Full Text Available Making full use of abundant renewable solar energy through the development of photovoltaic (PV technology is an effective means to solve the problems such as difficulty in electricity supply and energy shortages in remote rural areas. In order to improve the electricity generating efficiency of PV cells, it is necessary to track the maximum power point of PV array, which is difficult to make under partially shaded conditions due to the odds of the appearance of two or more local maximum power points., In this paper, a control algorithm of maximum power point tracking (MPPT based on improved particle swarm optimization (IPSO algorithm is presented for PV systems. Firstly, the current in maximum power point is searched with the IPSO algorithm, and then the real maximum power point is tracked through controlling the output current of PV array.,. The MPPT method based on IPSO algorithm is established and simulated with Matlab / Simulink, and meanwhile, the comparison between IPSO MPPT algorithm and traditional MPPT algorithm is also performed in this paper. It is proved through simulation and experimental results that the IPSO algorithm has good performances and very fast response even to partial shaded PV modules, , which ensures the stability of PV system.  

  1. Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Lokesh Selvaraj

    2014-01-01

    Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

  2. Particle Swarm Optimization with Adaptive Mutation

    Institute of Scientific and Technical Information of China (English)

    LU Zhen-su; HOU Zhi-rong; DU Juan

    2006-01-01

    A new adaptive mutation particle swarm optimizer,which is based on the variance of the population's fitness,is presented in this paper.During the rtmning time,the mutation probability for the current best particle is determined by two factors:the variance of the population's fitness and the current optimal solution.The ability of particle swarm optimization (PSO) algorithm to break away from the local optimum is greatly improved by the mutation.The experimental results show that the new algorithm not only has great advantage of convergence property over genetic algorithm and PSO,but can also avoid the premature convergence problem effectively.

  3. Diesel Engine performance improvement in a 1-D engine model using Particle Swarm Optimization

    Science.gov (United States)

    Karra, Prashanth

    2015-12-01

    A particle swarm optimization (PSO) technique was implemented to improve the engine development and optimization process to simultaneously reduce emissions and improve the fuel efficiency. The optimization was performed on a 4-stroke 4-cylinder GT-Power based 1-D diesel engine model. To achieve the multi-objective optimization, a merit function was defined which included the parameters to be optimized: Nitrogen Oxides (NOx), Nonmethyl hydro carbons (NMHC), Carbon Monoxide (CO), Brake Specific Fuel Consumption (BSFC). EPA Tier 3 emissions standards for non-road diesel engines between 37 and 75 kW of output were chosen as targets for the optimization. The combustion parameters analyzed in this study include: Start of main Injection, Start of Pilot Injection, Pilot fuel quantity, Swirl, and Tumble. The PSO was found to be very effective in quickly arriving at a solution that met the target criteria as defined in the merit function. The optimization took around 40-50 runs to find the most favourable engine operating condition under the constraints specified in the optimization. In a favourable case with a high merit function values, the NOx+NMHC and CO values were reduced to as low as 2.9 and 0.014 g/kWh, respectively. The operating conditions at this point were: 10 ATDC Main SOI, -25 ATDC Pilot SOI, 0.25 mg of pilot fuel, 0.45 Swirl and 0.85 tumble. These results indicate that late main injections preceded by a close, small pilot injection are most favourable conditions at the operating condition tested.

  4. Multivariable PID Decoupling Control Method of Electroslag Remelting Process Based on Improved Particle Swarm Optimization (PSO Algorithm

    Directory of Open Access Journals (Sweden)

    Jie-Sheng Wang

    2014-02-01

    Full Text Available A mathematical model of electroslag remelting (ESR process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID controller tuned optimally by an improved particle swarm optimization (IPSO algorithm is proposed to control the two-input/two-output (TITO ESR process. An adaptive chaotic migration mutation operator is used to tackle the particles trapped in the clustering field in order to enhance the diversity of the particles in the population, prevent premature convergence and improve the search efficiency of PSO algorithm. The simulation results show the feasibility and effectiveness of the proposed control method. The new method can overcome dynamic working conditions and coupling features of the system in a wide range, and it has strong robustness and adaptability.

  5. Global optimization for ducted coaxial-rotors aircraft based on Kriging model and improved particle swarm optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    杨璐鸿; 刘顺安; 张冠宇; 王春雪

    2015-01-01

    To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization (PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.

  6. A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization

    Directory of Open Access Journals (Sweden)

    Yanhua Zhong

    2012-11-01

    Full Text Available Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision; analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.

  7. A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization

    Directory of Open Access Journals (Sweden)

    Yanhua Zhong

    2013-01-01

    Full Text Available Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision, analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.

  8. Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

    Science.gov (United States)

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

  9. A Novel Approach for Shearer Cutting Load Identification through Integration of Improved Particle Swarm Optimization and Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Zhongbin Wang

    2014-04-01

    Full Text Available In order to accurately identify the change of shearer cutting load, a novel approach was proposed through integration of improved particle swarm optimization and wavelet neural network. An improved updating strategy for inertia weight was presented to avoid falling into the local optimum. Moreover, immune mechanism was applied in the proposed approach to enhance the population diversity and improve the quality of solution, and the flowchart of the proposed approach was designed. Furthermore, a simulation example was carried out and comparison results indicated that the proposed approach was feasible, efficient, and outperforming others. Finally, an industrial application example of coal mining face was demonstrated to specify the effect of the proposed system.

  10. An Improved Particle Swarm Optimization for Selective Single Machine Scheduling with Sequence Dependent Setup Costs and Downstream Demands

    Directory of Open Access Journals (Sweden)

    Kun Li

    2015-01-01

    Full Text Available This paper investigates a special single machine scheduling problem derived from practical industries, namely, the selective single machine scheduling with sequence dependent setup costs and downstream demands. Different from traditional single machine scheduling, this problem further takes into account the selection of jobs and the demands of downstream lines. This problem is formulated as a mixed integer linear programming model and an improved particle swarm optimization (PSO is proposed to solve it. To enhance the exploitation ability of the PSO, an adaptive neighborhood search with different search depth is developed based on the decision characteristics of the problem. To improve the search diversity and make the proposed PSO algorithm capable of getting out of local optimum, an elite solution pool is introduced into the PSO. Computational results based on extensive test instances show that the proposed PSO can obtain optimal solutions for small size problems and outperform the CPLEX and some other powerful algorithms for large size problems.

  11. Performance Improvement of Spaceborne SAR Using Antenna Pattern Synthesis Based on Quantum-Behaved Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Young-Jin Won

    2017-01-01

    Full Text Available This study improves the performance of a spaceborne synthetic aperture radar (SAR system using an antenna mask design method and antenna pattern synthesis algorithms for an active phased array SAR system. The SAR antenna is an important component that affects the SAR system performance because it is closely related to the antenna pattern. This study proposes a method for antenna mask design that is based on several previous studies as well as the antenna pattern synthesis algorithm, which is based on quantum-behaved particle swarm optimization (QPSO for an active phased array SAR system. The performance of the designed antenna masks and synthesized patterns demonstrate that the proposed mask design method and antenna pattern synthesis algorithm based on QPSO can be used to improve the SAR system performance for spaceborne applications.

  12. Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

    Directory of Open Access Journals (Sweden)

    Zheping Yan

    2014-01-01

    Full Text Available A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present an extension competition particle swarm optimization (ECPSO. The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms, basic particle swarm optimization (BPSO, linear decreasing inertia weight particle swarm optimization (LWPSO, exponential inertia weight particle swarm optimization (EPSO, and time-varying acceleration coefficient (TVAC. The results demonstrate that CPSO and ECPSO manifest faster searching speed, accuracy, and stability. The searching performance for multimodulus function of ECPSO is superior to CPSO. At last, calibration of the underwater transponder coordinates is present using particle swarm algorithm, and novel improved particle swarm algorithm shows better performance than other algorithms.

  13. Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Lei Si

    2016-06-01

    Full Text Available In order to accurately evaluate the health condition of a shearer, a hybrid prediction method was proposed based on the integration of a fuzzy neural network (FNN and improved particle swarm optimization (IPSO. The parameters of FNN were optimized by the use of PSO, which was coupled with a premature judgment and mutation mechanism to increase the convergence speed and enhance the generalization ability. The key technologies are elaborated and the flowchart of the proposed approach was designed. Furthermore, an experiment example was carried out and the comparison results indicated that the proposed approach was feasible and outperforms others. Finally, a field application example in coal mining face was demonstrated to specify the effect of the proposed system.

  14. Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

    OpenAIRE

    Dong Yumin; Zhao Li

    2014-01-01

    Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the a...

  15. Multiswarm Particle Swarm Optimization with Transfer of the Best Particle

    Directory of Open Access Journals (Sweden)

    Xiao-peng Wei

    2015-01-01

    Full Text Available We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems.

  16. Modified constriction particle swarm optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    Zhe Zhang; Limin Jia; Yong Qin

    2015-01-01

    To deal with the demerits of constriction particle swarm optimization (CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random ve-locity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likeli-hood of being trapped into local optima. Final y the convergence of the algorithm is verified by calculation examples.

  17. Extending Particle Swarm Optimisers with Self-Organized Criticality

    DEFF Research Database (Denmark)

    Løvbjerg, Morten; Krink, Thiemo

    2002-01-01

    Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.......Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions....

  18. Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

    Science.gov (United States)

    Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

    2016-10-01

    The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

  19. An Improved Version of Discrete Particle Swarm Optimization for Flexible Job Shop Scheduling Problem with Fuzzy Processing Time

    Directory of Open Access Journals (Sweden)

    Song Huang

    2016-01-01

    Full Text Available The fuzzy processing time occasionally exists in job shop scheduling problem of flexible manufacturing system. To deal with fuzzy processing time, fuzzy flexible job shop model was established in several papers and has attracted numerous researchers’ attention recently. In our research, an improved version of discrete particle swarm optimization (IDPSO is designed to solve flexible job shop scheduling problem with fuzzy processing time (FJSPF. In IDPSO, heuristic initial methods based on triangular fuzzy number are developed, and a combination of six initial methods is applied to initialize machine assignment and random method is used to initialize operation sequence. Then, some simple and effective discrete operators are employed to update particle’s position and generate new particles. In order to guide the particles effectively, we extend global best position to a set with several global best positions. Finally, experiments are designed to investigate the impact of four parameters in IDPSO by Taguchi method, and IDPSO is tested on five instances and compared with some state-of-the-art algorithms. The experimental results show that the proposed algorithm can obtain better solutions for FJSPF and is more competitive than the compared algorithms.

  20. Particle Swarm Transport in Fracture Networks

    Science.gov (United States)

    Pyrak-Nolte, L. J.; Mackin, T.; Boomsma, E.

    2012-12-01

    Colloidal particles of many types occur in fractures in the subsurface as a result of both natural and industrial processes (e.g., environmental influences, synthetic nano- & micro-particles from consumer products, chemical and mechanical erosion of geologic material, proppants used in gas and oil extraction, etc.). The degree of localization and speed of transport of such particles depends on the transport mechanisms, the chemical and physical properties of the particles and the surrounding rock, and the flow path geometry through the fracture. In this study, we investigated the transport of particle swarms through artificial fracture networks. A synthetic fracture network was created using an Objet Eden 350V 3D printer to build a network of fractures. Each fracture in the network had a rectangular cross-sectional area with a constant depth of 7 mm but with widths that ranged from 2 mm to 11 mm. The overall dimensions of the network were 132 mm by 166 mm. The fracture network had 7 ports that were used either as the inlet or outlet for fluid flow through the sample or for introducing a particle swarm. Water flow rates through the fracture were controlled with a syringe pump, and ranged from zero flow to 6 ml/min. Swarms were composed of a dilute suspension (2% by mass) of 3 μm fluorescent polystyrene beads in water. Swarms with volumes of 5, 10, 20, 30 and 60 μl were used and delivered into the network using a second syringe pump. The swarm behavior was imaged using an optical fluorescent imaging system illuminated by green (525 nm) LED arrays and captured by a CCD camera. For fracture networks with quiescent fluids, particle swarms fell under gravity and remained localized within the network. Large swarms (30-60 μl) were observed to bifurcate at shallower depths resulting in a broader dispersal of the particles than for smaller swarm volumes. For all swarm volumes studied, particle swarms tended to bifurcate at the intersection between fractures. These

  1. Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

    Science.gov (United States)

    Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

    2017-03-01

    Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

  2. Nonlinear Steady-State Model Based Gas Turbine Health Status Estimation Approach with Improved Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Yulong Ying

    2015-01-01

    Full Text Available In the lifespan of a gas turbine engine, abrupt faults and performance degradation of its gas-path components may happen; however the performance degradation is not easily foreseeable when the level of degradation is small. Gas path analysis (GPA method has been widely applied to monitor gas turbine engine health status as it can easily obtain the magnitudes of the detected component faults. However, when the number of components within engine is large or/and the measurement noise level is high, the smearing effect may be strong and the degraded components may not be recognized. In order to improve diagnostic effect, a nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm (PSO-GPA has been proposed in this study. The proposed approach has been tested in ten test cases where the degradation of a model three-shaft marine engine has been analyzed. These case studies have shown that the approach can accurately search and isolate the degraded components and further quantify the degradation for major gas-path components. Compared with the typical GPA method, the approach has shown better measurement noise immunity and diagnostic accuracy.

  3. Locating multiple optima using particle swarm optimization

    CSIR Research Space (South Africa)

    Brits, R

    2007-01-01

    Full Text Available Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems. This paper presents a new particle swarm optimization (PSO) technique to locate and refine multiple...

  4. 基于改进PSO算法的岸基导弹火力分配研究%The Fire Distribution for Shore-Based Missiles Based on Improved Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    王光源; 汲万峰; 孙钧正; 章尧卿

    2012-01-01

    针对基本粒子群算法(Particle Swarm Optimization,PSO)易局部收敛的缺陷,设计一种根据种群多样性测度动态调整惯性权重的改进粒子群算法,通过仿真测试函数与基本粒子群算法、自适应粒子群算法(Adaptive Particle Swarm Optimization,APSO)、带收缩因子的粒子群算法(Contractive Particle Swarm Optimization,CPSO)进行比较,结果表明本文改进的PSO算法在提高算法的综合搜索能力方面具有优越性.将改进的PSO算法运用到岸基导弹对海上舰艇攻击火力分配中,构建了火力分配模型,并进行了仿真实验,仿真结果验证了模型及算法的有效性.%Aiming at the basic particle swarm optimization limitation of easy partial constriction, an improved panicle swarm optimization algorithm on the foundation of dynamic modulation of population diversity measure inertia weight is developed. The emulation and comparison with basic particle swarm optimization , adaptive particle swarm optimization, and contractive particle swarm optimization is proven that the improved particle swarm optimization algorithm has advantage in enhancing integration hunting ability. The improved particle swarm optimization algorithm is used in fire distribution for shore-based missiles, and the model of fire distribution is presented. The effectiveness of the algorithm and model is validated by imitation.

  5. A Novel Particle Swarm Optimization Algorithm for Global Optimization.

    Science.gov (United States)

    Wang, Chun-Feng; Liu, Kui

    2016-01-01

    Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

  6. Research of improved particle swarm algorithm for apparel intelligent marker making%服装智能排料粒子群算法的改进研究

    Institute of Scientific and Technical Information of China (English)

    黄灿艺

    2011-01-01

    Aimming at the problem of apparel intelligent marker making, an improved particle swarm algorithm was put forward based on analyzing the merit and demerit of genetic algorithm, inertia parameter w was added to the speed item of particle swarm algorithm. Furthermore, a demonstration test is done by C+ + under the environment of VS 2005. The result indicated that the improved particle swarm algorithm can achieve ideal material utilization, and running time is close to the best.%针对服装智能排料问题提出一种改进的粒子群算法,对粒子群算法的速度分项加入一个参数惯性权值w,并将改进结果在VS 2005平台下用C++进行试验论证.结果表明:使用改进的粒子群算法,服装面料的利用率较高,且运行时间接近最优.

  7. Particle Swarm Optimization for Outdoor Lighting Design

    Directory of Open Access Journals (Sweden)

    Ana Castillo-Martinez

    2017-01-01

    Full Text Available Outdoor lighting is an essential service for modern life. However, the high influence of this type of facility on energy consumption makes it necessary to take extra care in the design phase. Therefore, this manuscript describes an algorithm to help light designers to get, in an easy way, the best configuration parameters and to improve energy efficiency, while ensuring a minimum level of overall uniformity. To make this possible, we used a particle swarm optimization (PSO algorithm. These algorithms are well established, and are simple and effective to solve optimization problems. To take into account the most influential parameters on lighting and energy efficiency, 500 simulations were performed using DIALux software (4.10.0.2, DIAL, Ludenscheid, Germany. Next, the relation between these parameters was studied using to data mining software. Subsequently, we conducted two experiments for setting parameters that enabled the best configuration algorithm in order to improve efficiency in the proposed process optimization.

  8. Apical-dominant particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Zhihua Cui; Xingjuan Cai; Jianchao Zeng; Guoji Sun

    2008-01-01

    Particle swarm optimization (PSO) is a new stochastic population-based search methodology by simulating the animal social behaviors such as birds flocking and fish schooling.Many improvements have been proposed within the framework of this biological assumption.However,in this paper,the search pattern of PSO is used to model the branch growth process of natural plants.It provides a different poten-tial manner from artificial plant.To illustrate the effectiveness of this new model,apical dominance phenomenon is introduced to construct a novel variant by emphasizing the influence of the phototaxis.In this improvement,the population is divided into three different kinds of buds associated with their performances.Furthermore,a mutation strategy is applied to enhance the ability escaping from a local optimum.Sim-ulation results demonstrate good performance of the new method when solving high-dimensional multi-modal problems.

  9. Selectively-informed particle swarm optimization.

    Science.gov (United States)

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-03-19

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.

  10. Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

    Science.gov (United States)

    Rahmalia, Dinita

    2017-08-01

    Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

  11. A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems

    Institute of Scientific and Technical Information of China (English)

    Yong WANG; Zixing CAI

    2009-01-01

    In the real-world applications, most optimization problems are subject to different types of constraints. These problems are known as constrained optimization problems (COPs). Solving COPs is a very important area in the optimization field. In this paper, a hybrid multi-swarm particle swarm optimization (HMPSO) is proposed to deal with COPs. This method adopts a parallel search operator in which the current swarm is partitioned into several subswarms and particle swarm optimization (PSO) is severed as the search engine for each sub-swarm. Moreover, in order to explore more promising regions of the search space, differential evolution (DE) is incorporated to improve the personal best of each particle. First, the method is tested on 13 benchmark test functions and compared with three stateof-the-art approaches. The simulation results indicate that the proposed HMPSO is highly competitive in solving the 13 benchmark test functions. Afterward, the effectiveness of some mechanisms proposed in this paper and the effect of the parameter setting were validated by various experiments. Finally, HMPSO is further applied to solve 24 benchmark test functions collected in the 2006 IEEE Congress on Evolutionary Computation (CEC2006) and the experimental results indicate that HMPSO is able to deal with 22 test functions.

  12. A Novel Distributed Quantum-Behaved Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Yangyang Li

    2017-01-01

    Full Text Available Quantum-behaved particle swarm optimization (QPSO is an improved version of particle swarm optimization (PSO and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases.

  13. Research of stochastic weight strategy for extended particle swarm optimizer

    Institute of Scientific and Technical Information of China (English)

    XU Jun-jie; YUE Xin; XIN Zhan-hong

    2008-01-01

    To improve the performance of extended particle swarm optimizer, a novel means of stochastic weight deployment is proposed for the iterative equation of velocity updation. In this scheme, one of the weights is specified to a random number within the range of [0, 1] and the other two remain constant configurations. The simulations show that this weight strategy outperforms the previous deterministic approach with respect to success rate and convergence speed. The experi- ments also reveal that if the weight for global best neighbor is specified to a stochastic number, extended particle swarm optimizer achieves high and robust performance on the given multi-modal function.

  14. Particle Swarm Optimizaton A Physics-Based Approach

    CERN Document Server

    Mikki, Said M

    2008-01-01

    This work aims to provide new introduction to the particle swarm optimization methods using a formal analogy with physical systems. By postulating that the swarm motion behaves similar to both classical and quantum particles, we establish a direct connection between what are usually assumed to be separate fields of study, optimization and physics. Within this framework, it becomes quite natural to derive the recently introduced quantum PSO algorithm from the Hamiltonian or the Lagrangian of the dynamical system. The physical theory of the PSO is used to suggest some improvements in the algorit

  15. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chuncai Xiao

    2014-12-01

    Full Text Available This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM and improved particle swarm optimization (IPSO algorithm (SVM-IPSO. In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN, the basic particle swarm optimization (PSO method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

  16. Novelty-driven Particle Swarm Optimization

    DEFF Research Database (Denmark)

    Galvao, Diana; Lehman, Joel Anthony; Urbano, Paulo

    2015-01-01

    Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higher-fitness areas. However...

  17. Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity

    Directory of Open Access Journals (Sweden)

    P.K. Das

    2016-03-01

    Full Text Available Classical Q-learning takes huge computation to calculate the Q-value for all possible actions in a particular state and takes large space to store its Q-value for all actions, as a result of which its convergence rate is slow. This paper proposed a new methodology to determine the optimize trajectory of the path for multi-robots in clutter environment using hybridization of improving classical Q-learning based on four fundamental principles with improved particle swarm optimization (IPSO by modifying parameters and differentially perturbed velocity (DV algorithm for improving the convergence. The algorithms are used to minimize path length and arrival time of all the robots to their respective destination in the environment and reducing the turning angle of each robot to reduce the energy consumption of each robot. In this proposed scheme, the improve classical Q-learning stores the Q-value of the best action of the state and thus save the storage space, which is used to decide the Pbest and gbest of the improved PSO in each iteration, and the velocity of the IPSO is adjusted by the vector differential operator inherited from differential evolution (DE. The validation of the algorithm is studied in simulated and Khepera-II robot.

  18. Optimal power flow by particle swarm optimization with an aging ...

    African Journals Online (AJOL)

    DR OKE

    International Journal of Engineering, Science and Technology, Vol. 7, No. ... improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. ..... QC-10 (p.u.). 0.00 ..... College, Asansol, in 2006; MBA in Power Management from University of Petroleum & Energy Studies, ...

  19. CriPS: Critical Dynamics in Particle Swarm Optimization

    OpenAIRE

    Erskine, Adam; Herrmann, J Michael

    2014-01-01

    Particle Swarm Optimisation (PSO) makes use of a dynamical system for solving a search task. Instead of adding search biases in order to improve performance in certain problems, we aim to remove algorithm-induced scales by controlling the swarm with a mechanism that is scale-free except possibly for a suppression of scales beyond the system size. In this way a very promising performance is achieved due to the balance of large-scale exploration and local search. The resulting algorithm shows e...

  20. The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters

    Directory of Open Access Journals (Sweden)

    Yancai Xiao

    2016-05-01

    Full Text Available In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD wind turbines, this paper proposes a fuzzy proportional integral (PI controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.

  1. Fuzzy entropy image segmentation based on particle Swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Linyi Li; Deren Li

    2008-01-01

    Partide swaFnl optimization is a stochastic global optimization algorithm that is based on swarm intelligence.Because of its excellent performance,particle swarm optimization is introduced into fuzzy entropy image segmentation to select the optimal fuzzy parameter combination and fuzzy threshold adaptively.In this study,the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the segmentation application.Then fuzzy entropy image segmentation based on particle swarm opti-mization is implemented and the proposed method obtains satisfactory results in the segmentation experiments.Compared with the exhaustive search method,particle swarm optimization can give the salne optimal fuzzy parameter combination and fuzzy threshold while needing less search time in the segmentation experiments and also has good search stability in the repeated experiments.Therefore,fuzzy entropy image segmentation based on particle swarm optimization is an efficient and promising segmentation method.

  2. An Improved Particle Swarm Optimization Based on Deluge Approach for Enhanced Hierarchical Cache Optimization in IPTV Networks

    Directory of Open Access Journals (Sweden)

    M. Somu

    2014-05-01

    Full Text Available In recent years, IP network has been considered as a new delivery network for TV services. A majority of the telecommunication industries have used IP network to offer on-demand services and linear TV services as it can offer a two-way and high-speed communication. In order to effectively and economically utilize the IP network, caching is the technique which is usually preferred. In IPTV system, a managed network is utilized to bring out TV services, the requests of Video on Demand (VOD objects are usually combined in a limited period intensively and user preferences are fluctuated dynamically. Furthermore, the VOD content updates often under the control of IPTV providers. In order to minimize this traffic and overall network cost, a segment of the video content is stored in caches closer to subscribers, for example, Digital Subscriber Line Access Multiplexer (DSLAM, a Central Office (CO and Intermediate Office (IO. The major problem focused in this approach is to determine the optimal cache memory that should be assigned in order to attain maximum cost effectiveness. This approach uses an effective Grate Deluge algorithm based Particle Swarm Optimization (GDPSO approach for attaining the optimal cache memory size which in turn minimizes the overall network cost. The analysis shows that hierarchical distributed caching can save significant network cost through the utilization of the GDPSO algorithm.

  3. A Novel Adaptive Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Xiaobing Yu

    2012-07-01

    Full Text Available Particle swarm optimization (PSO is a stochastic search technique for solving optimization problems, which has been proven to be efficient and effective in wide applications. However, the PSO can easily fly into the local optima and lack the ability to jump out of the local optima. A novel adaptive PSO is proposed by evaluating convergence of the fitness value. The novel mechanism is to ensure the diversity of particles. Simulations for benchmark test functions have illustrated that the proposed algorithm possesses better ability to find the global optima than other variants and is an effective global optimization tool.

  4. Forecasting hysteresis behaviours of magnetorheological elastomer base isolator utilizing a hybrid model based on support vector regression and improved particle swarm optimization

    Science.gov (United States)

    Yu, Yang; Li, Yancheng; Li, Jianchun

    2015-03-01

    Due to its inherent hysteretic characteristics, the main challenge for the application of a magnetorheological elastomer- (MRE) based isolator is the exploitation of the accurate model, which could fully describe its unique behaviour. This paper proposes a nonparametric model for a MRE-based isolator based on support vector regression (SVR). The trained identification model is to forecast the shear force of the MRE-based isolator online; thus, the dynamic response from the MRE-based isolator can be well captured. In order to improve the forecast capacity of the model, a type of improved particle swarm optimization (IPSO) is employed to optimize the parameters in SVR. Eventually, the trained model is applied to the MRE-based isolator modelling with testing data. The results indicate that the proposed hybrid model has a better generalization capacity and better recognition accuracy than other conventional models, and it is an effective and suitable approach for forecasting the behaviours of a MRE-based isolator.

  5. Earth Observing Satellite Orbit Design Via Particle Swarm Optimization

    Science.gov (United States)

    2014-08-01

    Earth Observing Satellite Orbit Design Via Particle Swarm Optimization Sharon Vtipil ∗ and John G. Warner ∗ US Naval Research Laboratory, Washington...number of passes per day given a satellite’s orbital altitude and inclination. These are used along with particle swarm optimization to determine optimal...well suited to use within a meta-heuristic optimization method such as the Particle Swarm Optimizer (PSO). This method seeks to find the optimal set

  6. Study of particle swarm optimization particle trajectories

    CSIR Research Space (South Africa)

    Van den Bergh, F

    2006-01-01

    Full Text Available provides a formal proof that each particle converges to a stable point. An empirical analysis of multidimensional stochastic particles is also presented. Experimental results are provided to support the conclusions drawn from the theoretical findings...

  7. Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region.

    Directory of Open Access Journals (Sweden)

    Qidong Yang

    Full Text Available Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO algorithm and the land-surface process model SHAW (Simultaneous Heat and Water, the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.

  8. Comparison of Harmony Search Algorithm and Particle Swarm Optimization for Distributed Generation Allocation to Improve Steady State Voltage Stability of Distribution Networks

    Directory of Open Access Journals (Sweden)

    Hamed Piarehzadeh

    2012-08-01

    Full Text Available In this study is tried to optimal distributed generation allocation for stability improvement in radial distribution systems. Voltage instability implies an uncontrolled decrease in voltage triggered by a disturbance, leading to voltage collapse and is primarily caused by dynamics connected with the load. The instability is divided into steady state and transient voltage instability Based on the time spectrum of the incident of the phenomena. The analysis is accomplished using a steady state voltage stability index which can be evaluated at each node of the distribution system. Several optimal capacities and locations are used to check these results. The location of DG has the main effect voltage stability on the system. Effects of location and capacity on incrementing steady state voltage stability in radial distribution systems are examined through Harmony Search Algorithm (HSA and finally the results are compared to Particle Swarm Optimization (PSO on the terms of speed, convergence and accuracy.

  9. Research on particle swarm optimization algorithm based on optimal movement probability

    Science.gov (United States)

    Ma, Jianhong; Zhang, Han; He, Baofeng

    2017-01-01

    The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

  10. Improved K-means Clustering Algorithm Based on Particle Swarm Optimization%基于改进粒子群的K-means聚类算法

    Institute of Scientific and Technical Information of China (English)

    吴涤单

    2014-01-01

    针对传统的k-means算法处理离散型数据的不足以及选取初始聚类中心的随机性等缺点,提出了一种基于改进的粒子群优化k-means算法,根据文中提供的优化算法寻找初始聚类中心后,在阀值范围内进行数据样本间的迭代更新,直至聚类中心稳定。经过实验结果验证分析表明,经过改进的粒子群优化k-means算法与传统的k-means算法相比,更具有良好的聚类收敛效果,聚类效果也相对稳定。%Random defects in the traditional K-means algorithm to process the discrete data insufficiency as well as the selection of initial clustering center, put forward an improved particle swarm optimization based on K-means algorithm, to find the initial cluster center according to the optimization algorithm is provided in this paper, are updated iteratively between data samples in the threshold range, until the clustering center. Through the experimental results verify the analysis shows that, by K-means algo-rithm, the improved particle swarm optimization k-means algorithm compared with the traditional clustering, has a good conver-gence effect, cluster effect is also relatively stable.

  11. Usage of the particle swarm optimization in problems of mechanics

    Directory of Open Access Journals (Sweden)

    Hajžman M.

    2016-06-01

    Full Text Available This paper deals with the optimization method called particle swarm optimization and its usage in mechanics. Basic versions of the method is introduced and several improvements and modifications are applied for better convergence of the algorithms. The performance of the optimization algorithm implemented in an original in-house software is investigated by means of three basic and one complex problems of mechanics. The goal of the first problem is to find optimal parameters of a dynamic absorber of vibrations. The second problem is about the tunning of eigenfrequencies of beam bending vibrations. The third problem is to optimize parameters of a clamped beam with various segments. The last complex problem is the optimization of a tilting mechanism with multilevel control. The presented results show that the particle swarm optimization can be efficiently used in mechanical tasks.

  12. Swarms of particles settling under gravity in a viscous fluid

    CERN Document Server

    Ekiel-Jezewska, Maria L

    2012-01-01

    We investigate swarms made of a small number of particles settling under gravity in a viscous fluid. The particles do not touch each other and can move relative to each other. The dynamics is analyzed in the point-particle approximation. A family of swarms is found with periodic oscillations of all the settling particles. In the presence of an additional particle above the swarm, the trajectories are horizontally repelled from the symmetry axis, and flattened vertically. The results are used to explain how a spherical cloud, made of a large number of particles distributed at random, evolves and destabilizes.

  13. Orientational hysteresis in swarms of active particles in external field

    CERN Document Server

    Romensky, Maksym

    2015-01-01

    Structure and ordering in swarms of active particles have much in common with condensed matter systems like magnets or liquid crystals. A number of important characteristics of such materials can be obtained via dynamic tests such as hysteresis. In this work, we show that dynamic hysteresis can be observed also in swarms of active particles and possesses similar properties to the counterparts in magnetic materials. To study the swarm dynamics, we use computer simulation of the active Brownian particle model with dissipative interactions. The swarm is confined to a narrow linear channel and one-dimensional polar order parameter is measured. In an oscillating external field, the order parameter demonstrates dynamic hysteresis with the shape of the loop and its area varying with the amplitude and frequency of the applied field, swarm density and the noise intensity. We measure the scaling exponents for the hysteresis loop area, which can be associated with the controllability of the swarm. Although the exponents...

  14. Particle Swarm Optimisation with Spatial Particle Extension

    DEFF Research Database (Denmark)

    Krink, Thiemo; Vesterstrøm, Jakob Svaneborg; Riget, Jacques

    2002-01-01

    In this paper, we introduce spatial extension to particles in the PSO model in order to overcome premature convergence in iterative optimisation. The standard PSO and the new model (SEPSO) are compared w.r.t. performance on well-studied benchmark problems. We show that the SEPSO indeed managed...

  15. Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem

    Directory of Open Access Journals (Sweden)

    Mansour Eddaly

    2016-10-01

    Full Text Available This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

  16. A New Class of Hybrid Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Da-Qing Guo; Yong-Jin Zhao; Hui Xiong; Xiao Li

    2007-01-01

    A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique (LDIW) and the mutative scale chaos optimization algorithm (MSCOA) are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.

  17. Hybrid particle swarm cooperative optimization algorithm and its application to MBC in alumina production

    Institute of Scientific and Technical Information of China (English)

    Shengli Song; Li Kong; Yong Gan; Rijian Su

    2008-01-01

    An effective hybrid particle swarm cooperative optimization (HPSCO) algorithm combining simulated annealing method and simplex method is proposed. The main idea is to divide particle swarm into several sub-groups and achieve optimization through cooperativeness of different sub-groups among the groups. The proposed algorithm is tested by benchmark functions and applied to material balance computation (MBC) in alumina production. Results show that HPSCO, with both a better stability and a steady convergence, has faster convergence speed and higher global convergence ability than the single method and the improved particle swarm optimization method. Most importantly, results demonstrate that HPSCO is more feasible and efficient than other algorithms in MBC.

  18. Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Xiaobiao; Safranek, James

    2014-09-01

    Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

  19. Binary Particle Swarm Optimization based Biclustering of Web usage Data

    CERN Document Server

    Bagyamani, R Rathipriya K Thangavel J

    2011-01-01

    Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketin...

  20. Research on Multiple Particle Swarm Algorithm Based on Analysis of Scientific Materials

    Directory of Open Access Journals (Sweden)

    Zhao Hongwei

    2017-01-01

    Full Text Available This paper proposed an improved particle swarm optimization algorithm based on analysis of scientific materials. The core thesis of MPSO (Multiple Particle Swarm Algorithm is to improve the single population PSO to interactive multi-swarms, which is used to settle the problem of being trapped into local minima during later iterations because it is lack of diversity. The simulation results show that the convergence rate is fast and the search performance is good, and it has achieved very good results.

  1. Performance Evaluation of OLSR Using Swarm Intelligence and Hybrid Particle Swarm Optimization Using Gravitational Search Algorithm

    Directory of Open Access Journals (Sweden)

    S. Meenakshi Sundaram

    2014-04-01

    Full Text Available The aim of this research is to evaluate the performance of OLSR using swarm intelligence and HPSO with Gravitational search algorithm to lower the jitter time, data drop and end to end delay and improve the network throughput. Simulation was carried out for multimedia traffic and video streamed network traffic using OPNET Simulator. Routing is exchanging of information from one host to another in a network. Routing forwards packets to destination using an efficient path. Path efficiency is measured through metrics like hop number, traffic and security. Each host node acts as a specialized router in Ad-hoc networks. A table driven proactive routing protocol Optimized Link State Protocol (OLSR has available topology information and routes. OLSR’s efficiency depends on Multipoint relay selection. Various studies were conducted to decrease control traffic overheads through modification of existing OLSR routing protocol and traffic shaping based on packet priority. This study proposes a modification of OLSR using swarm intelligence, Hybrid Particle Swarm Optimization (HPSO using Gravitational Search Algorithm (GSA and evaluation of performance of jitter, end to end delay, data drop and throughput. Simulation was carried out to investigate the proposed method for the network’s multimedia traffic.

  2. Antenna optimization using Particle Swarm Optimization algorithm

    Directory of Open Access Journals (Sweden)

    Golubović Ružica M.

    2006-01-01

    Full Text Available We present the results for two different antenna optimization problems that are found using the Particle Swarm Optimization (PSO algorithm. The first problem is finding the maximal forward gain of a Yagi antenna. The second problem is finding the optimal feeding of a broadside antenna array. The optimization problems have 6 and 20 optimization variables, respectively. The preferred values of the parameters of the PSO algorithm are found for presented problems. The results show that the preferred parameters of PSO are somewhat different for optimization problems with different number of dimensions of the optimization space. The results that are found using the PSO algorithm are compared with the results that are found using other optimization algorithms, in order to estimate the efficiency of the PSO.

  3. Semisupervised Particle Swarm Optimization for Classification

    Directory of Open Access Journals (Sweden)

    Xiangrong Zhang

    2014-01-01

    Full Text Available A semisupervised classification method based on particle swarm optimization (PSO is proposed. The semisupervised PSO simultaneously uses limited labeled samples and large amounts of unlabeled samples to find a collection of prototypes (or centroids that are considered to precisely represent the patterns of the whole data, and then, in principle of the “nearest neighborhood,” the unlabeled data can be classified with the obtained prototypes. In order to validate the performance of the proposed method, we compare the classification accuracy of PSO classifier, k-nearest neighbor algorithm, and support vector machine on six UCI datasets, four typical artificial datasets, and the USPS handwritten dataset. Experimental results demonstrate that the proposed method has good performance even with very limited labeled samples due to the usage of both discriminant information provided by labeled samples and the structure information provided by unlabeled samples.

  4. Cosmological parameter estimation using Particle Swarm Optimization

    Science.gov (United States)

    Prasad, J.; Souradeep, T.

    2014-03-01

    Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

  5. Acceleration Factor Harmonious Particle Swarm Optimizer

    Institute of Scientific and Technical Information of China (English)

    Jie Chen; Feng Pan; Tao Cai

    2006-01-01

    A Particle Swarm Optimizer (PSO) exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight ω is enhanced to (-1, 1).Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO (AFHPSO) is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.

  6. Particle Swarm Optimization Based Reactive Power Optimization

    CERN Document Server

    Sujin, P R; Linda, M Mary

    2010-01-01

    Reactive power plays an important role in supporting the real power transfer by maintaining voltage stability and system reliability. It is a critical element for a transmission operator to ensure the reliability of an electric system while minimizing the cost associated with it. The traditional objectives of reactive power dispatch are focused on the technical side of reactive support such as minimization of transmission losses. Reactive power cost compensation to a generator is based on the incurred cost of its reactive power contribution less the cost of its obligation to support the active power delivery. In this paper an efficient Particle Swarm Optimization (PSO) based reactive power optimization approach is presented. The optimal reactive power dispatch problem is a nonlinear optimization problem with several constraints. The objective of the proposed PSO is to minimize the total support cost from generators and reactive compensators. It is achieved by maintaining the whole system power loss as minimum...

  7. 基于改进粒子群算法的翼型稳健型气动优化设计%Robust Airfoil Optimization Based on Improved Particle Swarm Optimization Method

    Institute of Scientific and Technical Information of China (English)

    王元元; 张彬乾; 陈迎春

    2011-01-01

    A robust airfoil optimization platform was constructed based on modified particle swarm optimization method( I. E. Second-order oscillating particle swarm method ) .which consists of an efficient optimization algorithm, a precise aerodynamic analysis program, a highac-curacy surrogate model and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared to standard particle swarm method. Firstly, particle velocity was represented by the combination of particle position and variation of position, which makes the particle swarm algorithm become a second-order precision method with respect to particle position. Secondly, for the sake of adding diversity to the swarm and enlarging parameter searching domain to improve the global convergence performance of the algorithm, an oscillating term was introduced to the update formula of particle velocity. At last, taking two airfoils as examples, the aerodynamic shapes were optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils was greatly improved at a broad design range.%基于标准粒子群算法,将位移变化作为影响微粒速度的变量,使得粒子群算法关于粒子位置为二阶精度函数,加快了收敛速度;进一步地在粒子速度更新公式中引入振荡环节,提高了群体多样性,改善了算法的全局收敛性.以改进粒子群算法为基础,结合气动分析程序、代理模型以及翼型参数化方法,构建了翼型稳健型气动优化设计系统.针对某型客机的基本翼型以及翼梢小翼翼型气动优化设计结果表明,优化后的翼型气动特性相对于初始翼型在较宽的设计范围内都有了大幅度提高.

  8. Combined Data with Particle Swarm Optimization for Structural Damage Detection

    Directory of Open Access Journals (Sweden)

    Fei Kang

    2013-01-01

    Full Text Available This paper proposes a damage detection method based on combined data of static and modal tests using particle swarm optimization (PSO. To improve the performance of PSO, some immune properties such as selection, receptor editing, and vaccination are introduced into the basic PSO and an improved PSO algorithm is formed. Simulations on three benchmark functions show that the new algorithm performs better than PSO. The efficiency of the proposed damage detection method is tested on a clamped beam, and the results demonstrate that it is more efficient than PSO, differential evolution, and an adaptive real-parameter simulated annealing genetic algorithm.

  9. Chaotic Particle Swarm Optimization with Mutation for Classification

    Science.gov (United States)

    Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

    2015-01-01

    In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

  10. Chaotic particle swarm optimization with mutation for classification.

    Science.gov (United States)

    Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

    2015-01-01

    In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

  11. Multilayered feed forward neural network based on particle swarm optimizer algorithm

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    BP is a commonly used neural network training method, which has some disadvantages, such as local minima,sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer (PSO), guaranteed convergence particle swarm optimizer (GCPSO), an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.

  12. ADAPTIVE LIFTING BASED IMAGE COMPRESSION SCHEME WITH PARTICLE SWARM OPTIMIZATION TECHNIQUE

    Directory of Open Access Journals (Sweden)

    Nishat kanvel

    2010-09-01

    Full Text Available This paper presents an adaptive lifting scheme with Particle Swarm Optimization technique for image compression. Particle swarm Optimization technique is used to improve the accuracy of the predictionfunction used in the lifting scheme. This scheme is applied in Image compression and parameters such as PSNR, Compression Ratio and the visual quality of the image is calculated .The proposed scheme iscompared with the existing methods.

  13. Particle swarm optimization for programming deep brain stimulation arrays

    Science.gov (United States)

    Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

    2017-02-01

    Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (disabling electrodes (n  =  3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of  <1% between approaches. Significance. The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

  14. Drilling Path Optimization Based on Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHU Guangyu; ZHANG Weibo; DU Yuexiang

    2006-01-01

    This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.

  15. Optimal choice of parameters for particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    ZHANG Li-ping; YU Huan-jun; HU Shang-xu

    2005-01-01

    The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the performance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and improper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.

  16. A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm

    Directory of Open Access Journals (Sweden)

    Nor Azlina Ab Aziz

    2014-01-01

    Full Text Available In the original particle swarm optimisation (PSO algorithm, the particles’ velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO. The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO algorithm divides the particles into smaller groups. The best member of a group and the swarm’s best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well.

  17. Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation.

    Science.gov (United States)

    Mekhmoukh, Abdenour; Mokrani, Karim

    2015-11-01

    In this paper, a new image segmentation method based on Particle Swarm Optimization (PSO) and outlier rejection combined with level set is proposed. A traditional approach to the segmentation of Magnetic Resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image. The algorithm is very sensitive to noise and in-homogeneities in the image, moreover, it depends on cluster centers initialization. To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm, a novel extended FCM algorithm for image segmentation is presented. In general, in the FCM algorithm the initial cluster centers are chosen randomly, with the help of PSO algorithm the clusters centers are chosen optimally. Our algorithm takes also into consideration the spatial neighborhood information. These a priori are used in the cost function to be optimized. For MR images, the resulting fuzzy clustering is used to set the initial level set contour. The results confirm the effectiveness of the proposed algorithm.

  18. Particle swarm optimization based optimal bidding strategy in an ...

    African Journals Online (AJOL)

    user

    Particle swarm optimization based optimal bidding strategy in an open ... relaxation-based approach for strategic bidding in England-Wales pool type electricity market has ... presents the mathematical formulation of optimal bidding problem.

  19. APPLICATION OF A PARTICLE SWARM OPTIMIZATION IN AN ...

    African Journals Online (AJOL)

    Key words: Load flow, Optimal Power Flow, Power System, Particle Swarm ... the PSO method has been employed to solve economic dispatch problem. .... Once the reconstruction operators have been applied and the control variables values.

  20. Software Project Scheduling Management by Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Dinesh B. Hanchate

    2014-12-01

    Full Text Available PSO (Particle Swarm Optimization is, like GA, a heuristic global optimization method based on swarm intelligence. In this paper, we present a particle swarm optimization algorithm to solve software project scheduling problem. PSO itself inherits very efficient local search method to find the near optimal and best-known solutions for all instances given as inputs required for SPSM (Software Project Scheduling Management. At last, this paper imparts PSO and research situation with SPSM. The effect of PSO parameter on project cost and time is studied and some better results in terms of minimum SCE (Software Cost Estimation and time as compared to GA and ACO are obtained.

  1. Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

    Science.gov (United States)

    Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

    2017-01-01

    Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

  2. Manipulator inverse kinematics control based on particle swarm optimization neural network

    Science.gov (United States)

    Wen, Xiulan; Sheng, Danghong; Guo, Jing

    2008-10-01

    The inverse kinematics control of a robotic manipulator requires solving non-linear equations having transcendental functions and involving time-consuming calculations. Particle Swarm Optimization (PSO), which is based on the behaviour of insect swarms and exploits the solution space by taking into account the experience of the single particle as well as that of the entire swarm, is similar to the genetic algorithm (GA) in that it performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable solution. In this paper, PSO is firstly proposed to optimize feed-forward neural network for manipulator inverse kinematics. Compared with the results of the fast back propagation learning algorithm (FBP), conventional GA genetic algorithm based elitist reservation (EGA), improved GA (IGA) and immune evolutionary computation (IEC), the simulation results verify the particle swarm optimization neural network (PSONN) is effective for manipulator inverse kinematics control.

  3. Searching for Planets using Particle Swarm Optimization

    Science.gov (United States)

    Chambers, John E.

    2008-05-01

    The Doppler radial velocity technique has been highly successful in discovering planetary-mass companions in orbit around nearby stars. A typical data set contains around one hundred instantaneous velocities for the star, spread over a period of several years,with each observation measuring only the radial component of velocity. From this data set, one would like to determine the masses and orbital parameters of the system of planets responsible for the star's reflex motion. Assuming coplanar orbits, each planet is characterized by five parameters, with an additional parameter for each telescope used to make observations, representing the instrument's velocity offset. The large number of free parameters and the relatively sparse data sets make the fitting process challenging when multiple planets are present, especially if some of these objects have low masses. Conventional approaches using periodograms often perform poorly when the orbital periods are not separated by large amounts or the longest period is comparable to the length of the data set. Here, I will describe a new approach to fitting Doppler radial velocity sets using particle swarm optimization (PSO). I will describe how the PSO method works, and show examples of PSO fits to existing radial velocity data sets, with comparisons to published solutions and those submitted to the Systemic website (http://www.oklo.org).

  4. Particle Swarm Optimization for Structural Design Problems

    Directory of Open Access Journals (Sweden)

    Hamit SARUHAN

    2010-02-01

    Full Text Available The aim of this paper is to employ the Particle Swarm Optimization (PSO technique to a mechanical engineering design problem which is minimizing the volume of a cantilevered beam subject to bending strength constraints. Mechanical engineering design problems are complex activities which are computing capability are more and more required. The most of these problems are solved by conventional mathematical programming techniques that require gradient information. These techniques have several drawbacks from which the main one is becoming trapped in local optima. As an alternative to gradient-based techniques, the PSO does not require the evaluation of gradients of the objective function. The PSO algorithm employs the generation of guided random positions when they search for the global optimum point. The PSO which is a nature inspired heuristics search technique imitates the social behavior of bird flocking. The results obtained by the PSO are compared with Mathematical Programming (MP. It is demonstrated that the PSO performed and obtained better convergence reliability on the global optimum point than the MP. Using the MP, the volume of 2961000 mm3 was obtained while the beam volume of 2945345 mm3 was obtained by the PSO.

  5. 基于改进混沌粒子群算法的管网优化%Pipe Network Optimization Based on Improved Chaotic Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    乔俊飞; 潘广源; 韩红桂

    2013-01-01

    This paper analyses and improves the no purpose in initial and ineffective for jumping out of the local optimum in the late on Chaotic Particle Swarm Optimization(CPSO),based on the randomness,stochastic property and regularity of chaos to find a new superior individual by chaotic searching on the global best individual.Defined chaos parametersμ according to the particularity of the water distribution network diameter,introduces an improved method of CPSO(HMCPSO),and improves the ability for seeking the global excellent result.By introducing the mechanism of startup of CPSO,the convergence ability in early stage is improved effectively.By introducing the mechanism of judge the historical data of particle position,reduces the excess operations of chaos,the calculation time is reduced effectively.HMCPSO is then applied in the pipe network; comparing with PSO and CPSO,the result shows HMCPSO has higher speed and more stable outcomes.%针对混沌粒子群运算初期的无目的性、固定的控制参量在运算后期不利于跳出局部最优等进行了分析和改进,利用混沌运动的随机性、遍历性和规律性特点对粒子群体中的最优粒子进行混沌寻优,根据给水管网管径选取的离散化特殊性,对混沌粒子群的混沌参量μ进行公式化规定,提出一种改进型混沌粒子群算法(HMCPSO),提高粒子群算法摆脱局部极优的能力.通过引入混沌算法启动机制,有效提高种群初期的收敛能力,通过引入粒子群位置的历史数据判断机制,减少多余的混沌运算,有效缩短算法运行时间.将本改进算法应用于给水管网的模型中,仿真效果表明文中提出的改进算法与PSO和CPSO算法相比,找到的结果更优且稳定性较好,运算时间得到有效减少.

  6. Autotuning algorithm of particle swarm PID parameter based on D-Tent chaotic model

    Institute of Scientific and Technical Information of China (English)

    Min Zhu; Chunling Yang; Weiliang Li

    2013-01-01

    An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed al-gorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportional-integral-derivative (PID) parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor (PMSM) under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.

  7. Prediction of RNA Secondary Structure Based on Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    LIU Yuan-ning; DONG Hao; ZHANG Hao; WANG Gang; LI Zhi; CHEN Hui-ling

    2011-01-01

    A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO). PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization(IPSO) was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to e ncoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.

  8. Particle Swarm Optimization approach to defect detection in armour ceramics.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2017-03-01

    In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function.

  9. Hybrid particle swarm optimization for multiobjective resource allocation

    Institute of Scientific and Technical Information of China (English)

    Yi Yang; Li Xiaoxing; Gu Chunqin

    2008-01-01

    Resource allocation (RA) is the problem of allocating resources among various artifacts or business units to meet one or more expected goals,such as maximizing the profits,minimizing the costs,or achieving the best qualities.A complex multiobjective RA is addressed,and a multiobjective mathematical model is used to find solutions efficiently.Then,an improved particle swarm algorithm (mO_PSO) is proposed combined with a new particle diversity controller policies and dissipation operation.Meanwhile,a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented.The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.

  10. Steady-State Configuration and Tension Calculations of Marine Cables Under Complex Currents via Separated Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    徐雪松

    2014-01-01

    Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton−Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.

  11. Fractional order Darwinian particle swarm optimization applications and evaluation of an evolutionary algorithm

    CERN Document Server

    Couceiro, Micael

    2015-01-01

    This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, suc

  12. An Improved Quantum-behaved Particle Swarm Optimization with Cauchy Mutation Operator%一种基于Cauchy变异的量子粒子群算法

    Institute of Scientific and Technical Information of China (English)

    高浩; 吴冬梅; 丁洁

    2012-01-01

    Comparing with the other optimization algorithms, particle swarm optimization (PSO)has the merits of fast convergence rata and easy application, but the results of research have shown that the PSO has the shortcoming that it is easily be trapped into local optimum. A quantum-behaved particle swarm optimization with Cauchy mutation (CQPSO) is presented. The results of experiment show that the new algorithm not only accelerate the global search ability but also accelerate the convergence rate of the PSO, so it can be applied in engineering optimization problems.%和其他优化算法相比,粒子群算法有着简单易实现以及寻优结果快的优点,但研究结果表明标准粒子群算法在优化过程中存在着易于陷入最小的缺陷。文章提出了一种基于Cauchy策略的量子-粒子群算法。标准测试函数的仿真结果表明,新的算法不仅能够提高算法的全局搜索能力,而且能够加快算法的寻优速度,能够应用在实际工程中的函数优化问题。

  13. On the premature convergence of particle swarm optimization

    DEFF Research Database (Denmark)

    Larsen, Rie B.; Jouffroy, Jerome; Lassen, Benny

    2016-01-01

    This paper discusses convergence issues of the basic particle swarm optimization algorithm for different pa- rameters. For the one-dimensional case, it is shown that, for a specific range of parameters, the particles will converge prematurely, i.e. away from the actual minimum of the objective...

  14. Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

    DEFF Research Database (Denmark)

    Rutkowski Olesen, Jakob; Cordero, Jorge; Zeng, Yifeng

    2009-01-01

    This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data...

  15. Fractional particle swarm optimization in multidimensional search space.

    Science.gov (United States)

    Kiranyaz, Serkan; Ince, Turker; Yildirim, Alper; Gabbouj, Moncef

    2010-04-01

    In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this problem, we propose the fractional global best formation (FGBF) technique, which basically collects all the best dimensional components and fractionally creates an artificial global best (aGB) particle that has the potential to be a better "guide" than the PSO's native gbest particle. This way, the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. We investigated both individual and mutual applications of the proposed techniques over the following two well-known domains: 1) nonlinear function minimization and 2) data clustering. An extensive set of experiments shows that in both application domains, MD PSO with FGBF exhibits an impressive speed gain and converges to the global optima at the true dimension regardless of the search space dimension, swarm size, and the complexity of the problem.

  16. Particle swarm optimization using multi-information characteristics of all personal-best information.

    Science.gov (United States)

    Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

    2016-01-01

    Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.

  17. Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

    Science.gov (United States)

    Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

    2015-01-01

    The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

  18. Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Sanjay Saini

    Full Text Available The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO. However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO. The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF and Hierarchical Particle Swarm Optimization (HPSO. Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

  19. Implementasi Algoritma Particle Swarm untuk Menyelesaikan Sistem Persamaan Nonlinear

    Directory of Open Access Journals (Sweden)

    Ardiana Rosita

    2012-09-01

    Full Text Available Penyelesaian sistem persamaan nonlinear merupakan salah satu permasalahan yang sulit pada komputasi numerik dan berbagai aplikasi teknik. Beberapa metode telah dikembangkan untuk menyelesaikan sistem persamaan ini dan metode Newton merupakan metode yang paling sering digunakan. Namun metode ini memerlukan perkiraan solusi awal dan memilih perkiraan solusi awal yang baik untuk sebagian besar sistem persamaan nonlinear tidaklah mudah. Pada makalah ini, algoritma Particle Swarm yang diusulkan oleh Jaberipour dan kawan-kawan[1] diimplementasikan. Algoritma ini merupakan pengembangan dari algoritma Particle Swarm Optimization (PSO. Algoritma ini meyelesaikan sistem persamaan nonlinear yang sebelumnya telah diubah menjadi permasalahan optimasi. Uji coba dilakukan terhadap beberapa fungsi dan sistem persamaan nonlinear untuk menguji kinerja dan efisiensi algoritma. Berdasarkan hasil uji coba, beberapa fungsi dan sistem persamaan nonlinear telah konvergen pada iterasi ke 10 sampai 20 dan terdapat fungsi yang konvergen pada iterasi ke 200. Selain itu, solusi yang dihasilkan algoritma Particle Swarm mendekati solusi eksak.

  20. Application of particle swarm optimization algorithm in the heating system planning problem.

    Science.gov (United States)

    Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

    2013-01-01

    Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

  1. A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

    Directory of Open Access Journals (Sweden)

    Weitian Lin

    2014-01-01

    Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

  2. Design of Low Noise Microwave Amplifiers Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Sadık Ülker

    2012-07-01

    Full Text Available This short paper presents a work on the design of low noise microwave amplifiers using particle swarm optimization (PSO technique. Particle Swarm Optimization is used as a method that is applied to a single stage amplifier circuit to meet two criteria: desired gain and desired low noise. The aim is to get the best optimized design using the predefined constraints for gain and low noise values. The code is written to apply the algorithm to meet the desired goals and the obtained results are verified using different simulators. The results obtained show that PSO can be applied very efficiently for this kind of design problems with multiple constraints.

  3. Entropy Diversity in Multi-Objective Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Eduardo J. Solteiro Pires

    2013-12-01

    Full Text Available Multi-objective particle swarm optimization (MOPSO is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.

  4. NEURAL NETWORK TRAINING WITH PARALLEL PARTICLE SWARM OPTIMIZER

    Institute of Scientific and Technical Information of China (English)

    Qin Zheng; Liu Yu; Wang Yu

    2006-01-01

    Objective To reduce the execution time of neural network training. Methods Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which is implemented on a cluster using MPI libraries for inter-process communication. Results High speed-up factor is achieved and execution time is reduced greatly. On the other hand, the resulting neural network has good classification accuracy not only on training sets but also on test sets. Conclusion Since the fitness evaluation is intensive, parallel particle swarm optimization shows great advantages to speed up neural network training.

  5. OPTIMIZING LOCALIZATION ROUTE USING PARTICLE SWARM-A GENETIC APPROACH

    Directory of Open Access Journals (Sweden)

    L. Lakshmanan

    2014-01-01

    Full Text Available One of the most key problems in wireless sensor networks is finding optimal algorithms for sending packets from source node to destination node. Several algorithms exist in literature, since some are in vital role other may not. Since WSN focus on low power consumption during packet transmission and receiving, finally we adopt by merging swarm particle based algorithm with genetic approach. Initially we order the nodes based on their energy criterion and then focusing towards node path; this can be done using Proactive route algorithm for finding optimal path between Source-Destination (S-D nodes. Fast processing and pre traversal can be done using selective flooding approach and results are in genetic. We have improved our results with high accuracy and optimality in rendering routes.

  6. Particle swarm optimization based space debris surveillance network scheduling

    Science.gov (United States)

    Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao

    2017-02-01

    The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.

  7. Particle swarm optimization algorithm for partner selection in virtual enterprise

    Institute of Scientific and Technical Information of China (English)

    Qiang Zhao; Xinhui Zhang; Renbin Xiao

    2008-01-01

    Partner selection is a fundamental problem in the formation and success of a virtual enterprise. The partner selection problem with precedence and due date constraint is the basis of the various extensions and is studied in this paper. A nonlinear integer program model for the partner selection problem is established. The problem is shown to be NP-complete by reduction to the knapsack problem, and therefore no polynomial time algorithm exists. To solve it efficiently, a particle swarm optimization (PSO) algorithm is adopted, and several mechanisms that include initialization expansion mechanism, variance mechanism and local searching mechanism have been developed to improve the performance of the proposed PSO algorithm. A set of experiments have been conducted using real examples and numerical simulation, and have shown that the PSO algorithm is an effective and efficient way to solve the partner selection problems with precedence and due date constraints.

  8. 基于改进粒子群算法的神经网络优化证券投资组合方法%The Optimization of Portfolio Method of Neural Network Based on Improved Particle Swarm Optimizer algorithm

    Institute of Scientific and Technical Information of China (English)

    黄招娣

    2014-01-01

    采用人工神经网络对证券投资进行预测与分析的研究过程中,提高神经网络各个节点参数的优化能力是极其关键的。传统的神经网络存在学习速度慢、易陷入局部极小值、预测结果精度较低等缺点,一种改进型粒子群(Improved Particle Swarm Optimizer, IPSO)算法,可以优化BP (Back Propagation)神经网络,并将优化后的BP神经网络应用于优化证券投资组合中。实验结果表明:该研究方法能够在预测精度和稳定性方面明显优于传统的PSO-BP神经网络优化证券投资组合方法。%In the artificial-neural- work-base forecast and analyses of portfolio investment, it is extremely important to improve the optimizing ability of each node parameters in neural network. Targeted to deal with traditional neural networks' shortcomings of slow learning speed, easy occurrence of local minimal value and lower prediction accuracy, we put forward an Improved Particle Swarm algorithm (Improved Particle Swarm Optimizer, IPSO), with which BP (Back Propagation) neural network is optimized and applied to optimal securities portfolio. The results show that this method is superior than traditional PSO-BP neural network optimization portfolio method.

  9. Nontoxic colloidal particles impede antibiotic resistance of swarming bacteria by disrupting collective motion and speed

    Science.gov (United States)

    Lu, Shengtao; Liu, Fang; Xing, Bengang; Yeow, Edwin K. L.

    2015-12-01

    A monolayer of swarming B. subtilis on semisolid agar is shown to display enhanced resistance against antibacterial drugs due to their collective behavior and motility. The dynamics of swarming motion, visualized in real time using time-lapse microscopy, prevents the bacteria from prolonged exposure to lethal drug concentrations. The elevated drug resistance is significantly reduced when the collective motion of bacteria is judiciously disrupted using nontoxic polystyrene colloidal particles immobilized on the agar surface. The colloidal particles block and hinder the motion of the cells, and force large swarming rafts to break up into smaller packs in order to maneuver across narrow spaces between densely packed particles. In this manner, cohesive rafts rapidly lose their collectivity, speed, and group dynamics, and the cells become vulnerable to the drugs. The antibiotic resistance capability of swarming B. subtilis is experimentally observed to be negatively correlated with the number density of colloidal particles on the engineered surface. This relationship is further tested using an improved self-propelled particle model that takes into account interparticle alignment and hard-core repulsion. This work has pertinent implications on the design of optimal methods to treat drug resistant bacteria commonly found in swarming colonies.

  10. Network Traffic Prediction based on Particle Swarm BP Neural Network

    Directory of Open Access Journals (Sweden)

    Yan Zhu

    2013-11-01

    Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.

  11. Design and simulation of equilateral triangular microstrip antenna using particle swarm optimization (PSO) and advanced particle swarm optimization (APSO)

    Indian Academy of Sciences (India)

    PRABAL PRATAP; RAVINDER SINGH BHATIA; BINOD KUMAR

    2016-07-01

    In this paper a new design is proposed in microstrip antenna family. In this paper, a review design of microstrip antenna design using particle swarm optimization (PSO) and advanced particle swarm optimization (APSO) has been presented which optimizes the parameters and both results are compared. This technique helps antenna engineers to design, analyze, and simulate antenna efficiently and effectively. An advanced PSO driven antenna has been developed to calculate resonant frequency of slit-cut stacked equilateral triangular microstrip antenna. The paper presents simplicity, accuracy and comparison of result between PSO and APSO.

  12. The path planning of UAV based on orthogonal particle swarm optimization

    Science.gov (United States)

    Liu, Xin; Wei, Haiguang; Zhou, Chengping; Li, Shujing

    2013-10-01

    To ensure the attack mission success rate, a trajectory with high survivability and accepted path length and multiple paths with different attack angles must be planned. This paper proposes a novel path planning algorithm based on orthogonal particle swarm optimization, which divides population individual and speed vector into independent orthogonal parts, velocity and individual part update independently, this improvement advances optimization effect of traditional particle swarm optimization in the field of path planning, multiple paths are produced by setting different attacking angles, this method is simulated on electronic chart, the simulation result shows the effect of this method.

  13. Improved hybrid particle swarm algorithm based on simulated annealing%基于自适应模拟退火的改进混合粒子群算法

    Institute of Scientific and Technical Information of China (English)

    杨文光; 严哲; 隋丽丽

    2015-01-01

    为了改善旅行商(TSP)优化求解能力,对模拟退火与混合粒子群算法进行改进,引入了自适应寻优策略。交叉、变异的混合粒子群算法,易于陷入局部最优,而自适应的模拟退火算法可以跳出局部最优,进行全局寻优,所以两者的结合兼顾了全局和局部。该算法增加的自适应性寻优策略提供了判定粒子是否陷入局部极值的条件,并可借此以一定概率进行自适应寻优,增强了全局寻优能力。与混合粒子群算法实验结果对比,显示了本文算法的有效性。%In order to enhance the ability of solving TSP optimization, the hybrid particle swarm optimization (PSO) algorithm with simulated annealing is improved, which introduced the adaptive optimization strategy. Hybrid particle swarm optimization algorithm with crossover and mutation, is easy to fall into local optimum, and the simulated annealing algorithm can avoid local optimization, so the combination of both global and lo-cal. This algorithm increases the adaptive optimization strategy which provided to determine whether the parti-cles fall into local extreme conditions, and can be used to with a certain probability of adaptive optimization, enhanced the ability of global optimization. Compared with the hybrid particle swarm algorithm experimental results, shows the effectiveness of the proposed algorithm.

  14. Application of an Improved Particle Swarm Optimization Algorithm in Software Test Data Generation%改进PSO算法在软件测试数据生成中的应用

    Institute of Scientific and Technical Information of China (English)

    时贵英

    2012-01-01

    Software testing is an important means of software quality assurance, the automatic generation of test cases has been widely studied. By analyzing the advantages and disadvantages of the genetic algorithm, the particle swarm optimization algorithm and the ant colony algorithm,the paper proposes a new improved particle swarm algorithm in the automatic generation of test cases. The pheromone mechanism of the ant colony algorithm is introduced into the particle swarm algorithm, which can increase the diversity of particles and overcome the defect that PSO is easy to premature and stagnation. Finally the simulation experiment proves the feasibility and efficiency of the algorithm in software testing.%软件测试是软件质量保证的重要手段,测试用例自动生成一直是被广泛研究的问题.本文在分析了遗传算法、粒子群算法和蚁群算法的优缺点后,在软件测试用例的自动生成过程中采用一种新改进的粒子群算法.该算法将蚁群算法的信息素机制引入到粒子群算法中,加大了粒子间的多样性,有效地克服了粒子群算法容易发生早熟停滞的缺陷.最后通过仿真实验证明了算法应用于软件测试的可行性和高效性.

  15. Particle Swarm Optimization with Watts-Strogatz Model

    Science.gov (United States)

    Zhu, Zhuanghua

    Particle swarm optimization (PSO) is a popular swarm intelligent methodology by simulating the animal social behaviors. Recent study shows that this type of social behaviors is a complex system, however, for most variants of PSO, all individuals lie in a fixed topology, and conflict this natural phenomenon. Therefore, in this paper, a new variant of PSO combined with Watts-Strogatz small-world topology model, called WSPSO, is proposed. In WSPSO, the topology is changed according to Watts-Strogatz rules within the whole evolutionary process. Simulation results show the proposed algorithm is effective and efficient.

  16. 基于改进粒子群算法的3RRR并联机器人的优化设计%Optimization Design of 3RRR Parallel Robot based on Improved Particle Swarm Algorithm

    Institute of Scientific and Technical Information of China (English)

    胡晓雄; 贾育秦

    2013-01-01

    A new improved particle swarm algorithm is proposed based on binary particle swarm algorithm,at the same time,the improved algorithm is applied to 3RRR parallel robot the size parameters design and the simulation results show that the improved algorithm is effective and feasible.The important theoretical basis of this class robot applications in the processing of agricultural products and agriculture is provided.%基于二进制粒子群算法提出一种新的改进粒子群算法,同时将该改进算法应用于3RRR并联机器人的尺寸参数优化设计,并通过仿真实验表明了该改进算法有效可行,为该类机器人在农业及农产品加工中的应用提供了重要的理论依据.

  17. An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application

    Institute of Scientific and Technical Information of China (English)

    LI Xing-mei; ZHANG Li-hui; QI Jian-xun; ZHANG Su-fang

    2008-01-01

    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 free-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 thee effectiveness and stronger global convergence ability of the EPSO.

  18. 基于改进型粒子群算法的板式换热器优化设计%Plate Heat Exchanger Optimization Design Based on Improved Particle Swarm Algorithm

    Institute of Scientific and Technical Information of China (English)

    余静飞; 姜波; 胡申华

    2013-01-01

    In the optimization process of plate heat exchanger(PHE), many variables were involved, there were boundary constraints for some of these variables, such as flow rate. In this article,starting from the effect of the flow rate on heat transfer and pressure, thermodynamic model was built. Improvements have been made on the basis of elementary particle swarm optimization. The improved particle swarm algorithm was used in the plate heat exchanger optimization design, successful resolved the problem of plate heat exchanger cost optimization design with a constrained flow rate.%在板式换热器优化设计过程中,涉及到的变量较多,对有些变量的取值范围有界限约束,比如流速。本文从流速对换热效果和压力的影响出发,建立热力学模型,在基本粒子群算法上做了改进,利用此改进的粒子群算法对板式换热器流速受限的情况进行优化设计,解决了板式换热器中流速受到约束时的成本优化问题。

  19. Vehicle routing problem of logistics distribution based on improved particle swarm optimization algorithm%基于改进粒子群算法的物流配送车辆调度优化

    Institute of Scientific and Technical Information of China (English)

    吴聪; 杨建辉

    2015-01-01

    Vehicle routing scheduling is an important factor to improve the operation efficiency of logistics enterprises, to solve the defects of the standard particle swarm optimization algorithm, an improved particle swarm optimization algorithm for vehicle routing problem of logistics distribution is proposed. Firstly, the mathematical model for vehicle routing problem of logistics distribution is established, and then the vehicle and vehicle routing are encoded into particles, the optimal scheme for vehicle routing problem of logistics distribution is found by the collaboration between particles in which de-fects of the particle swarm algorithm are improved, finally the simulation experiment is used to test the performance. The results show that the proposed algorithm not only accelerates the solving speed, but also increases the obtaining the optimal solution probability or vehicle routing problem of logistics distribution problem, and has some advantages than other scheduling algorithms.%车辆优化调度是提高物流企业运营效益的重要因素,针对标准粒子群优化算法存在的不足,提出一种改进粒子群算法(IPSO)的物流配送车辆调度优化方法。建立物流配送车辆调度优化的数学模型,将车辆与车辆路径编码成粒子,通过粒子之间的协作找到最优物流配送车辆调度优化方案,并对粒子群算法存在的不足进行了相应的改进,最后给出仿真实验对其性能进行测试。实验结果表明,IPSO算法不仅加快了物流配送车辆调度优化问题求解的速度,而且获得了最优解的概率,具有比其他调度算法更明显的优势。

  20. An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

    Directory of Open Access Journals (Sweden)

    Ming Li

    2014-01-01

    Full Text Available The disadvantages of particle swarm optimization (PSO algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster.

  1. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

    Science.gov (United States)

    Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

    2013-01-01

    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

  2. Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

    Directory of Open Access Journals (Sweden)

    Razana Alwee

    2013-01-01

    Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

  3. Improvements To Glowworm Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Piotr Oramus

    2010-01-01

    Full Text Available Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multipleoptima of multimodal functions. The algorithm uses an ensemble of agents, which scan thesearch space and exchange information concerning a fitness of their current position. Thefitness is represented by a level of a luminescent quantity called luciferin. An agent movesin direction of randomly chosen neighbour, which broadcasts higher value of the luciferin.Unfortunately, in the absence of neighbours, the agent does not move at all. This is anunwelcome feature, because it diminishes the performance of the algorithm. Additionally,in the case of parallel processing, this feature can lead to unbalanced loads. This paperpresents simple modifications of the original algorithm, which improve performance of thealgorithm by limiting situations, in which the agent cannot move. The paper provides resultsof comparison of an original and modified algorithms calculated for several multimodal testfunctions.

  4. 基于改进粒子群算法的图像匹配技术研究%Image Matching Technology Based on Improved Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    冯浩; 李现伟

    2016-01-01

    在分析基本粒子群优化算法的基础上,对学习因子进行非线性异步策略调整,改变其固定常数模式,平衡算法在迭代过程中的局部和全局搜索能力;同时引入活力因子,对失活粒子执行变异操作,提高种群多样性。改进算法可以提升对多维空间的全局寻优能力,避免粒子产生早熟收敛现象。将改进粒子群算法引入图像匹配优化问题中,提出了一种基于改进粒子群算法的图像匹配算法,实验结果表明,该算法具有更快的匹配速度以及更高的匹配精度,具有强鲁棒性。%Based on the analysis of the basic particle swarm optimization algorithm,the thesis adjusted the learn⁃ing factor by the strategy of nonlinear asynchronous,changed the model of fixed constant,and balanced the global and local search ability in the process of iteration;simultaneously the thesis introduced the activity factor to im⁃prove the population diversity which performed mutation for the particles who lost energy.The improved algorithm can improve the global search capability in the multidimensional space,and avoided premature convergence phe⁃nomenon.The improved particle swarm algorithm was introduced into image matching optimization problem,and proposed an image matching algorithm based on the improved particle swarm algorithm,the experimental results showed that The algorithm has the advantages of faster matching speed and higher matching accuracy,and has strong robustness.

  5. 基于改进粒子群优化SVM的多分类入侵检测研究%Research on intrusion detection based on improved particle swarm optimization SVM

    Institute of Scientific and Technical Information of China (English)

    杨智慧; 王华忠; 颜秉勇; 陈冬青

    2016-01-01

    For the problem of traditional intrusion detection algorithms with low detection accurate rate which is caused by the high dimensional characteristics of the industrial control network data and diversity of attack patterns, a improved particle swarm optimization ( PSO) algorithm which used to optimize the parameters of support vector machine ( SVM ) , and an improved PSO-SVM multi classification intrusion detection method is proposed. The support vector machine parameters are op-timized by particle swarm of particles while the SVM classification accuracy is used as particle swarm target function for global search to determine the optimal parameters of SVM. Base on the improved PSO-SVM “one to one” classification an industrial intrusion detection mode is established. Finally, the simulation experiment is carried on with the latest proposed industrial standard data sets by the Mississippi State University Center for critical infrastructure protection. The results show that the av-erage detection accuracy rate of the proposed algorithm can reach more than 90% for different ways of attacking, and can identify the type of attack accurately. The improved PSO-SVM provides an ef-fective method for the intrusion detection of industrial control system.%针对工控网络数据的高维特性以及攻击方式多样性而导致传统入侵检测算法检测准确率低等问题,采用改进粒子群( PSO)算法优化支持向量机的参数,提出改进的PSO-SVM多分类入侵检测方法。该方法将SVM参数作为改进PSO的粒子,将SVM分类准确率作为PSO的目标函数进行全局搜索以确定SVM的最优参数,建立基于改进PSO-SVM的“一对一”多分类工控入侵检测模型。最后采用密西西比州立大学关键基础设施保护中心提出的工控标准数据集进行仿真实验,结果表明,该算法针对不同的攻击方式的平均检测准确率均能达到90%以上,能够准确识别攻击类型,可为工控

  6. A Particle Swarm Optimization with Adaptive Multi-Swarm Strategy for Capacitated Vehicle Routing Problem

    Directory of Open Access Journals (Sweden)

    Kui-Ting CHEN

    2015-12-01

    Full Text Available Capacitated vehicle routing problem with pickups and deliveries (CVRPPD is one of the most challenging combinatorial optimization problems which include goods delivery/pickup optimization, vehicle number optimization, routing path optimization and transportation cost minimization. The conventional particle swarm optimization (PSO is difficult to find an optimal solution of the CVRPPD due to its simple search strategy. A PSO with adaptive multi-swarm strategy (AMSPSO is proposed to solve the CVRPPD in this paper. The proposed AMSPSO employs multiple PSO algorithms and an adaptive algorithm with punishment mechanism to search the optimal solution, which can deal with large-scale optimization problems. The simulation results prove that the proposed AMSPSO can solve the CVRPPD with the least number of vehicles and less transportation cost, simultaneously.

  7. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-01-01

    Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

  8. Particle swarm optimization applied to impulsive orbital transfers

    Science.gov (United States)

    Pontani, Mauro; Conway, Bruce A.

    2012-05-01

    The particle swarm optimization (PSO) technique is a population-based stochastic method developed in recent years and successfully applied in several fields of research. It mimics the unpredictable motion of bird flocks while searching for food, with the intent of determining the optimal values of the unknown parameters of the problem under consideration. At the end of the process, the best particle (i.e. the best solution with reference to the objective function) is expected to contain the globally optimal values of the unknown parameters. The central idea underlying the method is contained in the formula for velocity updating. This formula includes three terms with stochastic weights. This research applies the particle swarm optimization algorithm to the problem of optimizing impulsive orbital transfers. More specifically, the following problems are considered and solved with the PSO algorithm: (i) determination of the globally optimal two- and three-impulse transfer trajectories between two coplanar circular orbits; (ii) determination of the optimal transfer between two coplanar, elliptic orbits with arbitrary orientation; (iii) determination of the optimal two-impulse transfer between two circular, non-coplanar orbits; (iv) determination of the globally optimal two-impulse transfer between two non-coplanar elliptic orbits. Despite its intuitiveness and simplicity, the particle swarm optimization method proves to be capable of effectively solving the orbital transfer problems of interest with great numerical accuracy.

  9. Pebble bed reactor fuel cycle optimization using particle swarm algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Tavron, Barak, E-mail: btavron@bgu.ac.il [Planning, Development and Technology Division, Israel Electric Corporation Ltd., P.O. Box 10, Haifa 31000 (Israel); Shwageraus, Eugene, E-mail: es607@cam.ac.uk [Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ (United Kingdom)

    2016-10-15

    Highlights: • Particle swarm method has been developed for fuel cycle optimization of PBR reactor. • Results show uranium utilization low sensitivity to fuel and core design parameters. • Multi-zone fuel loading pattern leads to a small improvement in uranium utilization. • Thorium mixes with highly enriched uranium yields the best uranium utilization. - Abstract: Pebble bed reactors (PBR) features, such as robust thermo-mechanical fuel design and on-line continuous fueling, facilitate wide range of fuel cycle alternatives. A range off fuel pebble types, containing different amounts of fertile or fissile fuel material, may be loaded into the reactor core. Several fuel loading zones may be used since radial mixing of the pebbles was shown to be limited. This radial separation suggests the possibility to implement the “seed-blanket” concept for the utilization of fertile fuels such as thorium, and for enhancing reactor fuel utilization. In this study, the particle-swarm meta-heuristic evolutionary optimization method (PSO) has been used to find optimal fuel cycle design which yields the highest natural uranium utilization. The PSO method is known for solving efficiently complex problems with non-linear objective function, continuous or discrete parameters and complex constrains. The VSOP system of codes has been used for PBR fuel utilization calculations and MATLAB script has been used to implement the PSO algorithm. Optimization of PBR natural uranium utilization (NUU) has been carried out for 3000 MWth High Temperature Reactor design (HTR) operating on the Once Trough Then Out (OTTO) fuel management scheme, and for 400 MWth Pebble Bed Modular Reactor (PBMR) operating on the multi-pass (MEDUL) fuel management scheme. Results showed only a modest improvement in the NUU (<5%) over reference designs. Investigation of thorium fuel cases showed that the use of HEU in combination with thorium results in the most favorable reactor performance in terms of

  10. A Diversity-Guided Particle Swarm Optimizer - the ARPSO

    DEFF Research Database (Denmark)

    Vesterstrøm, Jacob Svaneborg; Riget, Jacques

    2002-01-01

    The particle swarm optimization (PSO) algorithm is a new population based search strat- egy, which has exhibited good performance on well-known numerical test problems. How- ever, on strongly multi-modal test problems the PSO tends to suffer from premature convergence. This is due to a decrease...... that the ARPSO prevents premature convergence to a high degree, but still keeps a rapid convergence like the basic PSO. Thus, it clearly outperforms the basic PSO as well as the implemented GA in multi-modal optimization. Keywords Particle Swarm Optimization, Diversity-Guided Search 1 Introduction The PSO model...... is a new population based optimization strategy introduced by J. Kennedy et al. in 1995 (Kennedy95). It has already shown to be comparable in performance with tra- ditional optimization algorithms such as simulated annealing (SA) and the genetic algorithm (GA) (Angeline98; Eberhart98; Krink01; Vesterstrom...

  11. Genetic algorithm and particle swarm optimization combined with Powell method

    Science.gov (United States)

    Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

    2013-10-01

    In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

  12. EXPERIENCE WITH SYNCHRONOUS GENERATOR MODEL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

    OpenAIRE

    N.RATHIKA; Dr.A.Senthil kumar; A.ANUSUYA

    2014-01-01

    This paper intends to the modeling of polyphase synchronous generator and minimization of power losses using Particle swarm optimization (PSO) technique with a constriction factor. Usage of Polyphase synchronous generator mainly leads to the total power circulation in the system which can be distributed in all phases. Another advantage of polyphase system is the fault at one winding does not lead to the system shutdown. The Process optimization is the chastisement of adjusting a process so as...

  13. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    Science.gov (United States)

    Garro, Beatriz A; Vázquez, Roberto A

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

  14. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

    Directory of Open Access Journals (Sweden)

    Beatriz A. Garro

    2015-01-01

    Full Text Available Artificial Neural Network (ANN design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO, Second Generation of Particle Swarm Optimization (SGPSO, and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE and the classification error (CER and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

  15. Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems

    Directory of Open Access Journals (Sweden)

    Geng Zhang

    2015-01-01

    Full Text Available Although the original particle swarm optimizer (PSO method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO. In case that the interacting of the elements in D-dimensional function vector X=[x1,x2,…,xd,…,xD] is independent, cooperative particle swarm optimizer (CPSO is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms.

  16. A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

    Institute of Scientific and Technical Information of China (English)

    ZHOU Xiao-Jun; YANG Chun-Hua; GUI Wei-Hua; DONG Tian-Xue

    2014-01-01

    The convergence analysis of the standard particle swarm optimization (PSO) has shown that the changing of random functions, personal best and group best has the potential to improve the performance of the PSO. In this paper, a novel strategy with variable random functions and polynomial mutation is introduced into the PSO, which is called particle swarm optimization algorithm with variable random functions and mutation (PSO-RM). Random functions are adjusted with the density of the population so as to manipulate the weight of cognition part and social part. Mutation is executed on both personal best particle and group best particle to explore new areas. Experiment results have demonstrated the effectiveness of the strategy.

  17. 基于改进离散粒子群优化算法的逆向物流选址模型研究%Study on Reverse Logistics Location Model Based on Improved Discrete Particle Swarm Algorithm

    Institute of Scientific and Technical Information of China (English)

    杨名

    2012-01-01

    针对逆向物流中存在的问题,为了降低逆向物流总成本,提高逆向物流的运转效率,对逆向物流的选址问题进行了重点研究.以离散粒子群优化算法为基础,对粒子进行重新编码,使得算法的收敛速度加快,挖掘精度提高,选择一个合适的回收中心.%In this paper to reduced total cost of reverse logistics and improve its operational efficiency, we studies the location problem of reverse logistics and on the basis of the improved discrete particle swarm algorithm which was re—coded and quicker in convergence, selected a suitable recycling center.

  18. Particle Swarm Optimization for Hydraulic Analysis of Water Distribution Systems

    Directory of Open Access Journals (Sweden)

    Naser Moosavian

    2015-06-01

    Full Text Available The analysis of flow in water-distribution networks with several pumps by the Content Model may be turned into a non-convex optimization uncertain problem with multiple solutions. Newton-based methods such as GGA are not able to capture a global optimum in these situations. On the other hand, evolutionary methods designed to use the population of individuals may find a global solution even for such an uncertain problem. In the present paper, the Content Model is minimized using the particle-swarm optimization (PSO technique. This is a population-based iterative evolutionary algorithm, applied for non-linear and non-convex optimization problems. The penalty-function method is used to convert the constrained problem into an unconstrained one. Both the PSO and GGA algorithms are applied to analyse two sample examples. It is revealed that while GGA demonstrates better performance in convex problems, PSO is more successful in non-convex networks. By increasing the penalty-function coefficient the accuracy of the solution may be improved considerably.

  19. Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Ya-zhong Luo

    2014-01-01

    Full Text Available A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

  20. Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization

    Science.gov (United States)

    Kiranyaz, Serkan; Uhlmann (Eurasip Member), Stefan; Ince, Turker; Gabbouj, Moncef

    2010-12-01

    Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.

  1. Particle swarm optimization algorithm based low cost magnetometer calibration

    Science.gov (United States)

    Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.

    2011-12-01

    Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments

  2. Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Moncef Gabbouj

    2009-01-01

    Full Text Available Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO for finding optimal (number of dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis- similarities over HSV (or HSL color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.

  3. 基于改进粒子群算法的云计算任务调度算法%A Task Scheduling Algorithm Based on Improved Particle Swarm Optimization for Cloud Computing

    Institute of Scientific and Technical Information of China (English)

    娄建峰; 高岳林; 李飞; 张克平

    2016-01-01

    Facing with large amount of tasks in cloud computing ,a task scheduling algorithm based on improved Particle Swarm Optimization(PSO) was taken into research to minimize the task completion time and maximize the resource utilization .Firstly ,the velocity and position of each particle are set randomly within the search space in the initialization .A swarm of particles are used to represent the potential solutions and the position of each particle is encoded by natural number .After each iteration update ,legalized method was used to repair the particle .In order to reduce the probability of particles running out of the solution space ,some method was taken to limit the velocity of each particle .For overcoming precocious ,chaos was combined with PSO .By using chaos disturbance ,the particles can have a better position .Cloudsim was used to stimulate cloud computing environment for experimental test . Experimental results show that compared with tradition PSO the improved PSO converges faster and have a better scheduling result .%面对云计算中的大量任务,为了对其进行高效的调度,缩短任务完成时间并提高资源利用率,对基于粒子群算法的云计算任务调度算法进行了研究。首先用自然数对粒子编码表示粒子的位置,并在解空间内随机初始化种群,每个粒子的位置对应一个可行的调度方案。每次迭代更新后,对粒子进行修复操作,为降低粒子跑出解空间的概率,同时对粒子速度进行限定。针对传统粒子群算法易陷入早熟的缺陷,加入混沌扰动策略使种群跳出局部最优。通过Cloudsim仿真平台进行实验测试,实验结果表明该算法能取得更好的调度结果并且收敛速度更快。

  4. Design of Low Noise Microwave Amplifiers Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Sadık Ulker

    2012-08-01

    Full Text Available This short paper presents a work on the design of low noise microwave amplifiers using particle swarmoptimization (PSO technique. Particle Swarm Optimization is used as a method that is applied to a singlestage amplifier circuit to meet two criteria: desired gain and desired low noise. The aim is to get the bestoptimized design using the predefined constraints for gain and low noise values. The code is written toapply the algorithm to meet the desired goals and the obtained results are verified using differentsimulators. The results obtained show that PSO can be applied very efficiently for this kind of designproblems with multiple constraints.

  5. 粒子群优化的改进机场车辆调度模型研究%Particle swarm optimization to improve airport vehicle scheduling model research

    Institute of Scientific and Technical Information of China (English)

    刘洋; 肖伟

    2015-01-01

    With the rapid development of aviation, the airport security and efficiency of the vehicle scheduling status has been increasingly highlighted, the traditional airport vehicle scheduling uses the strategy of First in First out , this strategy algorithm is simple, easy to implement, defect is all scheduling group being treated the same, cannot provide delay guaran-tee for real-time demanding business, algorithm with impartiality. This paper proposes an improved airport vehicle sched-uling model based on optimization particle swarm , the particle swarm as a whole has to search the global optimal location as a special particle, using gradient descent strategy optimization of the particle, global optimization characteristics and optimization neighborhood of the gradient descent algorithm, feature fusion, to enhance the efficiency of particle swarm optimization algorithm of global optimization, reduce vehicle scheduling calculation time at the airport. Simulation experi-ments show that the particle swarm optimization to improve the airport vehicle scheduling model, can reduce the number of traditional scheduling method optimization iterations, and shorten the optimal operation time, effectively relieve air blockage caused by resources waste.%随着航空事业的迅猛发展,机场车辆调度的安全性和时效性地位已日趋突显,传统的机场车辆调度采取First in first out策略,该策略算法简易,便于实施,缺陷是全部调度的分组被相同对待,无法为实时要求较高的业务提供时延保证,算法也不具有公正性。提出了一种基于粒子群优化的改进机场车辆调度模型,把粒子群已经搜索到的全局最优地点视为一个特殊的粒子,采用梯度降低策略寻优该粒子,全局寻优特性和梯度降低算法的邻域寻优特性相融合,以提升粒子群优化算法的全局寻优效率,减少机场车辆调度计算的时间。仿真实验表明:粒子群优化的改进

  6. 基于改进量子粒子群算法的无人机路径规划%UAV Path Planning Based on Improved Quantum-behaved Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    郭蕴华; 王晓宗

    2016-01-01

    An improved algorithm based on the quantum-behaved particle swarm optimization is proposed for UAV path planning problem .The updated position of the particle is adaptively adjusted by the distance between the particle and the local at -tractor point and the distance between the particle and the boundary of the feasible region .Simulation results show that the im-proved QPSO algorithm can get the track with higher quality , and its convergence is better .%针对无人机路径规划算法收敛速度慢、易陷入局部最优等问题,提出一种改进的量子粒子群算法. 该算法根据粒子与局部吸引点和可行解边界的距离动态的修正粒子更新位置,改善了算法的全局寻优能力和收敛性能. 仿真实验表明所提出的改进QPSO算法比其他已有算法可以得到更高质量的航迹,并且其收敛性较好.

  7. Celestial Navigation Fix Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Tsou Ming-Cheng

    2015-09-01

    Full Text Available A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.

  8. PARTICLE SWARM OPTIMIZATION BASED ON PYRAMID MODEL FOR SATELLITE MODULE LAYOUT

    Institute of Scientific and Technical Information of China (English)

    Zhang Bao; Teng Hongfei

    2005-01-01

    To improve the global search ability of particle swarm optimization (PSO), a multi-population PSO based on pyramid model (PPSO) is presented. Then, it is applied to solve the layout optimization problems against the background of an international commercial communication satellite (INTELSAT-Ⅲ) module. Three improvements are developed, including multi-population search based on pyramid model, adaptive collision avoidance among particles, and mutation of degraded particles. In the numerical examples of the layout design of this simplified satellite module, the performance of PPSO is compared to global version PSO and local version PSO (ring and Neumann PSO). The results show that PPSO has higher computational accuracy, efficiency and success ratio.

  9. Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem

    Directory of Open Access Journals (Sweden)

    Lifang He

    2016-01-01

    Full Text Available The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowworm swarm optimization (IGSO algorithm has been presented to find the optimal multilevel thresholds of color image based on the between-class variance and minimum cross entropy (MCE. The proposed methods are examined on standard set of color test images by using various numbers of threshold values. The results are then compared with those of basic glowworm swarm optimization, adaptive particle swarm optimization (APSO, and self-adaptive differential evolution (SaDE. The simulation results show that the proposed method can find the optimal thresholds accurately and efficiently and is an effective multilevel thresholding method for color image segmentation.

  10. Parameter estimation for chaotic systems based on improved boundary chicken swarm optimization

    Science.gov (United States)

    Chen, Shaolong; Yan, Renhuan

    2016-10-01

    Estimating unknown parameters for chaotic system is a key problem in the field of chaos control and synchronization. Through constructing an appropriate fitness function, parameter estimation of chaotic system could be converted to a multidimensional parameter optimization problem. In this paper, a new method base on improved boundary chicken swarm optimization (IBCSO) algorithm is proposed for solving the problem of parameter estimation in chaotic system. However, to the best of our knowledge, there is no published research work on chicken swarm optimization for parameters estimation of chaotic system. Computer simulation based on Lorenz system and comparisons with chicken swarm optimization, particle swarm optimization, and genetic algorithm shows the effectiveness and feasibility of the proposed method.

  11. A closed-loop particle swarm optimizer for multivariable process controller design

    Institute of Scientific and Technical Information of China (English)

    Kai HAN; Jun ZHAO; Zu-hua XU; Ji-xin QIAN

    2008-01-01

    Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem.A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories.At each time step,a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness.With this modification,limitations caused by a uniform inertia weight for the whole population are avoided,and the particles have enough diversity.After the effectiveness,efficiency and robustness are tested by benchmark functions,CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.

  12. Optimize BP Neural Network by an Improved Particle Swarm Optimization to Implement Nuclide Identification%一种改进粒子群算法优化BP神经网络实现核素识别方法

    Institute of Scientific and Technical Information of China (English)

    刘议聪; 朱泓光; 宋永强

    2016-01-01

    To get the global optimal point, propose an optimize BP neural network by an improved particle swarm optimization (PSO) to implement nuclide identification. It changes inertia weight and learning factor dynamically with self-adaption to optimize the weight value and threshold value of BP neural network. It gets the global optimal value of the particle swarm by training BP neural network to identify models. Finally, it implements nuclide identification by using the optimal weight and threshold value. The experiment shows our proposed method can not only converge to the optimal value faster but also do a good balance between local search and global search. Therefore, it significantly improves the convergence speed and the accuracy of nuclide identification.%为获得全局最优点,提出一种改进粒子群算法优化BP神经网络实现核素识别方法.该算法用一种动态改变惯性权重与学习因子的自适应方法,优化BP神经网络的阈值与权值,通过训练BP神经网络识别模型得到粒子群的全局最优解,利用最优权值与阈值实现核素识别.分析结果表明:该方法不仅能更快地收敛于最优解,同时能更好地平衡全局搜索和局部搜索能力,有效地改善算法的收敛速度和识别精度.

  13. Particle Swarm Optimization-Proximal Point Algorithm for Nonlinear Complementarity Problems

    OpenAIRE

    Chai Jun-Feng; Wang Shu-Yan

    2013-01-01

    A new algorithm is presented for solving the nonlinear complementarity problem by combining the particle swarm and proximal point algorithm, which is called the particle swarm optimization-proximal point algorithm. The algorithm mainly transforms nonlinear complementarity problems into unconstrained optimization problems of smooth functions using the maximum entropy function and then optimizes the problem using the proximal point algorithm as the outer algorithm and particle swarm algorithm a...

  14. Particle swarm optimization of ascent trajectories of multistage launch vehicles

    Science.gov (United States)

    Pontani, Mauro

    2014-02-01

    Multistage launch vehicles are commonly employed to place spacecraft and satellites in their operational orbits. If the rocket characteristics are specified, the optimization of its ascending trajectory consists of determining the optimal control law that leads to maximizing the final mass at orbit injection. The numerical solution of a similar problem is not trivial and has been pursued with different methods, for decades. This paper is concerned with an original approach based on the joint use of swarming theory and the necessary conditions for optimality. The particle swarm optimization technique represents a heuristic population-based optimization method inspired by the natural motion of bird flocks. Each individual (or particle) that composes the swarm corresponds to a solution of the problem and is associated with a position and a velocity vector. The formula for velocity updating is the core of the method and is composed of three terms with stochastic weights. As a result, the population migrates toward different regions of the search space taking advantage of the mechanism of information sharing that affects the overall swarm dynamics. At the end of the process the best particle is selected and corresponds to the optimal solution to the problem of interest. In this work the three-dimensional trajectory of the multistage rocket is assumed to be composed of four arcs: (i) first stage propulsion, (ii) second stage propulsion, (iii) coast arc (after release of the second stage), and (iv) third stage propulsion. The Euler-Lagrange equations and the Pontryagin minimum principle, in conjunction with the Weierstrass-Erdmann corner conditions, are employed to express the thrust angles as functions of the adjoint variables conjugate to the dynamics equations. The use of these analytical conditions coming from the calculus of variations leads to obtaining the overall rocket dynamics as a function of seven parameters only, namely the unknown values of the initial state

  15. An Orthogonal Multi-Swarm Cooperative PSO Algorithm with a Particle Trajectory Knowledge Base

    Directory of Open Access Journals (Sweden)

    Jun Yang

    2017-01-01

    Full Text Available A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for multi-dimensional optimization problems, but also conceives a new adaptive cooperation mechanism to accomplish the information interaction among swarms and particles. Experiments are conducted on a set of benchmark functions, and the results show its better performance compared with traditional PSO algorithm in aspects of convergence, computational efficiency and avoiding premature convergence.

  16. IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR INTELLIGENTLY SETTING UAV ATTITUDE CONTROLLER PARAMETERS%基于改进微粒群算法的无人机姿态控制参数智能整定

    Institute of Scientific and Technical Information of China (English)

    浦黄忠; 甄子洋; 王道波; 胡勇

    2009-01-01

    提出了基于改进微粒群算法的无人机姿态控制器参数智能整定方法.标准微粒群算法在搜索后期由于群体缺乏多样性而容易出现收敛停滞现象,为此提出了一种改进的微粒群算法.标准微粒群算法中的微粒速度是根据惯性运动、群体历史最优位置和自身历史最优位置来调节的.改进微粒群算法中的微粒除了保持惯性运动外,仅向当前群体中任意更优个体的状态学习,而且惯性权重系数是随机数.改进方案减少了算法不确定参数,简化了微粒学习机制,且增强了群体多样性.本文构建了无人机姿态控制系统,将改进微粒群算法用于四个控制参数的寻优整定.仿真结果表明,改进微粒群算法比一般微粒群算法具有更强的全局搜索能力,故获得更优的无人机姿态控制参数.%An improved particle swarm optimization (PSO) algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle (UAV). Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global

  17. An Image Enhancement Method Using the Quantum-Behaved Particle Swarm Optimization with an Adaptive Strategy

    Directory of Open Access Journals (Sweden)

    Xiaoping Su

    2013-01-01

    Full Text Available Image enhancement techniques are very important to image processing, which are used to improve image quality or extract the fine details in degraded images. In this paper, two novel objective functions based on the normalized incomplete Beta transform function are proposed to evaluate the effectiveness of grayscale image enhancement and color image enhancement, respectively. Using these objective functions, the parameters of transform functions are estimated by the quantum-behaved particle swarm optimization (QPSO. We also propose an improved QPSO with an adaptive parameter control strategy. The QPSO and the AQPSO algorithms, along with genetic algorithm (GA and particle swarm optimization (PSO, are tested on several benchmark grayscale and color images. The results show that the QPSO and AQPSO perform better than GA and PSO for the enhancement of these images, and the AQPSO has some advantages over QPSO due to its adaptive parameter control strategy.

  18. Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems

    Directory of Open Access Journals (Sweden)

    Zahra Beheshti

    2014-01-01

    Full Text Available In recent years, particle swarm optimization (PSO has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20 unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.

  19. Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

    Science.gov (United States)

    Janapati, Ravi Chander; Balaswamy, Ch.; Soundararajan, K.

    2016-08-01

    Localization is the key research area in Wireless Sensor Networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao Bound (CRB). This censoring scheme can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper Distributed localization algorithm PSO with CRB is proposed. Proposed method shows better results in terms of position accuracy, latency and complexity.

  20. Finite element model selection using Particle Swarm Optimization

    CERN Document Server

    Mthembu, Linda; Friswell, Michael I; Adhikari, Sondipon

    2009-01-01

    This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has t...

  1. Incorporate Energy Strategy into Particle Swarm Optimizer Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lun; DONG De-cun; LU Yan; CHEN Lan

    2008-01-01

    The issue of optimizing the dynamic parameters in Particle Swarm Optimizer (PSO) is addressed in this paper.An algorithm is designed which makes all particles originally endowed with a certain level energy, what here we define as EPSO (Energy Strategy PSO).During the iterative process of PSO algorithm, the Inertia Weight is updated according to the calculation of the particle's energy.The portion ratio of the current residual energy to the initial endowed energy is used as the parameter Inertia Weight which aims to update the particles' velocity efficiently.By the simulation in a graph theoritical and a functional optimization problem respectively, it could be easily found that the rate of convergence in EPSO is obviously increased.

  2. 基于改进粒子群算法的无刷直流电机控制研究%Brushless DC Motor Control Research Based on Improved Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    付光杰; 杨帛润; 高俊莹

    2013-01-01

    To consider the complex and coupled nonlinear characteristics of a brushless DC motor,and to overcome the low speed accuracy and slow response shortcoming of the traditional control algorithm,a new strategy for the coefficient of PID controller tuning of brushless DC motor control system simulation modeling based on improved particle swarm optimization (PSO) is proposed in this paper.Building a simulation model of BLDC control system in Matlab/Simulink,and to achieve the dual closed-loop control:The velocity loop adopts the PID control based on particle swarm optimization,the current loop uses hysteresis current control.Simulation results verify the control method of brushless DC motor speed control system has more rapidity,stability and robustness.%文章针对无刷直流电机(BLDC)复杂的、耦合的非线性的特点,克服传统的控制算法控制速度精度不高、响应速度慢等缺点,提出了一种基于改进的粒子群算法(PSO)对PID控制器系数自整定的无刷直流电机控制新策略.在Matlab/Simulink中搭建BLDC控制系统的仿真模型,并实现双闭环的控制:速度环采用改进粒子群算法优化PID控制,电流环采用滞环电流控制.仿真结果验证了该控制方法对无刷直流电机调速系统具有良好的快速性、稳定性和鲁棒性.

  3. 基于改进谱聚类与粒子群优化的图像分割算法%Image Segmentation Algorithm Based on Improved Spectral Clustering and Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

      本文提出了一种基于改进谱聚类与粒子群优化的图像分割算法。该算法利用双树复小波变换系数,求得能量均值构造相似性矩阵,充分利用了待聚类数据所包含的空间邻近信息和特征相似性信息。在谱映射的过程中,采用了Nystr迸m逼近策略,降低了谱聚类算法的复杂度和内存消耗,然后在进行K均值聚类时使用粒子群优化算法。最后,通过对医学图像和遥感图像分割验证了新算法的有效性。%A image segmentation algorithm based on improved spectral clustering and particle swarm optimization is proposed .Similarity matrix is constructed by the mean of dual -tree complex wavelet transform coefficients in this dissertation so as to make full use of the spatial adjacency information and feature similarity information included in the data .To efficiently apply the algorithm to image segmentation ,Nyström approximation strategy is used in the course of spectral mapping to reduce the computation complexity and memory consumption .And then we tentatively adopt particle swarm optimization algorithm to optimize the K - means clustering in the spectral clustering algorithm .Experimental results on medical images and remote sensing images verify the validity of the proposed algorithm .

  4. Swarm.

    Science.gov (United States)

    Petersen, Hugh

    2002-01-01

    Describes an eighth grade art project for which students created bug swarms on scratchboard. Explains that the project also teaches students about design principles, such as balance. Discusses how the students created their drawings. (CMK)

  5. Learning Bayesian Networks from Data by Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Learning Bayesian network is an NP-hard problem. When the number of variables is large, the process of searching optimal network structure could be very time consuming and tends to return a structure which is local optimal. The particle swarm optimization (PSO) was introduced to the problem of learning Bayesian networks and a novel structure learning algorithm using PSO was proposed. To search in directed acyclic graphs spaces efficiently, a discrete PSO algorithm especially for structure learning was proposed based on the characteristics of Bayesian networks. The results of experiments show that our PSO based algorithm is fast for convergence and can obtain better structures compared with genetic algorithm based algorithms.

  6. Multidimensional particle swarm optimization for machine learning and pattern recognition

    CERN Document Server

    Kiranyaz, Serkan; Gabbouj, Moncef

    2013-01-01

    For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.  After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in chal

  7. Hybrid particle swarm optimization for solving resource-constrained FMS

    Institute of Scientific and Technical Information of China (English)

    Dongyun Wang; Liping Liu

    2008-01-01

    In this paper,an approach for resource-constrained flexible manufacturing system(FMS)scheduling was proposed,which is based on the particle swarm optimization(PSO)algorithm and simulated annealing(SA)algorithm.First,the formulation for resource-con-strained FMS scheduling problem was introduced and cost function for this problem was obtained.Then.a hybrid algorithm of PSO and SA was employed to obtain optimal solution.The simulated results show that the approach can dislodge a state from a local min-imum and guide it to the global minimum.

  8. Optimization of mechanical structures using particle swarm optimization

    Energy Technology Data Exchange (ETDEWEB)

    Leite, Victor C.; Schirru, Roberto, E-mail: victor.coppo.leite@lmp.ufrj.br [Coordenacao dos Programas de Pos-Graduacao em Engenharia (LMP/PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Lab. de Monitoracao de Processos

    2015-07-01

    Several optimization problems are dealed with the particle swarm optimization (PSO) algorithm, there is a wide kind of optimization problems, it may be applications related to logistics or the reload of nuclear reactors. This paper discusses the use of the PSO in the treatment of problems related to mechanical structure optimization. The geometry and material characteristics of mechanical components are important for the proper functioning and performance of the systems were they are applied, particularly to the nuclear field. Calculations related to mechanical aspects are all made using ANSYS, while the PSO is programed in MATLAB. (author)

  9. Power System Aggregate Load Area Modelling by Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    Jian-Lin Wei; Ji-Hong Wang; Q.H.Wu; Nan Lu

    2005-01-01

    This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.

  10. Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms

    Directory of Open Access Journals (Sweden)

    Haifa Mehdi

    2011-11-01

    Full Text Available This paper presents an efficient and fast method for fine tuning the controller parameters of robot manipulators in constrained motion. The stability of the robotic system is proved using a Lyapunov‐based impedance approach whereas the optimal design of the controller parameters are tuned, in offline, by a Particle Swarm Optimization (PSO algorithm. For designing the PSOmethod,differentindexperformancesare considered in both joint and Cartesian spaces. A 3DOF manipulator constrained to a circular trajectory is finally used to validate the performances of the proposed approach. The simulation results show the stability and the performances of the proposed approach.

  11. Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

    DEFF Research Database (Denmark)

    Pinto, Tiago; Vale, Zita; Sousa, Tiago

    2014-01-01

    for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method......, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day...

  12. SECURE STEGANOGRAPHY BASED ON BINARY PARTICLE SWARM OPTIMIZATION

    Institute of Scientific and Technical Information of China (English)

    Guo Yanqing; Kong Xiangwei; You Xingang

    2009-01-01

    The objective of steganography is to hide message securely in cover objects for secret communication. How to design a secure steganographic algorithm is still major challenge in this research field. In this letter, developing secure steganography is formulated as solving a constrained IP (Integer Programming) problem, which takes the relative entropy of cover and stego distributions as the objective function. Furthermore, a novel method is introduced based on BPSO (Binary Particle Swarm Optimization) for achieving the optimal solution of this programming problem. Experimental results show that the proposed method can achieve excellent performance on preserving neighboring co-occurrence features for JPEG steganography.

  13. PID control for chaotic synchronization using particle swarm optimization

    Energy Technology Data Exchange (ETDEWEB)

    Chang, W.-D. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)], E-mail: wdchang@mail.stu.edu.tw

    2009-01-30

    In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.

  14. Transmitter antenna placement in indoor environments using particle swarm optimisation

    Science.gov (United States)

    Talepour, Zeinab; Tavakoli, Saeed; Ahmadi-Shokouh, Javad

    2013-07-01

    The aim of this article is to suitably locate the minimum number of transmitter antennas in a given indoor environment to achieve good propagation coverage. To calculate the electromagnetic field in various points of the environment, we develop a software engine, named ray-tracing engine (RTE), in Matlab. To achieve realistic calculations, all parameters of geometry and material of building are considered. Particle swarm optimisation is employed to determine good location of transmitters. Simulation results show that a full coverage is obtained through suitably locating three transmitters.

  15. Particle Swarm Optimization Applied to the Economic Dispatch Problem

    Directory of Open Access Journals (Sweden)

    Rafik Labdani

    2006-06-01

    Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via a simple particle swarm optimization (PSO algorithm. The objective is to minimize the fuel cost and keep the power outputs of generators, bus voltages, shunt capacitors/reactors and transformers tap-setting in their secure limits.The effectiveness of PSO was compared to that of OPF by MATPOWER. The potential and superiority of PSO have been demonstrated through the results of IEEE 30-bus system

  16. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

    Science.gov (United States)

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-03-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

  17. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

    Directory of Open Access Journals (Sweden)

    Huanqing Cui

    2017-03-01

    Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

  18. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

    Science.gov (United States)

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-01-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

  19. Particle swarm optimization with a leader and followers

    Institute of Scientific and Technical Information of China (English)

    Junwei Wang; Dingwei Wang

    2008-01-01

    Referring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO, a team is formed by the particles with a leader and some followers. The role of the leader is to determine the search direction. The followers decide their flying modes according to their distances to the leader individually. Thus, a wide area can be explored and the particle collision can be really avoided. When GTO is applied to four benchmark examples of complex nonlinear functions, it has a better computation performance than the standard PSO.

  20. 基于改进粒子群算法的图像阈值分割方法%Image Threshold Segmentation Method Based on Improved Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    章慧; 龚声蓉; 严云洋

    2012-01-01

    针对图像提取问题,最优阈值选取是否合理对图像分割效果至关重要.在处理不同种类图像区域时,粒子群算法(PSO)由于早熟现象难以准确计算最优分割阈值,因此导致图像分割准确率低.为了提高图像分割准确率且准确地提取出图像目标,提出一种基于混沌粒子群算法(CPSO)的图像阈值分割方法.受益于混沌运行的遍历性、对初始条件的敏感性等优点,CPSO很好地解决了PSO的粒子群过早聚集和陷入局部最优等难题,加快了全局搜索最优解的能力.采用具体图像对CPSO算法图像分割性能进行仿真实验,结果表明,相比于其它图像分割算法,CPSO不仅加快了运算速度,提高了图像分割效率,而且提高了图像分割准确率,非常适合于图像实时分割处理.%Aiming at image extraction problem , optimal threshold selection is key for image segmentation results. In processing different kinds of image region, because of particle swarm optimization algorithm (PSO)'s premature phe-nomenon, it is difficult to accurately calculate the optimal segmentation threshold image segmentation, and accuracy rate is low. In order to improve the segmentation accuracy and accurate extraction of the image target, this paper proposed a image threshold segmentation methods based on chaos particle swaim optimization algorithm(CPSO). Benefit from chaotic operation of ergodicity, sensitivity to initial conditions and other advantages, CPSO improves particle swarm premature aggregation and can notbe traped in a local optimum problem, accelerates the overall optimal solution search ability. The CPSO image segmentation performance is best by simulation experiment,and the experimental results show that, compared with other image segmentation algorithm,CPSO not only accelerates the speed of operation , improves the efficiency of image segmentation,but also improves the segmentation accuracy,and it is very suitable for real-time image

  1. A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

    Science.gov (United States)

    Ma, Yanfang; Xu, Jiuping

    2015-06-01

    This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.

  2. Power System Stabilizer Design Based on a Particle Swarm Optimization Multiobjective Function Implemented Under Graphical Interface

    Directory of Open Access Journals (Sweden)

    Ghouraf Djamel Eddine

    2016-05-01

    Full Text Available Power system stability considered a necessary condition for normal functioning of an electrical network. The role of regulation and control systems is to ensure that stability by determining the essential elements that influence it. This paper proposes a Particle Swarm Optimization (PSO based multiobjective function to tuning optimal parameters of Power System Stabilizer (PSS; this later is used as auxiliary to generator excitation system in order to damp electro mechanicals oscillations of the rotor and consequently improve Power system stability. The computer simulation results obtained by developed graphical user interface (GUI have proved the efficiency of PSS optimized by a Particle Swarm Optimization, in comparison with a conventional PSS, showing stable   system   responses   almost   insensitive   to   large parameter variations.Our present study was performed using a GUI realized under MATLAB in our work.

  3. WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER

    Directory of Open Access Journals (Sweden)

    Andi Adriansyah

    2016-02-01

    Full Text Available Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO. A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

  4. A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

    Science.gov (United States)

    Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

    2017-01-01

    An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

  5. The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.

    Science.gov (United States)

    Han, Gaining; Fu, Weiping; Wang, Wen

    2016-01-01

    In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

  6. The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

    Directory of Open Access Journals (Sweden)

    Gaining Han

    2016-01-01

    Full Text Available In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

  7. Security Constrained Optimal Power Flow with FACTS Devices Using Modified Particle Swarm Optimization

    Science.gov (United States)

    Somasundaram, P.; Muthuselvan, N. B.

    This paper presents new computationally efficient improved Particle Swarm algorithms for solving Security Constrained Optimal Power Flow (SCOPF) in power systems with the inclusion of FACTS devices. The proposed algorithms are developed based on the combined application of Gaussian and Cauchy Probability distribution functions incorporated in Particle Swarm Optimization (PSO). The power flow algorithm with the presence of Static Var Compensator (SVC) Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC), has been formulated and solved. The proposed algorithms are tested on standard IEEE 30-bus system. The analysis using PSO and modified PSO reveals that the proposed algorithms are relatively simple, efficient, reliable and suitable for real-time applications. And these algorithms can provide accurate solution with fast convergence and have the potential to be applied to other power engineering problems.

  8. DAILY SCHEDULING OF SMALL HYDRO POWER PLANTS DISPATCH WITH MODIFIED PARTICLES SWARM OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Sinvaldo Rodrigues Moreno

    2015-04-01

    Full Text Available This paper presents a new approach for short-term hydro power scheduling of reservoirs using an algorithm-based Particle Swarm Optimization (PSO. PSO is a population-based algorithm designed to find good solutions to optimization problems, its characteristics have encouraged its adoption to tackle a variety of problems in different fields. In this paper the authors consider an optimization problem related to a daily scheduling of small hydro power dispatch. The goal is construct a feasible solution that maximize the cascade electricity production, following the environmental constraints and water balance. The paper proposes an improved Particle Swarm Optimization (PSO algorithm, which takes advantage of simplicity and facility of implementation. The algorithm was successfully applied to the optimization of the daily schedule strategies of small hydro power plants, considering maximum water utilization and all constraints related to simultaneous water uses. Extensive computational tests and comparisons with other heuristics methods showed the effectiveness of the proposed approach.

  9. Classification of rice planthopper based on improved gray level co-occurrence matrix and particle swarm algorithm%基于改进灰度共生矩阵和粒子群算法的稻飞虱分类

    Institute of Scientific and Technical Information of China (English)

    邹修国; 丁为民; 陈彩蓉; 刘德营

    2014-01-01

    The rice planthopper images acquired by remote real-time recognition system usually have poor quality, and hence it is impossible to classify rice planthoppers using the color features of rice planthopper images. This study proposed to extract texture features of images based on gray level co-occurrence matrix (GLCM) and used the texture features to classify rice planthoppers. A H-shape mobile photographing device designed by us was used to obtain color images of rice planthoppers. The color images were grayed by formula, and then the background of images was removed using Otsu image segmentation method to generate binary images followed by calculation through the binary image coordinates. The GLCM was improved to extract texture features of images without background. Specifically, the center of gravity was determined by coordinates of the images and considered as the center to construct GLCM. The images of the rice planthopper were copied into the sub images with 160 pixels×160 pixels based on the center. Using multiple annular routes, the features of rice planthopper gray images were extracted including energy, entropy, moment of inertia and correlation. In the training and testing experiment of the extracted features, back propagation (BP) nerve network and optimized BP nerve network based on parametric selection -improved particle swarm optimization algorithm were individually used to train and classify the rice planthopper, and the training time and identification rate of each method were compared. A total of 300Sogatella,Laodelphax andNilaparvata lugens with 100 samples for each type of rice planthopper was trained. The training time using the optimized BP nerve network based on improved particle swarm optimization algorithm was only 0.5683 seconds, which was far less than that (29.5772 seconds) using BP neural network. Based on the BP neural network, the identification rate reached 80% forSogatella, 90% forLaodelphax, and 95% forNilaparvata lugens. Based on

  10. A Study of Load Flow Analysis Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Deepak Saini

    2015-01-01

    Full Text Available Load flow study is done to determine the power system static states (voltage magnitudes and voltage angles at each bus to find the steady state working condition of a power system. It is important and most frequently carried out study performed by power utilities for power system planning, optimization, operation and control. In this project a Particle Swarm Optimization (PSO is proposed to solve load flow problem under different loading/ contingency conditions for computing bus voltage magnitudes and angles of the power system. With the increasing size of power system, this is very necessary to finding the solution to maximize the utilization of existing system and to provide adequate voltage support. For this the good voltage profile is must. STATCOM, if placed optimally can be effective in providing good voltage profile and in turn resulting into stable power system. The study presents a hybrid particle swarm based methodology for solving load flow in electrical power systems. Load flow is an electrical engineering well-known problem which provides the system status in the steady-state and is required by several functions performed in power system control centers.

  11. Control strategy of maglev vehicles based on particle swarm algorithm

    Institute of Scientific and Technical Information of China (English)

    Hui Wang; Gang Shen; Jinsong Zhou

    2014-01-01

    Taking a single magnet levitation system as the object, a nonlinear numerical model of the vehicle-guide-way coupling system was established to study the levitation control strategies. According to the similarity in dynamics, the single magnet-guideway coupling system was simpli-fied into a magnet-suspended track system, and the corre-sponding hardware-in-loop test rig was set up using dSPACE. A full-state-feedback controller was developed using the levitation gap signal and the current signal, and controller parameters were optimized by particle swarm algorithm. The results from the simulation and the test rig show that, the proposed control method can keep the sys-tem stable by calculating the controller output with the full-state information of the coupling system, Step responses from the test rig show that the controller can stabilize the system within 0.15 s with a 2% overshot, and performs well even in the condition of violent external disturbances. Unlike the linear quadratic optimal method, the particle swarm algorithm carries out the optimization with the nonlinear controlled object included, and its optimized results make the system responses much better.

  12. Quantum-behaved particle swarm optimization with collaborative attractors for nonlinear numerical problems

    Science.gov (United States)

    Liu, Tianyu; Jiao, Licheng; Ma, Wenping; Shang, Ronghua

    2017-03-01

    In this paper, an improved quantum-behaved particle swarm optimization (CL-QPSO), which adopts a new collaborative learning strategy to generate local attractors for particles, is proposed to solve nonlinear numerical problems. Local attractors, which directly determine the convergence behavior of particles, play an important role in quantum-behaved particle swarm optimization (QPSO). In order to get a promising and efficient local attractor for each particle, a collaborative learning strategy is introduced to generate local attractors in the proposed algorithm. Collaborative learning strategy consists of two operators, namely orthogonal operator and comparison operator. For each particle, orthogonal operator is used to discover the useful information that lies in its personal and global best positions, while comparison operator is used to enhance the particle's ability of jumping out of local optima. By using a probability parameter, the two operators cooperate with each other to generate local attractors for particles. A comprehensive comparison of CL-QPSO with some state-of-the-art evolutionary algorithms on nonlinear numeric optimization functions demonstrates the effectiveness of the proposed algorithm.

  13. 基于改进的粒子群多阈值算法的白色异性纤维检测%Detection of white foreign fibers based on improved particle swarm algorithm

    Institute of Scientific and Technical Information of China (English)

    王昊鹏; 冯显英; 王娜; 石井

    2013-01-01

    为了提高皮棉中白色异性纤维的识别精度,该文提出了一种基于改进混沌粒子群的白色异性纤维检测算法,该算法将图像的像素点按灰度值分为多类,把所有相邻类间方差看做一个粒子种群,以最大类间方差组作为种群适应度评价函数。通过滑动窗口技术判断算法是否陷入局部最优。有效克服了标准粒子群算法容易陷入局部最优的缺陷。通过试验验证,该文提出的算法对白色异性纤维的识别准确率达到98.6%。通过与标准二维Otsu算法的对比分割试验发现在分割较细小的白色异性纤维以及白色纤维与皮棉发生重叠的情况时,该算法的分割结果比标准二维Otsu算法更准确,噪声点更少。为皮棉异性纤维检测与剔除工艺的改善提供了技术依据。%In order to improve the recognition accuracy of white foreign fibers in cotton, a detection algorithm of white foreign fibers based on improved chaos particle swarm optimization was proposed in this paper. In this algorithm, the image was divided into different classes according to the grey value of image pixels. The variances between adjacent classes were thought of as a particle. All of these particles constituted a particle swarm. The maximum variances between classes were thought of as a fitness function. Therefore, the chaotic particle swarm optimization (PSO) algorithm was applied to image segmentation. The standard particle swarm optimization was easy to fall into a local optimum. Given this problem, this algorithm took the sliding window technology to determine if it falls into a local optimum. This algorithm contrasted the average population fitness in the sliding window with the current population fitness in the sliding window. If the current population fitness was similar to the average population fitness, the algorithm was thought not to fall into the local optimum, continued to evolve, and the sliding window starting

  14. Adaptive feature selection using v-shaped binary particle swarm optimization

    Science.gov (United States)

    Dong, Hongbin; Zhou, Xiurong

    2017-01-01

    Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850

  15. Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight Particle Swarm Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Qiang; WU Jianxin; SUN Yan

    2009-01-01

    Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm(EPSA), is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimization. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.

  16. Fishing for Data: Using Particle Swarm Optimization to Search Data

    Science.gov (United States)

    Caputo, Daniel P.; Dolan, R.

    2010-01-01

    As the size of data and model sets continue to increase, more efficient ways are needed to sift through the available information. We present a computational method which will efficiently search large parameter spaces to either map the space or find individual data/models of interest. Particle swarm optimization (PSO) is a subclass of artificial life computer algorithms. The PSO algorithm attempts to leverage "swarm intelligence” against finding optimal solutions to a problem. This system is often based on a biological model of a swarm (e.g. schooling fish). These biological models are broken down into a few simple rules which govern the behavior of the system. "Agents” (e.g. fish) are introduced and the agents, following the rules, search out solutions much like a fish would seek out food. We have made extensive modifications to the standard PSO model which increase its efficiency as-well-as adding the capacity to map a parameter space and find multiple solutions. Our modified PSO is ideally suited to search and map large sets of data/models which are degenerate or to search through data/models which are too numerous to analyze by hand. One example of this would include radiative transfer models, which are inherently degenerate. Applying the PSO algorithm will allow the degeneracy space to be mapped and thus better determine limits on dust shell parameters. Another example is searching through legacy data from a survey for hints of Polycyclic Aromatic Hydrocarbon emission. What might have once taken years of searching (and many frustrated graduate students) can now be relegated to the task of a computer which will work day and night for only the cost of electricity. We hope this algorithm will allow fellow astronomers to more efficiently search data and models, thereby freeing them to focus on the physics of the Universe.

  17. Swarm Robots Search for Multiple Targets Based on an Improved Grouping Strategy.

    Science.gov (United States)

    Tang, Qirong; Ding, Lu; Yu, Fangchao; Zhang, Yuan; Li, Yinghao; Tu, Haibo

    2017-03-14

    Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movements, which considers the influence range of targets and environmental information they have sensed. The group structure may change dynamically and each group focuses on searching one target. All targets are supposed to be found finally. Obstacle avoidance is considered during the search process. Simulation compared with previous method demonstrates the adaptability, accuracy and efficiency of the proposed strategy in multiple targets searching.

  18. Frequent item sets mining from high-dimensional dataset based on a novel binary particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    张中杰; 黄健; 卫莹

    2016-01-01

    A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset (BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary (QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.

  19. Multi-objective two-stage multiprocessor flow shop scheduling - a subgroup particle swarm optimisation approach

    Science.gov (United States)

    Huang, Rong-Hwa; Yang, Chang-Lin; Hsu, Chun-Ting

    2015-12-01

    Flow shop production system - compared to other economically important production systems - is popular in real manufacturing environments. This study focuses on the flow shop with multiprocessor scheduling problem (FSMP), and develops an improved particle swarm optimisation heuristic to solve it. Additionally, this study designs an integer programming model to perform effectiveness and robustness testing on the proposed heuristic. Experimental results demonstrate a 10% to 50% improvement in the effectiveness of the proposed heuristic in small-scale problem tests, and a 10% to 40% improvement in the robustness of the heuristic in large-scale problem tests, indicating extremely satisfactory performance.

  20. 基于改进粒子群优化的广义K分布杂波模型参数估计方法%Parameter estimation for generalized K-distribution clutter model based on improved particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    刘峥; 张翼; 何峻; 付强

    2011-01-01

    In the modeling, simulation and classification of the clutter, the estimation of model parameters of the clutter is an important research area. For the frequently adopted generalized K-distribution clutter model,the speckle and amplitude modulation components are both assumed to obey the generalized Gamma distribution. It turns out that the parameter estimation in this model is difficult due to high-dimensionality and nonlinearity. In order to solve this problem, this paper applies the improved particle swarm optimization (PSO) to the estimation of parameters of the generalized K-distribution. Specifically, the paper adopts the uniform design method to initialize the particle swarm and employs the strategy of across and mutation to improve the global convergence performance of the standard PSO. In fact, the proposed method can accurately estimate each parameter of the clutter model. Moreover, the method has the advantages of low computation burden, fast convergence rate and preferable stability. It is demonstrated by simulation results that the method is of good adaptability and estimation accuracy, which proves its effectiveness and exactness.%在杂波建模、仿真和分类识别研究中,杂波模型参数估计是一个重要的内容.广义K分布杂波模型的散斑分量和幅度调制分量均服从广义Gamma分布,参数估计存在高维、非线性等问题.将改进的粒子群优化算法应用于广义K分布杂波模型参数估计,采用均匀设计方法初始化粒子群,利用交叉变异策略改善粒子群优化的全局收敛性,该方法能准确地估计杂波模型各参数,计算简单,收敛速度较快,稳定性较好.仿真实验结果表明该方法具有良好的适应性和估计精度,验证了其有效性和准确性.

  1. 基于改进粒子群算法的共形阵列天线综合%Conformal Antenna Array Beam Pattern Synthesis Based on Improved Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    杨永建; 王晟达; 马健; 甘轶; 邓有为

    2012-01-01

    共形阵列天线的赋形方向图综合涉及大量的运算,成为现在研究的一大难点,目前对共形阵方向图综合的研究比较少,且所用算法存在理论复杂、耗时长的缺点.文中运用改进粒子群算法对圆环阵、圆柱阵方向图的综合进行了研究,仿真结果表明,改进粒子群算法能够较快地形成期望方向图,证明了该方法的有效性和实用性.%The shaped of conformal antenna array beam pattern synthesis is one of difficulties in array antennas beam pattern, because larger numbers of operation is needed. Presently, the research for conformal array antennabeam pattern synthesis is less, and the used arithmetic is complex in theoretics and need more times. Improved particle swarm optimization (PSO) to synthetize circu-larand columniform array beam pattern is used, the result of simulation shows the improved optimization can form desired beam pattern quickly, and proves the method is effective and practicability.

  2. Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

    Science.gov (United States)

    Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

    2016-07-14

    Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

  3. Optimization on Reactive Power of Power System Based on Particle Swarm and Its Improved Algorithm%基于粒子群及其改进算法的电力系统无功优化

    Institute of Scientific and Technical Information of China (English)

    钟鸣; 文波; 洪彬倬

    2015-01-01

    This paper introduces mathematic model for optimization on reactive power of power system,discusses application of modern intelligent algorithm in solving nonlinear planning of reactive power optimization and analyzes realization of ap-plication of particle swarm optimization (PSO)algorithm in optimization on reactive power of power system. Discrete parti-cle swarm optimization (DPSO)algorithm was introduced and these two methods were used for processing discrete variables. Example analysis on IEEE-30 node system verified feasibility of PSO and DPSO. It was proved that these two algorithms were of similar optimizing effectiveness but astringency of PSO was better that DPSO while DPSO was more correct and suit-able to process discrete variables than PSO.%介绍了电力系统无功优化的数学模型,论述了现代智能算法在解决无功优化的非线性规划问题中的应用,实现了粒子群优化(particle swarm optimization,PSO)算法在电力系统无功优化问题中的应用。引入离散粒子群(discrete particle swarm optimization,DPSO)算法,采用两种方法对离散变量进行处理。IEEE-30节点系统的算例分析验证了 PSO和DPSO 的可行性。这两种算法具有相近的优化效果,但 PSO 的收敛性优于 DPSO,而DPSO对离散变量的处理比 PSO更准确,也更切合实际。

  4. Stability, Convergence of Harmonious Particle Swarm Optimizer and Its Application

    Institute of Scientific and Technical Information of China (English)

    PAN Feng; CHEN Jie; CAI Tao; GAN Ming-gang; WANG Guang-hui

    2008-01-01

    Particle swarm optimizer (PSO), a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. The sufficient conditions for asymptotic stability of an acceleration factor and inertia weight are deduced in this paper. The value of the inertia weight w is enhanced to (-1,1). Furthermore a new adaptive PSO algorithm-harmonious PSO (HPSO) is proposed and proved that HPSO is a global search algorithm. Finally it is focused on a design task of a servo system controller. Considering the existence of model uncertainty and noise from sensors, HPSO are applied to optimize the parameters of fuzzy PID controller. The experiment results demonstrate the efficiency of the methods.

  5. Differential Evolution and Particle Swarm Optimization for Partitional Clustering

    DEFF Research Database (Denmark)

    Krink, Thiemo; Paterlini, Sandra

    2006-01-01

    for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE...... is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both with respect to precision as well as robustness (reproducibility) of the results. Only for simple data sets, the GA and PSO can obtain the same quality of results. Apart from superior performance, DE is easy...... to implement and requires hardly any parameter tuning compared to substantial tuning for GAs and PSOs. Our study shows that DE rather than GAs should receive primary attention in partitional clustering algorithms....

  6. R2-Based Multi/Many-Objective Particle Swarm Optimization

    Science.gov (United States)

    Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar

    2016-01-01

    We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA. PMID:27656200

  7. Planar straightness error evaluation based on particle swarm optimization

    Science.gov (United States)

    Mao, Jian; Zheng, Huawen; Cao, Yanlong; Yang, Jiangxin

    2006-11-01

    The straightness error generally refers to the deviation between an actual line and an ideal line. According to the characteristics of planar straightness error evaluation, a novel method to evaluate planar straightness errors based on the particle swarm optimization (PSO) is proposed. The planar straightness error evaluation problem is formulated as a nonlinear optimization problem. According to minimum zone condition the mathematical model of planar straightness together with the optimal objective function and fitness function is developed. Compared with the genetic algorithm (GA), the PSO algorithm has some advantages. It is not only implemented without crossover and mutation but also has fast congruence speed. Moreover fewer parameters are needed to set up. The results show that the PSO method is very suitable for nonlinear optimization problems and provides a promising new method for straightness error evaluation. It can be applied to deal with the measured data of planar straightness obtained by the three-coordinates measuring machines.

  8. EXPERIENCE WITH SYNCHRONOUS GENERATOR MODEL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

    Directory of Open Access Journals (Sweden)

    N.RATHIKA

    2014-07-01

    Full Text Available This paper intends to the modeling of polyphase synchronous generator and minimization of power losses using Particle swarm optimization (PSO technique with a constriction factor. Usage of Polyphase synchronous generator mainly leads to the total power circulation in the system which can be distributed in all phases. Another advantage of polyphase system is the fault at one winding does not lead to the system shutdown. The Process optimization is the chastisement of adjusting a process so as to optimize some stipulated set of parameters without violating some constraint. Accurate value can be extracted using PSO and it can be reformulated. Modeling and simulation of the machine is executed. MATLAB/Simulink has been cast-off to implement and validate the result.

  9. Reliability Evaluation of Slopes Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammad Khajehzadeh

    2011-01-01

    Full Text Available The objective of this research is to develop a numerical procedure to reliability evaluation of earth slope and locating the critical probabilistic slip surface. The performance function is  formulated using simplified Bishop’s limit equilibrium method  to calculate the reliability index. The reliability index defined by Hasofer and Lind is used as an index of safety measure. Searching the critical probabilistic surface that is associated with the lowest reliability index will be formulated as an optimization problem. In this paper, particle swarm optimization is applied to calculate the minimum Hasofer and Lind reliability index and critical probabilistic failure surface. To demonstrate the applicability and to investigate the effectiveness of the algorithm, two numerical examples from literature are illustrated. Results show that the proposed method is capable to achieve better solutions for reliability analysis of slope if compared with those reported in the literature.

  10. Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers

    CERN Document Server

    Rogers, Adam

    2011-01-01

    Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automa...

  11. Cosmological parameter estimation using Particle Swarm Optimization (PSO)

    CERN Document Server

    Prasad, Jayanti

    2011-01-01

    Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter space. Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid based approaches) with the dimensionality of the search space. MCMC methods sample the full joint probability distribution (posterior) from which one and two dimensional probability distributions, best fit (average) values of parameters and then error bars can be computed. In the present work we demonstrate the application of another stochastic method, named Particle Swarm Optimization (PSO), that is widely used in the field of engineering and artificial intelligence, for cosmo...

  12. Particle Swarm Optimization with Genetic Operators for Vehicle Routing Problem

    Directory of Open Access Journals (Sweden)

    P. V. PURANIK

    2012-07-01

    Full Text Available Vehicle Routing Problem (VRP is to find shortest route thereby minimizing total cost. VRP is a NP-hard and Combinatorial optimization problem. Such problems increase exponentially with the problem size. Various derivative based optimization techniques are employed for optimization. Derivative based optimization techniques are difficult to evaluate. Therefore parallel search algorithm emerged to solve VRP. In this work, a particle swarm optimization (PSO algorithm and Genetic algorithm (GA with crossover and mutation operator are applied to two typical functions to deal with the problem of VRP efficiently using MATLAB software. Before solving VRP, optimization of functions using PSO and GA are checked. In this paper capacitate VRP with time window (CVRPTW is proposed. The computational result shows generation of input for VRP, optimization of Rastrigin function, Rosenbrock function using PSO and GA.

  13. A fuzzy neural network evolved by particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    PENG Zhi-ping; PENG Hong

    2007-01-01

    A cooperative system of a fuzzy logic model and a fuzzy neural network (CSFLMFNN) is proposed,in which a fuzzy logic model is acquired from domain experts and a fuzzy neural network is generated and prewired according to the model. Then PSO-CSFLMFNN is constructed by introducing particle swarm optimization (PSO) into the cooperative system instead of the commonly used evolutionary algorithms to evolve the prewired fuzzy neural network. The evolutionary fuzzy neural network implements accuracy fuzzy inference without rule matching. PSO-CSFLMFNN is applied to the intelligent fault diagnosis for a petrochemical engineering equipment, in which the cooperative system is proved to be effective. It is shown by the applied results that the performance of the evolutionary fuzzy neural network outperforms remarkably that of the one evolved by genetic algorithm in the convergence rate and the generalization precision.

  14. Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

    Directory of Open Access Journals (Sweden)

    Ye Tian

    2014-01-01

    Full Text Available An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL of lithium-ion (Li-ion batteries based on artificial fish swarm algorithm (AFSA and particle filter (PF, which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.

  15. Logistics distribution vehicle scheduling based on improved particle swarm optimiza-tion%基于改进粒子群算法的物流配送车辆调度

    Institute of Scientific and Technical Information of China (English)

    马冬青; 王蔚

    2014-01-01

    The logistics distribution vehicle scheduling problem is to arrange distribution efficiently with limited resources. The optimization goal is to obtain a program which has lower cost with the constraints of the requirements of the users and the conditions of the vehicles. Affected by many factors, such as the location and requirement of customers, and the transport capacity of delivery vehicles, the vehicle scheduling problem is very complicated. A mathematical model of the double-way vehicle scheduling problem is established, considering the mile limit and user limit, and referring to the classic vehicle routing problem model. Based on the particle swarm optimization, a distribution vehicle scheduling algorithm is given. And by using hill-climbing methods, the local searching ability of the particle swarm optimization algorithm is improved.%物流配送车辆调度问题是指安排有限的车辆有效地完成配送任务。优化目标是在满足客户需求和车辆能力约束的条件下,找出配送成本较低的配送车辆调度方案。由于配送过程受客户位置、配送车辆限制等多种因素影响,导致车辆的调度问题十分复杂。参照经典车辆路径问题模型,考虑了车辆配送里程和用户数等限制,建立了双向车辆调度问题的数学模型。在标准粒子群算法的基础上,引入爬山操作,增加了粒子群的多样性,提高了算法的局部搜索能力,并设计了基于改进粒子群算法的物流配送车辆调度算法,有效地解决了物流配送车辆的优化调度问题。

  16. Detection of Harmful Gas in Ammunition Warehouse Based on BP Neural Network Improved by Particle Swarm Optimization%基于改进BP网络的弹药库房有害气体检测

    Institute of Scientific and Technical Information of China (English)

    刘建国; 安振涛; 张倩; 赵志宁

    2014-01-01

    通过气体采样分析,确定了弹药库房需要重点检测的有害气体种类,在此基础上构建了基于传感器阵列与粒子群优化 BP网络的气体检测系统;针对线性惯性权重调整的粒子群优化 BP神经网络算法的不足,提出了一种新的非线性惯性权重调整方法,既保证了算法在运行前期具有较快的搜索速度和全局搜索能力,又保证了后期具有较高的搜索精度;为了克服算法在运行后期的局部收敛,在算法运行过程中引入速度变异算子,使算法摆脱了易陷入局部最优点的束缚;最后,通过实验对气体检测系统的性能进行了检验。结果表明,该气体检测系统速度快、精度高,能够较好地实现对弹药库房有害气体的检测。%Through the analysis of the gas sample,the species of harmful gas was defined in the ammunition warehouse.The gas analysis system composed of sensor array and BP neural network was established.An advanced BP algorithm based on improved particle swarm opti-mization with non-linear adjustment of inertia weight was proposed by analyzing the shortcomings of BP algorithm using linear adjustment of inertia weight.The algorithm can not only maintain the characteristic of fast speed and global searching in the early convergence phase,but also keep a high precision in the later convergence phase.In order to escape from the local minimum basin of attraction of the later phase,a mutation operator of velocity is added to the Particle Swarm Optimization(PSO)algorithm.In the end,the performance of the algorithm was tested by the experiment.The experimental result indicates that the system has a rapid detection speed and a high precision.It is an effective method for harmful gas detection.

  17. Modeling of photovoltaic-array based on BP neural networks improved by particle swarm optimization algorithm%基于粒子群算法的BP神经网络光伏电池建模

    Institute of Scientific and Technical Information of China (English)

    郭亮; 陈维荣; 贾俊波; 韩明; 刘永浩

    2011-01-01

    针对光伏电池复杂难以建模的非线性特性,本文提出一种基于粒子群算法(PSO)的反向传播(BP)神经网络建模方法.神经网络具有很强的非线性拟合能力,但同时也存在收敛速度慢、容易陷入局部极值、建模精度不高等缺点.本文采用粒子群算法来优化神经网络的内部连接权值,以改善神经网络的性能,并基于这种改进的神经网络构建光伏电池动态模型.测试及仿真结果表明,通过此法建立的光佚电池模型辨识精度高,收敛速度快,取得了较好的效果.%In view of serious complexity and nonlinearity of photovoltaic array, its modeling is very difficult, therefore a Back Propagation (BP) Neural Network model based on Particle Swarm Optimization (PSO) algorithm is proposed. Neural Network has excellent nonlinear fitting ability, but it also has some shortcomings, such as low convergence speed, easy to fall into local optimal value, and low accuracy, etc. PSO algorithm is used to optimize the inner connection weights of Neural Network; therefore the performance of Neural Network is promoted. Modeling of photovoltaic array is based on this improved Neural Network. Test and simulation results showed the high identification accuracy and high convergence speed of this model. Key words: photovoltaic array; neural network; particle swarm optimization algorithm Constructing stable nodes to suppress common-mode EMI for power converters Abstract: In this paper, a simple approach to suppress common mode conducted electromagnetic interference (EMI) is proposed. The method is explained in detail with Boost converter adopted as a typical example. Most of common mode conducted EMI due to the high dv/dt nodes in the converter can be suppressed by constructing stable potential nodes ( dy/dr almost zero) in the circuit, which is implemented by changing placement of the boost inductor and using the common-anode diode instead of the common-cathode one. It is verified by

  18. Object Detection In Image Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Nirbhowjap Singh

    2010-12-01

    Full Text Available Image matching is a key component in almost any image analysis process. Image matching is crucial to a wide range of applications, such as in navigation, guidance, automatic surveillance, robot vision, and in mapping sciences. Any automated system for three-dimensional point positioning must include a potent procedure for image matching. Most biological vision systems have the talent to cope with changing world. Computer vision systems have developed in the same way. For a computer vision system, the ability to cope withmoving and changing objects, changing illumination, and changing viewpoints is essential to perform several tasks. Object detection is necessary for surveillance applications, for guidance of autonomous vehicles, for efficient video compression, for smart tracking of moving objects, for automatic target recognition (ATR systems and for many other applications. Cross-correlation and related techniqueshave dominated the field since the early fifties. Conventional template matching algorithm based on cross-correlation requires complex calculation and large time for object detection, which makes difficult to use them in real time applications. The shortcomings of this class of image matching methods have caused a slow-down in the development of operational automated correlation systems. In the proposed work particle swarm optimization & its variants basedalgorithm is used for detection of object in image. Implementation of this algorithm reduces the time required for object detection than conventional template matching algorithm. Algorithm can detect object in less number of iteration & hence less time & energy than the complexity of conventional template matching. This feature makes the method capable for real time implementation. In this thesis a study of particle Swarm optimization algorithm is done & then formulation of the algorithm for object detection using PSO & its variants is implemented for validating its effectiveness.

  19. Incorporating the Avoidance Behavior to the Standard Particle Swarm Optimization 2011

    Directory of Open Access Journals (Sweden)

    ALTINOZ, O. T.

    2015-05-01

    Full Text Available Inspired from social and cognitive behaviors of animals living as swarms; particle swarm optimization (PSO provides a simple but very powerful tool for researchers who are dealing with collective intelligence. The algorithm depends on modeling the very basic random behavior (i.e. exploration capability of individuals in addition to their tendency to revisit positions of good memories (cognitive behavior and tendency to keep an eye on and follow the majority of swarm members (social behavior. The balance among these three major behaviors is the key of success of the algorithm. On the other hand, there are other social and cognitive phenomena, which might be useful for improvement of the algorithm. In this paper, we particularly investigate avoidance from the bad behavior. We propose modifications about modeling the Standard PSO 2011 formulation, and we test performance of our proposals at each step via benchmark functions, and compare the results of the proposed algorithms with well-known algorithms. Our results show that incorporation of Social Avoidance behavior into SPSO11 improves the performance. It is also shown that in case the Social Avoidance behavior is applied in an adaptive manner at the very first iterations of the algorithm, there might be further improvements.

  20. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    Science.gov (United States)

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  1. Reliable Distribution Feeder Reconfiguration Containing Distributed Generation using Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    I. Moradi

    2014-03-01

    Full Text Available Distributed feeder reconfiguration (DFR is an operation processand a very important methodfor saving electrical energy and loss reduction in distribution systems. This process is carried out by changingdistribution system topology by opening and/or closing of circuit breakers. Status of the circuit breakers is optimally determined to have an improved system operation and reduced power losses. This paper proposes a multi-objective evolutionary method for distribution feeder reconfiguration. The multi-objectives optimization minimizes power losses and improves reliability of the system. For this purpose a particle swarm optimization algorithm is used for solving the problem. Simulation results show the efficiency of the proposed method for DFR

  2. A novel real-time patient-specific seizure diagnosis algorithm based on analysis of EEG and ECG signals using spectral and spatial features and improved particle swarm optimization classifier.

    Science.gov (United States)

    Nasehi, Saadat; Pourghassem, Hossein

    2012-08-01

    This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by Gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature.

  3. Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

    Directory of Open Access Journals (Sweden)

    Jin Yang

    2016-07-01

    Full Text Available Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs. However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs, we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

  4. Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The author introduced particle swarm optimization as a new method for power transmission network expansion planning. A new discrete method for particle swarm optimization, was developed, which is suitable for power transmission network expansion planning, and requires less computer s memory. The optimization fitness function construction, parameter selection, convergence judgement, and their characters were analyzed. Numerical simulation demonstrated the effectiveness and correctness of the method, This paper provides an academic and practical basis of particle swarm optimization in application of transmission network expansion planning for further investigation.

  5. Attractive and Repulsive Particle Swarm Optimization and Random Virus Algorithm for Solving Reactive Power Optimization Problem

    Directory of Open Access Journals (Sweden)

    K. Lenin

    2013-03-01

    Full Text Available Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents Attractive and repulsive Particle Swarm Optimization (ARPSO and Random Virus Algorithm (RVA in trying to overcome the Problem of premature convergence. RVA and ARPSO is applied to Reactive Power Optimization problem and is evaluated on standard IEEE 30Bus System. The results show that RVA prevents premature convergence to high degree but still keeps a rapid convergence. It gives best solution when compared to Attractive and repulsive Particle Swarm Optimization (ARPSO and Particle Swarm Optimization (PSO.

  6. Groundwater level forecasting using an artificial neural network trained with particle swarm optimization.

    Science.gov (United States)

    Tapoglou, E.; Trichakis, I. C.; Dokou, Z.; Karatzas, G. P.

    2012-04-01

    The purpose of this study is to examine the use of particle swarm optimization algorithm in order to train a feed-forward multi-layer artificial neural network, which can simulate hydraulic head change at an observation well. Particle swarm optimization is a relatively new evolutionary algorithm, developed by Eberhart and Kennedy (1995), which is used to find optimal solutions to numerical and quantitative problems. Three different variations of particle swarm optimization algorithm are considered, the classic algorithm with the improvement of inertia weight, PSO-TVAC and GLBest-PSO. The best performance among all the algorithms was achieved by GLBest-PSO, where the distance between the overall best solution found and the best solution of each particle plays a major role in updating each particle's velocity. The algorithm is implemented using field data from the region of Agia, Chania, Greece. The particle swarm optimization algorithm shows an improvement of 9.3% and 18% in training and test errors respectively, compared to the errors of the back propagation algorithm. The trained neural network can predict the hydraulic head change at a well, without being able to predict extreme and transitional phenomena. The maximum divergence from the observed values is 0.35m. When the hydraulic head change is converted into hydraulic head, using the observed hydraulic head of the previous day, the deviations of simulated values from the actual hydraulic head appear comparatively smaller, with an average deviation of 0.041m. The trained neural network was also used for midterm prediction. In this case, the hydraulic head of the first day of the simulation is used together with the hydraulic head change derived from the simulation. The values obtained by this process were smaller than the observed, while the maximum difference is approximately 1m. However, this error, is not accumulated during the two hydrological years of simulation, and the error at the end of the simulation

  7. A feature extraction method of the particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization for Brillouin scattering spectra

    Science.gov (United States)

    Zhang, Yanjun; Zhao, Yu; Fu, Xinghu; Xu, Jinrui

    2016-10-01

    A novel particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization is proposed for extracting the features of Brillouin scattering spectra. Firstly, the adaptive inertia weight parameter of the velocity is introduced to the basic particle swarm algorithm. Based on the current iteration number of particles and the adaptation value, the algorithm can change the weight coefficient and adjust the iteration speed of searching space for particles, so the local optimization ability can be enhanced. Secondly, the logical self-mapping chaotic search is carried out by using the chaos optimization in particle swarm optimization algorithm, which makes the particle swarm optimization algorithm jump out of local optimum. The novel algorithm is compared with finite element analysis-Levenberg Marquardt algorithm, particle swarm optimization-Levenberg Marquardt algorithm and particle swarm optimization algorithm by changing the linewidth, the signal-to-noise ratio and the linear weight ratio of Brillouin scattering spectra. Then the algorithm is applied to the feature extraction of Brillouin scattering spectra in different temperatures. The simulation analysis and experimental results show that this algorithm has a high fitting degree and small Brillouin frequency shift error for different linewidth, SNR and linear weight ratio. Therefore, this algorithm can be applied to the distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can effectively improve the accuracy of Brillouin frequency shift extraction.

  8. A Bayesian interpretation of the particle swarm optimization and its kernel extension.

    Science.gov (United States)

    Andras, Peter

    2012-01-01

    Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved-such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases.

  9. 基于微粒群优化算法的计划评审技术改进%Improved program evaluate review technique based on particle swarm optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    汪若洋; 熊选东; 张亮忠; 王松锋

    2012-01-01

    Concerning the lack of directional instructions and quantitative standardization in the Program Evaluation Review Technique (PERT) that is commonly used in the progress of project management, an improved method of PERT based on Particle Swarm Optimization (PSO) was proposed through the introduction of PSO. The method used the PERT method of determining the process time on the project and combined the ideas and principles of the standard PSO algorithm to improve the traditional PERT. The simulation results show that the improved PERT gives a more optimized and more accurate quantitative control standards on the project process, can get better controlling and regulating ability to the whole project process compared with the traditional PERT.%针对项目进度管理中常用的计划评审技术(PERT)缺乏方向性指示和定量标准化的不足,通过引入微粒群优化(PSO)算法,提出了一种基于PSO对PERT的改进方法.该方法利用PERT对项目工序时间确定的方法,结合标准PSO算法的思想及原理,对传统PERT进行了改进.仿真实验结果表明,改进后的PERT对项目的过程和整体给出了一个更为优化、更为准确的定量控制标准,比传统的PERT对整个项目过程的控制和调节能力更加突出和有效.

  10. Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction

    Directory of Open Access Journals (Sweden)

    Chao-Hong Chen

    2011-01-01

    Full Text Available We analyze the convergence time of particle swarm optimization (PSO on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.

  11. Application of particle swarm optimization blind source separation technology in fault diagnosis of gearbox

    Institute of Scientific and Technical Information of China (English)

    黄晋英; 潘宏侠; 毕世华; 杨喜旺

    2008-01-01

    Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.

  12. Welding Diagnostics by Means of Particle Swarm Optimization and Feature Selection

    Directory of Open Access Journals (Sweden)

    J. Mirapeix

    2012-01-01

    Full Text Available In a previous contribution, a welding diagnostics approach based on plasma optical spectroscopy was presented. It consisted of the employment of optimization algorithms and synthetic spectra to obtain the participation profiles of the species participating in the plasma. A modification of the model is discussed here: on the one hand the controlled random search algorithm has been substituted by a particle swarm optimization implementation. On the other hand a feature selection stage has been included to determine those spectral windows where the optimization process will take place. Both experimental and field tests will be shown to illustrate the performance of the solution that improves the results of the previous work.

  13. Blind Decorrelating Detection Based on Particle Swarm Optimization under Spreading Code Mismatch

    Institute of Scientific and Technical Information of China (English)

    Jhih-Chung Chang; Chih-Chang Shen

    2014-01-01

    A way of resolving spreading code mismatches in blind multiuser detection with a particle swarm optimization (PSO) approach is proposed. It has been shown that the PSO algorithm incorporating the linear system of the decorrelating detector, which is termed as decorrelating PSO (DPSO), can significantly improve the bit error rate (BER) and the system capacity. As the code mismatch occurs, the output BER performance is vulnerable to degradation for DPSO. With a blind decorrelating scheme, the proposed blind DPSO (BDPSO) offers more robust capabilities over existing DPSO under code mismatch scenarios.

  14. Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Zhu Hongqiu; Yang Chunhua; Gui Weihua

    2009-01-01

    To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning (CBR) with adaptive particle swarm optimization (PSO). The number of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.

  15. Modified Particle Swarm Optimization for Blind Deconvolution and Identification of Multichannel FIR Filters

    Directory of Open Access Journals (Sweden)

    Khanagha Ali

    2010-01-01

    Full Text Available Blind identification of MIMO FIR systems has widely received attentions in various fields of wireless data communications. Here, we use Particle Swarm Optimization (PSO as the update mechanism of the well-known inverse filtering approach and we show its good performance compared to original method. Specially, the proposed method is shown to be more robust against lower SNR scenarios or in cases with smaller lengths of available data records. Also, a modified version of PSO is presented which further improves the robustness and preciseness of PSO algorithm. However the most important promise of the modified version is its drastically faster convergence compared to standard implementation of PSO.

  16. Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Oussama Ait Sahed

    2015-01-01

    Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

  17. The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm

    Science.gov (United States)

    Zhao, Xinying; Hu, Yihua; Gu, Youlin; Li, Le

    2015-10-01

    Microorganism aggregated particle swarm, which is quite an important composition of complex media environment, can be developed as a new kind of infrared functional materials. Current researches mainly focus on the optical properties of single microorganism particle. As for the swarm, especially the microorganism aggregated particle swarm, a more accurate simulation model should be proposed to calculate its extinction effect. At the same time, certain parameters deserve to be discussed, which helps to better develop the microorganism aggregated particle swarm as a new kind of infrared functional materials. In this paper, take Aspergillus Niger spore as an example. On the one hand, a new calculation model is established. Firstly, the cluster-cluster aggregation (CCA) model is used to simulate the structure of Aspergillus Niger spore aggregated particle. Secondly, the single scattering extinction parameters for Aspergillus Niger spore aggregated particle are calculated by using the discrete dipole approximation (DDA) method. Thirdly, the transmittance of Aspergillus Niger spore aggregated particle swarm is simulated by using Monte Carlo method. On the other hand, based on the model proposed above, what influences can wavelength causes has been studied, including the spectral distribution of scattering intensity of Aspergillus Niger spore aggregated particle and the infrared spectral transmittance of the aggregated particle swarm within the range of 8~14μm incident infrared wavelengths. Numerical results indicate that the scattering intensity of Aspergillus Niger spore aggregated particle reduces with the increase of incident wavelengths at each scattering angle. Scattering energy mainly concentrates on the scattering angle between 0~40°, forward scattering has an obvious effect. In addition, the infrared transmittance of Aspergillus Niger spore aggregated particle swarm goes up with the increase of incident wavelengths. However, some turning points of the trend

  18. Discrete ternary particle swarm optimization for area optimization of MPRM circuits

    Institute of Scientific and Technical Information of China (English)

    Yu Haizhen; Wang Pengjun; Wang Disheng; Zhang Huihong

    2013-01-01

    Having the advantage of simplicity,robustness and low computational costs,the particle swarm optimization (PSO) algorithm is a powerful evolutionary computation tool for synthesis and optimization of ReedMuller logic based circuits.Exploring discrete PSO and probabilistic transition rules,the discrete ternary particle swarm optimization (DTPSO) is proposed for mixed polarity Reed-Muller (MPRM) circuits.According to the characteristics of mixed polarity OR/XNOR expression,a tabular technique is improved,and it is applied in the polarity conversion of MPRM functions.DTPSO is introduced to search the best polarity for an area of MPRM circuits by building parameter mapping relationships between particles and polarities.The computational results show that the proposed DTPSO outperforms the reported method using maxterm conversion starting from POS Boolean functions.The average saving in the number of terms is about 11.5%; the algorithm is quite efficient in terms of CPU time and achieves 12.2% improvement on average.

  19. The Improved Research on Particle Swarm Optimization with Adaptive Variation%带自适应变异的粒子群优化算法改进研究

    Institute of Scientific and Technical Information of China (English)

    冯浩; 李现伟

    2015-01-01

    针对函数优化的非线性特点,在标准粒子群优化算法的基础上,提出了一种带自适应变异的粒子群优化算法。该算法对惯性权值进行参数设计,建立非线性递减策略曲线模型,通过设置校准系数,改变惯性权值的曲线变化率,使其随迭代过程进行自适应变化。通过在迭代初期选取较大的惯性权值,增强算法的局部寻优能力,加快算法收敛速度,而在迭代后期选取较小的惯性权值,提升算法的全局搜索性能。同时,在算法中引入变异机制,增加种群的多样性,从而更好地提升算法由局部到全局的开放式搜索能力。通过选择基准测试函数对几种算法进行性能测试,证明改进算法收敛速度快、精度高,总体性能优于对比算法。%Based on the nonlinear feature of function optimization , this paper proposes a particle swarm opti-mization algorithm with adaptive variation which is on the basis of the standard particle swarm optimization algo -rithm.The algorithm designs the parameter of inertia weight , set up the nonlinear decreasing curve model , and al-ters the curve of the inertia weight rate by setting the calibration coefficient , and makes it changing adaptively with the iterative process .The algorithm can enhance local optimization ability and accelerate the convergence speed through selecting large inertia weight at the beginning of iteration , and improve global searching performance by se-lecting low inertia weight in the late iteration .At the same time , variation mechanism is introduced into this algo-rithm to increase the diversity of population , and improved the ability of the open search performance optimized from the partial to the whole The advantage of fast convergence and high precision in the improved algorithm has been proved by selecting benchmark functions and testing several comparing algorithms .The improved algorithm outperforms the compared

  20. Extraction of Leukocyte Areas Based on Improved ITTI and Particle Swarm Optimization Algorithm%基于改进ITTI模型及粒子群优化算法的白细胞区域提取

    Institute of Scientific and Technical Information of China (English)

    纪滨; 杨盼盼; 申元霞

    2016-01-01

    白细胞显微图像病理分析中,人眼关注的白细胞是感兴趣的区域。ITTI视觉模型是提取图像感兴趣区域(ROI)的有效办法。为了进一步改善其提取的准确性,提出了基于改进的ITTI视觉模型与粒子群优化算法相结合的目标控制方法,并将其应用于医学骨髓细胞图像中的白细胞区域提取。首先利用高斯滤波和多尺度归一化的方法分别提取原始图像的方向、亮度、颜色显著性特征,再根据人眼的视觉对不同显著性特征敏感程度不同的特性对3种显著性特征采用自适应系数相融合的方式得到显著图,最后利用基于改进的粒子群优化算法的Otsu法对显著图进行ROI的提取,并采用数字形态学的方法对其进行后续处理。结果表明,本文算法可以较好地提取完整的白细胞区域,有助于提高病理分析的效率。%In the process of pathological analysis of the Leukocyte microscopic image, the leukocyte areas are regions of interest(ROI). The ITTI visual model is an effective method of extracting the ROI from image. For improving the extracting accuracy, an object extracting method combining the improved ITTI visual model with particle swarm optimization algorithm is proposed and used to extract the ROI from the bone marrow cell image. Firstly, based on Gaussian filter and multi-scale normalization, features of the orientation, brightness, and color are computed from the original image. And then, according the fact that the sensitivity of eyes is not the same as different features the saliency map is obtained with adaptive coefficient from three significant characteristics. Finally, by using Otsu method based on the improved particle swarm optimization(PSO) algorithm, the ROIs are extracted and subsequent processed with method of morphology. Experimental results show that this method can extract white blood cell areas perfectly, which is helpful for improving the efficiency of

  1. Minimal K-Covering Set Algorithm based on Particle Swarm Optimizer

    National Research Council Canada - National Science Library

    Yong Hu

    2013-01-01

    .... In order to maximize the cost savings network resources for wireless sensor networks, extend the life network, this paper proposed a algorithm for the minimal k-covering set based on particle swarm optimizer...

  2. Multi-objective fuzzy particle swarm optimization based on elite archiving and its convergence

    Institute of Scientific and Technical Information of China (English)

    Wei Jingxuan; Wang Yuping

    2008-01-01

    A fuzzy particle swarm optimization (PSO) on the basis of elite archiving is proposed for solving multi-objective optimization problems.First,a new perturbation operator is designed,and the concepts of fuzzy global best and fuzzy personal best are given on basis of the new operator.After that,particle updating equations are revised on the basis of the two new concepts to discourage the premature convergence and enlarge the potential search space; second,the elite archiving technique is used during the process of evolution,namely,the elite particles are introduced into the swarm,whereas the inferior particles are deleted.Therefore,the quality of the swarm is ensured.Finally,the convergence of this swarm is proved.The experimental results show that the nondominated solutions found by the proposed algorithm are uniformly distributed and widely spread along the Pareto front.

  3. Application of Particle Swarm Optimization Algorithm in Design of Multilayered Planar Shielding Body

    Institute of Scientific and Technical Information of China (English)

    FUJiwei; HOUChaozhen; DOULihua

    2005-01-01

    Based on the basic electromagnetic wave propagation theory in this article, the Particle swarm optimization algorithm (PSO) is used in the design of the multilayered composite materials and the thickness of shielding body by the existent multilayered planar composite elec-tromagnetic shielding materials model, the different shielding materials of each layer can be designed under some kinds of circumstances: the prespecified Shielding effectiveness (SE), different incident angle and the prespecified band of frequencies. Finally the algorithm is simulated. At the same time the similar procedure can be implemented by Genetic algorithm (GA). The results acquired by particle swarm optimization algorithm are compared with there sults acquired by the genetic algorithm. The results indicate that: the particle swarm optimization algorithm is much better than the genetic algorithm not only in convergence speed but also in simplicity. So a more effective method (Particle Swarm Optimization algorithm) is offered for the design of the multilayered composite shielding materials.

  4. Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

    Institute of Scientific and Technical Information of China (English)

    TANG Xian-lun; ZHUANG Ling; QIU Guo-qing; CAI Jun

    2009-01-01

    The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.

  5. Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation

    CSIR Research Space (South Africa)

    Greeff, M

    2008-06-01

    Full Text Available Many optimisation problems are multi-objective and change dynamically. Many methods use a weighted average approach to the multiple objectives. This paper introduces the usage of the vector evaluated particle swarm optimiser (VEPSO) to solve dynamic...

  6. Optimal design of filter with double-layer EBG structure based on improved particle swarm optimization%一种双层EBG结构滤波器的粒子群优化设计

    Institute of Scientific and Technical Information of China (English)

    孟非; 沙莎; 解志斌; 彭涛

    2012-01-01

    A kind of new dual-plane electromagnetic band gap (EBG) structure is studied in this paper. The filter has metal conductor line etched bow-tie cells and metal ground etched cirques. In order to improve the transmission performance of the filter, particle swarm optimization algorithm, which is improve based on quantum theory , differential evolution operator and chaos disturbance, and the HFSS software, which has great electromagnetic simulating function, are integrated to optimize the size of the cirques automatically. The simulation results show that the relative bandwidth at - l0dB and the attenuation of stopband are increased 25. 0% and 15. 4% respectively, and the maximum passband ripples on the left and right sides are reduced by 69. 2% and 79. 8%. The optimal result is excellent.%研究了一种基于双层电磁带隙结构的滤波器,该滤波器的金属导带上刻蚀蝶形单元,接地板上刻蚀圆环孔.为使该滤波器的传输特性更好,运用了基于量子理论、微分进化算子、混沌扰动改进的粒子群优化算法,并结合电磁仿真软件HFSS对接地板圆环孔的结构尺寸进行了自动优化设计,优化后-10 dB的相对带宽和阻带的衰减值分别增加了25.0%和15.4%,左右最大通带波纹分别减小了69.2%和79.8%,优化效果明显.

  7. Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

    Directory of Open Access Journals (Sweden)

    Li Mao

    2016-01-01

    Full Text Available Artificial bee colony (ABC algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

  8. Robust design of broadband EUV multilayer beam splitters based on particle swarm optimization

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Hui, E-mail: jianghui@sinap.ac.cn [Shanghai Synchrotron Radiation Facility, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Zhangheng Road 239, Pudong District, Shanghai 201204 (China); King' s College London, Department of Physics, Strand, London WC2R 2LS (United Kingdom); Michette, Alan G. [King' s College London, Department of Physics, Strand, London WC2R 2LS (United Kingdom)

    2013-03-01

    A robust design idea for broadband EUV multilayer beam splitters is introduced that achieves the aim of decreasing the influence of layer thickness errors on optical performances. Such beam splitters can be used in interferometry to determine the quality of EUVL masks by comparing with a reference multilayer. In the optimization, particle swarm techniques were used for the first time in such designs. Compared to conventional genetic algorithms, particle swarm optimization has stronger ergodicity, simpler processing and faster convergence.

  9. 基于改进K-means聚类和量子粒子群算法的多航迹规划%Multiple Route Planning Based on Improved K-means Clustering and Quantum-behaved Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    董阳; 王瑾; 柏鹏

    2014-01-01

    针对在复杂环境下需要通过多航迹规划以实现武器协同的问题,利用排挤机制产生 K-means聚类的初始聚类中心,并将改进K-means聚类与量子粒子群算法( QPSO)相结合应用于无人机的三维多航迹规划。改进算法解决了K-means聚类易陷入局部最优、聚类准确率低的问题。根据产生的初始聚类中心,将粒子划分成多个子种群,利用QPSO算法对每个子种群进行优化,使得每个子种群可以产生一条可行航迹。仿真分析证明了改进算法可以有效保证子种群之间的多样性,生成较为分散的多条可行航迹。%For the problem of multiple routes planning to realize the weapon cooperation in complex envi-ronment,K-means clustering is improved by an exclusion mechanism which generates the initial cluster centers. A method combining quantum-behaved particle swarm optimization( QPSO) with K-means cluste-ring is proposed and applied to 3-D multiple routes planning of unmanned aerial vehicle( UAV) . The im-proved algorithm solves the problem of falling in local best and improves the clustering accuracy. It classi-fies the particles to several subgroups. Then every subgroup is optimized by QPSO so as to generate a feasi-ble route. Finally,multiple and dispersive routes are constituted. Simulation proves that the improved algo-rithm can assure the variety of subgroups and generates feasible and diverse routes.

  10. 基于粒子群算法的模糊层次分析法改进及其应用研究%Improvement of Fuzzy Analytic Hierarchy Process Based on Particle Swarm Optimization and Its Application Research

    Institute of Scientific and Technical Information of China (English)

    李壮阔; 薛有添

    2013-01-01

    提出了基于粒子群算法( Particle Swarm Optimization )的模糊层次分析法( Fuzzy Analytic Hierarchy Process)模型,简称PSO-FAHP,将模糊判断矩阵一致性的检验、修正及各元素权重值的计算过程转化成非线性带约束系统优化问题,并利用粒子群算法实现了该问题的求解。构建了制造企业供应商选择评价指标体系,最后将该模型应用于制造企业供应商选择评价,结果表明,此模型能有效帮助企业选择较佳的供应商,对供应链构建有一定的指导意义。%This paper proposed a model of fuzzy analytic hierarchy process ( FAHP) based on particle swarm opti-mization( PSO) called PSO-FAHP.It transformed the consistency modified and elements ranking alternative of fuzzy judgement matrix into an optimization problem for nonlinear constrained system , and made use of particle swarm optimization to solve it .Finally, it built a evaluation index system for manufacturing enterprises ’ vendors selection and applied the PSO-FAHP model to manufacturing enterprises ’ venders selection and evaluation .The results show that this model can effectively help companies choose better venders and have certain guiding signifi -cance for building the supply chain .

  11. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection.

    Science.gov (United States)

    Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo

    2012-01-01

    Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods.

  12. APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOP SCHEDULING PROBLEM

    Institute of Scientific and Technical Information of China (English)

    Xia Weijun; Wu Zhiming; Zhang Wei; Yang Genke

    2004-01-01

    A new heuristic algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling problem. The new algorithm is based on the principles of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, is developed. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems and comparing results with other algorithms in literature. Comparing results indicate that PSO-based algorithm is a viable and effective approach for the job-shop scheduling problem.

  13. Chaotic Inertia Weight Particle Swarm Optimization for PCR Primer Design

    Directory of Open Access Journals (Sweden)

    Cheng-Huei Yang

    2013-06-01

    Full Text Available In order to provide feasible primer sets for performing a polymerase chain reaction (PCR experiment, many primer design methods have been proposed. However, the majority of these methods require a long time to obtain an optimal solution since large quantities of template DNA need to be analyzed, and the designed primer sets usually do not provide a specific PCR product size. In recent years, particle swarm optimization (PSO has been applied to solve many problems and yielded good results. In this paper, a logistic map is proposed to determine the value of inertia weight of PSO (CIWPSO to design feasible primers. Accuracies for the primer design of the Homo sapiens RNA binding motif protein 11 (RBM11, mRNA (NM_144770, and the Homo sapiens G protein-coupled receptor 78 (GPR78, mRNA (NM_080819 were calculated. Five hundred runs of PSO and the CIWPSO primer design method were performed on different PCR product lengths and the different methods of calculating the melting temperature. A comparison of the accuracy results for PSO and CIWPSO primer design showed that CIWPSO is superior to the PSO for primer design. The proposed method could effectively find optimal or near-optimal primer sets.

  14. Multiple objective particle swarm optimization technique for economic load dispatch

    Institute of Scientific and Technical Information of China (English)

    ZHAO Bo; CAO Yi-jia

    2005-01-01

    A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call "repository") and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

  15. Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Xiyang Liu

    2016-01-01

    Full Text Available Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

  16. A New Particle Swarm Optimization Based Stock Market Prediction Technique

    Directory of Open Access Journals (Sweden)

    Essam El. Seidy

    2016-04-01

    Full Text Available Over the last years, the average person's interest in the stock market has grown dramatically. This demand has doubled with the advancement of technology that has opened in the International stock market, so that nowadays anybody can own stocks, and use many types of software to perform the aspired profit with minimum risk. Consequently, the analysis and prediction of future values and trends of the financial markets have got more attention, and due to large applications in different business transactions, stock market prediction has become a critical topic of research. In this paper, our earlier presented particle swarm optimization with center of mass technique (PSOCoM is applied to the task of training an adaptive linear combiner to form a new stock market prediction model. This prediction model is used with some common indicators to maximize the return and minimize the risk for the stock market. The experimental results show that the proposed technique is superior than the other PSO based models according to the prediction accuracy.

  17. Optimasi Desain Heat Exchanger dengan Menggunakan Metode Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Rifnaldi Veriyawan

    2014-09-01

    Full Text Available Industri proses terutama perminyakan adalah salah satu industri membutuhkan energi panas dengan jumlah kapasitas besar. Dengan berjalan perkembangan teknologi dibutuhkannya proses perpindahan panas dalam jumlah besar. Tetapi dengan besarnya penukaran panas yang diberikan maka besar pula luas permukaan. Dibutuhkannya optimasi pada desain heat exchanger terutama shell-and-tube¬. Dalam tugas akhir ini, Algoritma particle swarm optimization (PSO digunakan untuk mengoptimasikan nilai koefesien perpindahan panas keseluruhan dengan mendapatkan nilai terbaik. Perumusan fungsi tujuan nilai perpindahan panas keseluruhan (U, dan luas permukaan (A yang digunakan untuk mencari nilai fungsi objektif pada PSO. Partikel dalam PSO menyatakan sebagai posisi atau solusi dari hasil optimasi didapatnya nilai perpindahan panas maksimal dengan luas permukaan dan pressure drop dibawah data desain atau datasheet. Partikel tersebut dalam pemodelan berupa rentang nilai minimal dan maksimal dari diameter luar diantara (do dan jumlah baffle (Nb. Dari hasil optimasi pada tiga HE didapatkan nilai U dan A secara berturut-turut; HE E-1111 472 W/m2C dan 289 m2 ;pada HE E-1107 174 W/m2C dan 265 m2 ; dan HE E-1102 618 W/m2C dan 574 m2. Nilai perpindahan panas keseluruhan yang telah dioptimasi sesuai dengan fungsi objektif dapat dikatakan HE shell-and-tube mencapai titik optimal.

  18. Particle swarm optimization with scale-free interactions.

    Directory of Open Access Journals (Sweden)

    Chen Liu

    Full Text Available The particle swarm optimization (PSO algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks.

  19. Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm

    Science.gov (United States)

    De, Bishnu P.; Kar, Rajib; Mandal, Durbadal; Ghoshal, Sakti P.

    2015-07-01

    The inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds' flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.

  20. Optimization design for locating and sizing of distributed generation based on improved particle swarm algorithm%基于改进粒子群算法的分布式电源选址定容优化设计

    Institute of Scientific and Technical Information of China (English)

    王秀丽; 赵兴勇; 曹建文; 袁晓明

    2014-01-01

    Based on the analysis of the impact of the access of the distributed generation on the power distribution network,the locating and sizing design of the distributed generation,a major issue of the power distribution network is optimized.By setting up the obj ective function containing the costs of investment and operation,environmental factors and the cost of network loss,and considering the constraints of tide current,current,voltage and system capacity,a model of distributed generation is established,and using an improved particle swarm algorithm, a system to locate and size the distributed generation is designed.Through the simulation analysis and calculation,the accuracy and validity of this design are verified.%在分析了分布式电源接入对配电网影响的基础上,针对配电网中的关键问题---分布式电源的选址和定容,进行了优化设计。通过构建包含分布式电源的投资运行费用、环境因素、网损费用的目标函数,并考虑潮流约束、电流约束、电压约束、系统容量约束等条件,搭建了分布式电源的模型,采用一种改进的粒子群算法,设计出了分布式电源所处位置及容量。通过仿真分析计算,验证了该方案的正确性和有效性。

  1. Hybrid particle swarm optimization with Cauchy distribution for solving reentrant flexible flow shop with blocking constraint

    Directory of Open Access Journals (Sweden)

    Chatnugrob Sangsawang

    2016-06-01

    Full Text Available This paper addresses a problem of the two-stage flexible flow shop with reentrant and blocking constraints in Hard Disk Drive Manufacturing. This problem can be formulated as a deterministic FFS|stage=2,rcrc, block|Cmax problem. In this study, adaptive Hybrid Particle Swarm Optimization with Cauchy distribution (HPSO was developed to solve the problem. The objective of this research is to find the sequences in order to minimize the makespan. To show their performances, computational experiments were performed on a number of test problems and the results are reported. Experimental results show that the proposed algorithms give better solutions than the classical Particle Swarm Optimization (PSO for all test problems. Additionally, the relative improvement (RI of the makespan solutions obtained by the proposed algorithms with respect to those of the current practice is performed in order to measure the quality of the makespan solutions generated by the proposed algorithms. The RI results show that the HPSO algorithm can improve the makespan solution by averages of 14.78%.

  2. Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-07-01

    Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

  3. OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

    Directory of Open Access Journals (Sweden)

    Dhananjay Kumar

    2016-01-01

    Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

  4. Optimization of hydrofoil for tidal current turbine based on particle swarm optimization and computational fluid dynamic method

    OpenAIRE

    Zhang De-Sheng; Chen Jian; Shi Wei-Dong; Shi Lei; Geng Lin-Lin

    2016-01-01

    Both efficiency and cavitation performance of the hydrofoil are the key technologies to design the tidal current turbine. In this paper, the hydrofoil efficiency and lift coefficient were improved based on particle swarm optimization method and XFoil codes. The cavitation performance of the optimized hydrofoil was also discussed by the computational fluid dynamic. Numerical results show the efficiency of the optimized hydrofoil was improved 11% ranging from...

  5. Optimization of testing surveillance and maintenance by Particle Swarm; Optimizacion de las pruebas de vigilancia y el mantenimiento mediante particle swarm

    Energy Technology Data Exchange (ETDEWEB)

    Carlos, S.; Sanchez, A.; Martorell, S.; Villamizar, M.

    2010-07-01

    This paper presents the optimization of the testing surveillance and maintenance (TS and M) plan for high-pressure injection system (HPIS) in a nuclear power plant. Using the Particle Swarm Optimization (PSO) is obtained a group of viable solutions, each one corresponding to a non-dominated solution, which can be implemented in the plant.

  6. Application of particle swarm optimization in path planning of mobile robot

    Science.gov (United States)

    Wang, Yong; Cai, Feng; Wang, Ying

    2017-08-01

    In order to realize the optimal path planning of mobile robot in unknown environment, a particle swarm optimization algorithm based on path length as fitness function is proposed. The location of the global optimal particle is determined by the minimum fitness value, and the robot moves along the points of the optimal particles to the target position. The process of moving to the target point is done with MATLAB R2014a. Compared with the standard particle swarm optimization algorithm, the simulation results show that this method can effectively avoid all obstacles and get the optimal path.

  7. A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

    Directory of Open Access Journals (Sweden)

    Qi Hu

    2013-04-01

    Full Text Available State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data’s real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.

  8. Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chenzhong Cao

    2009-07-01

    Full Text Available Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT and murine local lymph node assay (LLNA are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.

  9. Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification.

    Science.gov (United States)

    Wei, Hua-Liang; Billings, Stephen A; Zhao, Yifan; Guo, Lingzhong

    2009-01-01

    In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.

  10. Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks.

    Science.gov (United States)

    Robinson, Y Harold; Rajaram, M

    2015-01-01

    Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.

  11. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Asrul Adam

    2014-01-01

    Full Text Available Electroencephalogram (EEG signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1 standard PSO and (2 random asynchronous particle swarm optimization (RA-PSO. The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

  12. Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Y. Harold Robinson

    2015-01-01

    Full Text Available Mobile ad hoc network (MANET is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO that uses continuous time recurrent neural network (CTRNN to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.

  13. Advanced Adaptive Particle Swarm Optimization based SVC Controller for Power System Stability

    Directory of Open Access Journals (Sweden)

    Poonam Singhal

    2014-12-01

    Full Text Available The interconnected systems is continually increasing in size and extending over whole geographical regions, it is becoming increasingly more difficult to maintain synchronism between various parts of the power system. This paper work presents an advanced adaptive Particle swarm optimization technique to optimize the SVC controller parameters for enhancement of the steady state stability & overcoming the premature convergence & stagnation problems as in basic PSO algorithm & Particle swarm optimization with shrinkage factor & inertia weight approach (PSO-SFIWA. In this paper SMIB system along with PID damped SVC controller is considered for study. The generator speed deviation is used as an auxiliary signal to SVC, to generate the desired damping. This controller improves the dynamic performance of power system by reducing the steady-state error. The controller parameters are optimized using basic PSO, PSO-SFIWA & Advanced Adaptive PSO. Computational results show that Advanced Adaptive based SVC controller is able to find better quality solution as compare to conventional PSO & PSO-SFIWA Techniques.

  14. Intelligent particle swarm optimized fuzzy PID controller for AVR system

    Energy Technology Data Exchange (ETDEWEB)

    Mukherjee, V. [Department of Electrical Engineering, Asansol Engineering College, Asansol, West Bengal (India); Ghoshal, S.P. [Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal (India)

    2007-10-15

    In process plants like thermal power plants, biomedical instrumentation the popular use of proportional-integral-derivative (PID) controllers can be noted. Proper tuning of such controllers is obviously a prime priority as any other alternative situation will require a high degree of industrial expertise. So in order to get the best results of PID controllers the optimal tuning of PID gains is required. This paper, thus, deals with the determination of off-line, nominal, optimal PID gains of a PID controller of an automatic voltage regulator (AVR) for nominal system parameters and step reference voltage input. Craziness based particle swarm optimization (CRPSO) and binary coded genetic algorithm (GA) are the two props used to get the optimal PID gains. CRPSO proves to be more robust than GA in performing optimal transient performance even under various nominal operating conditions. Computational time required by CRPSO is lesser than that of GA. Factors that have influenced the enhancement of global searching ability of PSO are the incorporation of systematic and intelligent velocity, position updating procedure and introduction of craziness. This modified from of PSO is termed as CRPSO. For on-line off-nominal system parameters Sugeno fuzzy logic (SFL) is applied to get on-line terminal voltage response. The work of SFL is to extrapolate intelligently and linearly, the nominal optimal gains in order to determine off-nominal optimal gains. The on-line computational burden of SFL is noticeably low. Consequently, on-line optimized transient response of incremental change in terminal voltage is obtained. (author)

  15. Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization

    Science.gov (United States)

    Birge, Brian

    2013-01-01

    The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.

  16. A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows.

    Science.gov (United States)

    Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian

    2015-08-27

    A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following.

  17. Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization

    CERN Document Server

    Zhang, Jianlei; Chu, Tianguang; Perc, Matjaz; 10.1371/journal.pone.0021787

    2011-01-01

    We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available stra...

  18. Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

    Science.gov (United States)

    Yu, Xiang; Zhang, Xueqing

    2017-01-01

    Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

  19. 基于改进粒子群算法的参数化机翼气动优化%Aerodynamic Optimization for Parameterized Wing Based on Improved Particle-Swarm-Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    金鑫; 孙刚

    2012-01-01

    Aerodynamic drag reduction design is the key to the design of civil aircraft. To solve the drag reduction problem of wing a new method was proposed based on non-uniform B-spline modeling technology and an improved particle swarm optimization(PSO) algorithm. The former was used to describe the wing shapes with small amount of calculation: it not only had good local control of shape, but also ensured the overall appearance of smoothness; the latter, as a new intelligent optimization method, had fast convergence ability and global search ability for multi-objective optimization problems. The results showed that cubic non-uniform B-spline curves and bi-cubic non-uniform B-spline surface could describe the airfoil and wing shapes more accurately with fewer control points, and the efficiency of multi-objective aerodynamic optimization had been improved. Even for the airfoil and wing with high efficiency factor, aerodynamic performance also made a further increase.%机翼减阻设计是民用客机气动设计的关键,本文提出了一种基于非均匀B样条曲线曲面造型技术和改进的粒子群算法的新型优化方法.前者用来描述机翼的外形,具有计算量小的优点,在优化过程中不仅具有良好的局部操控性,又能保证整体外形的光顺性;后者作为一种新兴的智能化优化方法,具有简单易行、收敛速度快、全局搜索能力强等优点,同时又适用于多目标优化问题.研究结果表明:三次非均匀B样条曲线曲面能够方便地使用较少的控制顶点较为精确地描述翼型及机翼的外形,在此基础上利用改进的粒子群算法进行的多目标气动优化设计,优化效率得到了提升.在效率因子本身较高的初始外形基础上,最终外形的气动性能也取得了较大幅度的提高.

  20. Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

    Directory of Open Access Journals (Sweden)

    Wang Chun-Feng

    2014-01-01

    Full Text Available Artificial bee colony (ABC algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.

  1. Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach

    Science.gov (United States)

    Yan, Danping; Lu, Yongzhong; Levy, David

    2015-01-01

    Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles’ local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators. PMID:26039090

  2. Purely hydrodynamic origin for swarming of swimming particles

    Science.gov (United States)

    Oyama, Norihiro; Molina, John Jairo; Yamamoto, Ryoichi

    2016-04-01

    Three-dimensional simulations with fully resolved hydrodynamics are performed to study the collective motion of model swimmers in bulk and confinement. Calculating the dynamic structure factor, we clarified that the swarming in bulk systems can be understood as a pseudoacoustic mode. Under confinement between flat parallel walls, this pseudoacoustic mode leads to a traveling wavelike motion. This swarming behavior is due purely to the hydrodynamic interactions between the swimmers and depends strongly on the type and strength of swimming (i.e., pusher or puller).

  3. hydroPSO: A Versatile Particle Swarm Optimisation R Package for Calibration of Environmental Models

    Science.gov (United States)

    Zambrano-Bigiarini, M.; Rojas, R.

    2012-04-01

    Particle Swarm Optimisation (PSO) is a recent and powerful population-based stochastic optimisation technique inspired by social behaviour of bird flocking, which shares similarities with other evolutionary techniques such as Genetic Algorithms (GA). In PSO, however, each individual of the population, known as particle in PSO terminology, adjusts its flying trajectory on the multi-dimensional search-space according to its own experience (best-known personal position) and the one of its neighbours in the swarm (best-known local position). PSO has recently received a surge of attention given its flexibility, ease of programming, low memory and CPU requirements, and efficiency. Despite these advantages, PSO may still get trapped into sub-optimal solutions, suffer from swarm explosion or premature convergence. Thus, the development of enhancements to the "canonical" PSO is an active area of research. To date, several modifications to the canonical PSO have been proposed in the literature, resulting into a large and dispersed collection of codes and algorithms which might well be used for similar if not identical purposes. In this work we present hydroPSO, a platform-independent R package implementing several enhancements to the canonical PSO that we consider of utmost importance to bring this technique to the attention of a broader community of scientists and practitioners. hydroPSO is model-independent, allowing the user to interface any model code with the calibration engine without having to invest considerable effort in customizing PSO to a new calibration problem. Some of the controlling options to fine-tune hydroPSO are: four alternative topologies, several types of inertia weight, time-variant acceleration coefficients, time-variant maximum velocity, regrouping of particles when premature convergence is detected, different types of boundary conditions and many others. Additionally, hydroPSO implements recent PSO variants such as: Improved Particle Swarm

  4. Infrared face recognition based on binary particle swarm optimization and SVM-wrapper model

    Science.gov (United States)

    Xie, Zhihua; Liu, Guodong

    2015-10-01

    Infrared facial imaging, being light- independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. Robust feature selection and representation is a key issue for infrared face recognition research. This paper proposes a novel infrared face recognition method based on local binary pattern (LBP). LBP can improve the robust of infrared face recognition under different environment situations. How to make full use of the discriminant ability in LBP patterns is an important problem. A search algorithm combination binary particle swarm with SVM is used to find out the best discriminative subset in LBP features. Experimental results show that the proposed method outperforms traditional LBP based infrared face recognition methods. It can significantly improve the recognition performance of infrared face recognition.

  5. Particle swarm optimization for optimal sensor placement in ultrasonic SHM systems

    Science.gov (United States)

    Blanloeuil, Philippe; Nurhazli, Nur A. E.; Veidt, Martin

    2016-04-01

    A Particle Swarm Optimization (PSO) algorithm is used to improve sensors placement in an ultrasonic Structural Health Monitoring (SHM) system where the detection is performed through the beam-forming imaging algorithm. The imaging algorithm reconstructs the defect image and estimates its location based on analytically generated signals, considering circular through hole damage in an aluminum plate as the tested structure. Then, the PSO algorithm changes the position of sensors to improve the accuracy of the detection. Thus, the two algorithms are working together iteratively to optimize the system configuration, taking into account a complete modeling of the SHM system. It is shown that this approach can provide good sensors placements for detection of multiple defects in the target area, and for different numbers of sensors.

  6. Computing of network tenacity based on modified binary particle swarm optimization algorithm

    Science.gov (United States)

    Shen, Maoxing; Sun, Chengyu

    2017-05-01

    For rapid calculation of network node tenacity, which can depict the invulnerability performance of network, this paper designs a computational method based on modified binary particle swarm optimization (BPSO) algorithm. Firstly, to improve the astringency of the BPSO algorithm, the algorithm adopted an improved bit transfer probability function and location updating formula. Secondly, algorithm for fitness function value of BPSO based on the breadth-first search is designed. Thirdly, the computing method for network tenacity based on the modified BPSO algorithm is presented. Results of experiment conducted in the Advanced Research Project Agency (ARPA) network and Tactical Support Communication (TCS) network illustrate that the computing method is impactful and high-performance to calculate network tenacity.

  7. Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Supriya Dhabal

    2014-01-01

    Full Text Available We present a novel hybrid algorithm based on particle swarm optimization (PSO and simulated annealing (SA for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters.

  8. Two-Stage Damage Detection Method Based on Improved Particle Swarm Optimization Algorithm%基于改进PSO算法的两阶段损伤识别方法

    Institute of Scientific and Technical Information of China (English)

    郭惠勇; 王磊; 李正良

    2011-01-01

    为解决结构多损伤情况下的位置识别和损伤程度判定问题,提出了一种基于改进粒子群优化算法和贝叶斯理论的两阶段损伤识别方法,该方法采用频率和模态应变能作为损伤定位源数据,分别用基于频率改变和基于应变能耗散率的识别方法进行损伤信息的初步提取,再利用贝叶斯融合理论对损伤位置进行较为精确的判定.然后,利用粒子群优化(PSO)算法对损伤位置和程度进行更为精确的二次识别.考虑到简单PSO算法易陷入局部最优解,提出了3种改进措施,即粒子位置突变、最优记忆粒子微搜索和双收敛措施.数值仿真结果表明:采用贝叶斯融合理论可以有效地识别出可能的损伤单元,在此基础上用改进的PSO算法可以更精确地识别损伤的位置和程度,同时采用3种改进措施的PSO算法的识别精度明显优于其他PSO算法和遗传算法.%In order to solve structural multi-damage identification problem, a two-stage identification method based on the particle swarm optimization(PSO) algorithm and the Bayesian theory was proposed. In this method, structural modal strain energy (MSE) and frequency are considered as two information sources, and the methods based on frequency change and MSE dissipation ratio are utilized to extract damage information. Then, the Bayesian theory is utilized to integrate the two information sources and preliminarily detect structural damage locations. Finally, the PSO algorithm is adopted to precisely identify structural damage locations and extents. In order to improve the identification results of a simple PSO algorithm, three improved strategies, particle position mutation, elitist micro-search and double convergence criterion, were presented. The simulation results for a two-dimensional truss structure show that the Bayesian theory can identify the suspected damage locations, the improved PSO algorithm can precisely detect the damage extent, and

  9. Modeling Photovoltaic-Arrays Based on the RBF Neural Networks Improved by Particle Swarm Optimization Algorithm%粒子群优化RBF神经网络光伏电池建模研究

    Institute of Scientific and Technical Information of China (English)

    张军朝; 陈俊杰

    2011-01-01

    Photovoltaic Array is nonlinear, and the power generated by it is influenced by sun light, temperature and so on. We put forward PV array model by using neural networks identification technique in this paper. The temperature, radiation and voltage of the solar cells are taken as the input and the current as the output of the neural networks model. Using RBF neural network to model for photovoltaic battery and particle swarm optimization algorithm to optimize the RBF neural network, finally the photovoltaic model is established. Simulated experiments are carried out on the photovoltaic battery data, the results show that the improved RBF neural networks have better accuracy and adapt ability than traditional RBF method. The RBF neural networks modeling makes it possible to design on-line controller of photovoltaic system.%本文研究神经网络在光伏电池建模优化问题.由于光伏电池具有高度非线性特性,其输出功率受到外界自然因素的影响,使得传统方法不能满足光伏控制系统动态要求.针对上述问题,本文提出一种粒子群优化的神经网络光伏电池建模算法.改进的方法以日照、温度和负载电压作为提出的RBF神经网络模型的输入值,把光伏电池的输出功率作为神经网络的输出,采用RBF神经网络对光伏电池进行建模,同时利用粒子群算法对神经网络参数进行优化,最后建立光伏电池的动态响应模型.仿真实验结果证明,所提模型更好地克服传统方法的缺点,收敛速度快,具有较高的预测精度和适合能力.

  10. Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Marco Aurelio Sotelo-Figueroa

    2014-01-01

    Full Text Available In recent years Grammatical Evolution (GE has been used as a representation of Genetic Programming (GP which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI. Particle Swarm Optimisation (PSO is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP; it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

  11. Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

    Science.gov (United States)

    Yu, Xiang; Zhang, Xueqing

    2017-01-01

    Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run. PMID:28192508

  12. Binary classification posed as a quadratically constrained quadratic programming and solved using particle swarm optimization

    Indian Academy of Sciences (India)

    DEEPAK KUMAR; A G RAMAKRISHNAN

    2016-03-01

    Particle swarm optimization (PSO) is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The central idea is to use PSO to move in the direction towards optimal solution rather than searching the entire feasibleregion. Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to forma quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or classification boundary for a data set. Our results on the Iris, Pima, Wine, Thyroid, Balance, Bupa, Haberman, and TAE datasets show that the proposed method works better than a neural network and the performance is close to that of a support vector machine

  13. PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.

    Science.gov (United States)

    Ng, Marcus C K; Fong, Simon; Siu, Shirley W I

    2015-06-01

    Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

  14. Path planning for UAV based on quantum-behaved particle swarm optimization

    Science.gov (United States)

    Fu, Yangguang; Ding, Mingyue; Zhou, Chengping; Cai, Chao; Sun, Yangguang

    2009-10-01

    Based on quantum-behaved particle swarm optimization (QPSO), a novel path planner for unmanned aerial vehicle (UAV) is employed to generate a safe and flyable path. The standard particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are presented and compared through a UAV path planning application. Every particle in swarm represents a potential path in search space. For the purpose of pruning the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. As the system iterated, each particle is pulled toward its local attractor, which is located between the personal best position (pbest) and the global best position (gbest) based on the interaction of particles' individual searches and group's public search. For the sake of simplicity, we only consider planning the projection of path on the plane and assume threats are static instead of moving. Simulation results demonstrated the effectiveness and feasibility of the proposed approach.

  15. 基于改进微粒群法和有限元法的混凝土温控方案优化%Optimization of concrete temperature control measures based on improved particle swarm optimization and finite element method

    Institute of Scientific and Technical Information of China (English)

    强晟; 郑伟忠; 张勇强; 刘连建

    2014-01-01

    For the selection of temperature control measures for massive concrete, traditional methods are fully in accordance with the industry standard requirements and subject to repeated artificial amending by practical experience in engineering design and construction. Therefore, it is inefficient and limited by the designer's experience. In this paper, an improved particle swarm optimization (PSO) combined with concrete temperature field and stress field based on the finite element method (FEM) was tested to select the optimal concrete temperature control measures. In the simulation cases, two optimization objectives were defined. The first objective was that the tensile stress of multi feature points should satisfy a safety-cracking factor of at least 1.80. The second was that the whole temperature measures cost should be minimal. The optimization computation was implemented separately with only a single objective for safety factor and both objectives for safety and cost. From the results of 6 calculation cases for a fictitious small-scale concrete dam structure, the following conclusions were drawn. 1) The results show that the proposed method can achieve automatic finding of the temperature control measures optimization, and the optimization results are more scientific and more reasonable. If the ranges of the temperature control parameters can be defined reasonably, the dependence of optimization on humans can be decreased. It will increase the scientificity and persuasion of the temperature control scheme, especially under the complicated situation of more optimization objects, which is very difficult to draw a most reasonable quantitative measures composition. 2) The efficiency of the whole research is improved noticeably. According to experience, if the safety factor is taken as the only objective, the optimization will cost 5 to 7 days by a medium level researcher. In this paper, the time cost of the intelligent optimization is only 2.2 days. 3) After

  16. Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

    2009-12-01

    To better understand insurgent activities and asymmetric warfare, a social adaptive model for modeling multiple insurgent groups attacking multiple military and civilian targets is proposed and investigated. This report presents a pilot study using the particle swarm modeling, a widely used non-linear optimal tool to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamically changing environment and to provide insight and understanding of insurgency warfare. Our results show that unified leadership, strategic planning, and effective communication between insurgent groups are not the necessary requirements for insurgents to efficiently attain their objective.

  17. Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Dorin Sendrescu

    2013-01-01

    Full Text Available This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.

  18. Numerical Simulation of a Tumor Growth Dynamics Model Using Particle Swarm Optimization.

    Science.gov (United States)

    Wang, Zhijun; Wang, Qing

    Tumor cell growth models involve high-dimensional parameter spaces that require computationally tractable methods to solve. To address a proposed tumor growth dynamics mathematical model, an instance of the particle swarm optimization method was implemented to speed up the search process in the multi-dimensional parameter space to find optimal parameter values that fit experimental data from mice cancel cells. The fitness function, which measures the difference between calculated results and experimental data, was minimized in the numerical simulation process. The results and search efficiency of the particle swarm optimization method were compared to those from other evolutional methods such as genetic algorithms.

  19. Adaptive particle swarm optimization for optimal orbital elements of binary stars

    Science.gov (United States)

    Attia, Abdel-Fattah

    2016-12-01

    The paper presents an adaptive particle swarm optimization (APSO) as an alternative method to determine the optimal orbital elements of the star η Bootis of MK type G0 IV. The proposed algorithm transforms the problem of finding periodic orbits into the problem of detecting global minimizers as a function, to get a best fit of Keplerian and Phase curves. The experimental results demonstrate that the proposed approach of APSO generally more accurate than the standard particle swarm optimization (PSO) and other published optimization algorithms, in terms of solution accuracy, convergence speed and algorithm reliability.

  20. Multiagent and Particle Swarm Optimization for Ship Integrated Power System Network Reconfiguration

    Directory of Open Access Journals (Sweden)

    Zheng Wang

    2014-01-01

    Full Text Available Ship integrated power system adopts electric power propulsion. Power network and electric power network are integrated into complicated one. Network reconfiguration of ship integrated power system is a typical nonlinear optimization that is multitarget and multiconstraint. According to the characteristics of ship integrated power system, simplified network model and reconfiguration mathematical model are established. A multiagent and particle swarm optimization is presented to solve network reconfiguration problem. The results of simulation show that multiagent and particle swarm optimization can reconfigure ship integrated power system efficiently.

  1. Application of a particle swarm optimization for shape optimization in hydraulic machinery

    Directory of Open Access Journals (Sweden)

    Moravec Prokop

    2017-01-01

    Full Text Available A study of shape optimization has become increasingly popular in academia and industry. A typical problem is to find an optimal shape, which minimizes (or maximizes a certain cost function and satisfies given constraints. Particle Swarm Optimization (PSO has received a lot of attention in past years and is inspired by social behaviour of some animals such as flocking behaviour of birds. This paper focuses on a possibility of a diffuser shape optimization using particle swarm optimization (PSO, which is coupled with CFD simulation. Influence of main parameters of PSO-algorithm and later diffuser shapes obtained with this method are discussed and advantages/disadvantages summarized.

  2. Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.

    Science.gov (United States)

    Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Perc, Matjaž

    2011-01-01

    We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.

  3. Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Jianlei Zhang

    Full Text Available We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.

  4. ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting

    Science.gov (United States)

    Wang, Hongjie; Zhao, Weigang

    This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of ARIMA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.

  5. Penyelesaian Masalah Penempatan Fasilitas dengan Algoritma Estimasi Distribusi dan Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Amalia Utamima

    2016-11-01

    Full Text Available The layout positioning problem of facilities on a straight line is known as Single Row Facility Layout Problem (PFSB. Categorized as NP-Complete problem, PFSB aim to arrange the layout so that the sum of distances between all facilities’ pairs can be minimized. Estimation of Distribution Algorithm (EDA improves the solution quality efficiently in first few runs, but the diversity lost grows rapidly as more iterations are run. To maintain the diversity, hybridization with meta-heuristic algorithms is needed. This research proposes EDAPSO, an algorithm which consists of hybridization of EDA and Particle Swarm Optimization (PSO. The objective of this research is to test the performance of EDAPSO algorithm for solving PFSB. EDAPSO’s performance is tested in 10 benchmark problems of PFSB and it successfully achieves optimum solution.

  6. A Hybrid Multi Objective Particle Swarm Optimization Method to Discover Biclusters in Microarray Data

    CERN Document Server

    lashkargir, Mohsen; Dastjerdi, Ahmad Baraani

    2009-01-01

    In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A Multi Objective model is capable of solving such problems. Our method proposes a Hybrid algorithm which is based on the Multi Objective Particle Swarm Optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping amongst the biclusters and try to cover all elements of the gene expression matrix. Experimental results in the bench mark database show a significant improvement in both overlap among biclusters and coverage of elements in the gene expression matrix.

  7. An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization

    Indian Academy of Sciences (India)

    S Ganapathy; R Sethukkarasi; P Yogesh; P Vijayakumar; A Kannan

    2014-04-01

    In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min–Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for improving the detection accuracy. Intelligent fuzzy rules are extracted from the temporal features with Fuzzy Min–Max neural network based classifier, and then PSO rule extractor is used to minimize the number of features in the extracted rules. We empirically evaluated the effectiveness of the proposed TFMM-PSO system using the UCI Machine Learning Repository Data Set. The results are analysed and compared with other published results. In addition, the detection accuracy is validated by using the ten-fold cross validation.

  8. A Novel Method for Edge Detection in Images Based on Particle Swarm Optimization

    Science.gov (United States)

    Baby Sherin, C.; Mredhula, L.

    2017-01-01

    Edges give important structural information about the images. Edge detection is a process of identifying and locating the edges in an image. Edges are the points where discontinuity of intensity occurs. It also represents the boundaries of objects in images. In this paper a new edge detection method based on Particle Swarm Optimization is discussed. The proposed method uses morphological operations and a thresholding technique to improve the result of edge detector. This algorithm performs better in images comparing to other traditional methods of edge detection. The performance of proposed method is compared with traditional edge detection methods such as Sobel, Prewitt, Laplacian of Gaussian and Canny with parameters Baddeley's Delta Metric. Statistical analysis is performed to evaluate accuracy of edge detection techniques.

  9. Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

    Directory of Open Access Journals (Sweden)

    Alma Y. Alanis

    2013-01-01

    Full Text Available This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF improved using particle swarm optimization (PSO to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.

  10. Transmission Expansion Planning – A Multiyear Dynamic Approach Using a Discrete Evolutionary Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Saraiva J. T.

    2012-10-01

    Full Text Available The basic objective of Transmission Expansion Planning (TEP is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.

  11. A coordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization

    DEFF Research Database (Denmark)

    Xu, Lizhong; Yang, Guangya; Xu, Zhao

    2013-01-01

    This paper develops a coordinated electricity and heat dispatching model for Microgrid under day-ahead environment. In addition to operational constraints, network loss and physical limits are addressed in this model, which are always ignored in previous work. As an important component of Microgrid......, detailed combined heat and power (CHP) model is developed. The part load performance of CHP is modeled by curve fitting method. Furthermore, electric heater is introduced into the model to improve the economy of Microgrid operation and enhance the flexibility of the Microgrid by electricity-heat conversion....... Particle swarm optimization (PSO) is employed to solve this model for the operation schedule to minimize the total operational cost of Microgrid by coordinating the CHP, electric heater, boiler and heat storage. The efficacy of the model and methodology is verified with different operation scenarios....

  12. A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes.

    Science.gov (United States)

    Chuang, Li-Yeh; Moi, Sin-Hua; Lin, Yu-Da; Yang, Cheng-Hong

    2016-10-01

    Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset. Nine chaotic maps were used to improve PSO method results and compared the searching ability amongst all CPSO methods. The XOR and ZZ disease models were used to compare all chaotic maps combined with PSO method. Efficacy evaluations of CPSO methods were based on statistical values from the chi-square test (χ(2)). The results showed that chaotic maps could improve the searching ability of PSO method when population are trapped in the local optimum. The minor allele frequency (MAF) indicated that, amongst all CPSO methods, the numbers of SNPs, sample size, and the highest χ(2) value in all datasets were found in the Sinai chaotic map combined with PSO method. We used the simple linear regression results of the gbest values in all generations to compare the all methods. Sinai chaotic map combined with PSO method provided the highest β values (β≥0.32 in XOR disease model and β≥0.04 in ZZ disease model) and the significant p-value (p-valuechaotic map was found to effectively enhance the fitness values (χ(2)) of PSO method, indicating that the Sinai chaotic map combined with PSO method is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining

    Directory of Open Access Journals (Sweden)

    Amreen Khan,

    2010-07-01

    Full Text Available Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI has recently emerged that meets these requirements and has successfully been applied to a number ofreal world clustering problems. This paper looks into the use ofParticle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and also allows the particles to be robust to trace the changing environment.

  14. Mutation particle swarm optimization of the BP-PID controller for piezoelectric ceramics

    Science.gov (United States)

    Zheng, Huaqing; Jiang, Minlan

    2016-01-01

    PID control is the most common used method in industrial control because its structure is simple and it is easy to implement. PID controller has good control effect, now it has been widely used. However, PID method has a few limitations. The overshoot of the PID controller is very big. The adjustment time is long. When the parameters of controlled plant are changing over time, the parameters of controller could hardly change automatically to adjust to changing environment. Thus, it can't meet the demand of control quality in the process of controlling piezoelectric ceramic. In order to effectively control the piezoelectric ceramic and improve the control accuracy, this paper replaced the learning algorithm of the BP with the mutation particle swarm optimization algorithm(MPSO) on the process of the parameters setting of BP-PID. That designed a better self-adaptive controller which is combing the BP neural network based on mutation particle swarm optimization with the conventional PID control theory. This combination is called the MPSO-BP-PID. In the mechanism of the MPSO, the mutation operation is carried out with the fitness variance and the global best fitness value as the standard. That can overcome the precocious of the PSO and strengthen its global search ability. As a result, the MPSO-BP-PID can complete controlling the controlled plant with higher speed and accuracy. Therefore, the MPSO-BP-PID is applied to the piezoelectric ceramic. It can effectively overcome the hysteresis, nonlinearity of the piezoelectric ceramic. In the experiment, compared with BP-PID and PSO-BP-PID, it proved that MPSO is effective and the MPSO-BP-PID has stronger adaptability and robustness.

  15. Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization algorithms.

    Science.gov (United States)

    Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo

    2017-01-01

    In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human

  16. The Study on Food Sensory Evaluation based on Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Hairong Wang

    2015-07-01

    Full Text Available In this study, it explores the procedures and methods of the system for establishing food sensory evaluation based on particle swarm optimization algorithm, by means of explaining the interpretation of sensory evaluation and sensory analysis, combined with the applying situation of sensory evaluation in food industry.

  17. Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review.

    Science.gov (United States)

    Bonyadi, Mohammad Reza; Michalewicz, Zbigniew

    2017-01-01

    This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

  18. Design of Wire Antennas by Using an Evolved Particle Swarm Optimization Algorithm

    NARCIS (Netherlands)

    Lepelaars, E.S.A.M.; Zwamborn, A.P.M.; Rogovic, A.; Marasini, C.; Monorchio, A.

    2007-01-01

    A Particle Swarm Optimization (PSO) algorithm has been used in conjunction with a full-wave numerical code based on the Method of Moments (MoM) to design and optimize wire antennas. The PSO is a robust stochastic evolutionary numerical technique that is very effective in optimizing multidimensional

  19. Agent based Particle Swarm Optimization for Load Frequency Control of Distribution Grid

    DEFF Research Database (Denmark)

    Cha, Seung-Tae; Saleem, Arshad; Wu, Qiuwei;

    2012-01-01

    This paper presents a Particle Swarm Optimization (PSO) based on multi-agent controller. Real-time digital simulator (RTDS) is used for modelling the power system, while a PSO based multi-agent LFC algorithm is developed in JAVA for communicating with resource agents and determines the scenario t...

  20. Portfolio management using value at risk: A comparison between genetic algorithms and particle swarm optimization

    NARCIS (Netherlands)

    V.A.F. Dallagnol (V. A F); J.H. van den Berg (Jan); L. Mous (Lonneke)

    2009-01-01

    textabstractIn this paper, it is shown a comparison of the application of particle swarm optimization and genetic algorithms to portfolio management, in a constrained portfolio optimization problem where no short sales are allowed. The objective function to be minimized is the value at risk calculat

  1. 基于改进粒子群优化算法的鲁棒性列车运行图编制方法%A Method for Robust Train Diagram Generation Based on Improved Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    赵宏涛; 苗义烽; 王涛; 张琦

    2013-01-01

    The model constraints are described by train priority,running intervals and succession intervals et al.; the comprehensive objective function is set up through maximizing the total value of dimensionless transformation of 6 indicators which are passenger train's running kilometers,average travel speed,total delay time against standard schedule,delay variance,freight train's car kilometers per car per day and average daily output.All of these contribute to the establishment of train diagram optimization model.Two parameters of virtual train and buffer time slot are introduced to enhance train diagram robustness while a method for robust train diagram generation based on improved particle swarm optimization algorithm is given.Simulation experiments for train diagram generation are done using this method under the experimental environment with 5 stations,5 passenger trains and 7 freight trains.The results show that the method can generate high quality train diagram with better computational efficiency.After train operation being disturbed,virtual train and buffer time slot release resources,and train diagram can be recovered to an acceptable range with better fault-tolerant robustness.%以列车优先级、追踪运行间隔时间和连发间隔时间限制为约束条件,以旅客列车的走行公里总数、平均旅行速度、对标准计划的延迟总时间和延迟时间方差,以及货物列车的日车公里总数和日产最6个指标无量纲转化后的合计值最大为综合目标函数,建立列车运行图编制优化模型.为增加列车运行图的鲁棒性,引入虚拟车和缓冲时间槽2个参数,采用改进的粒子群优化算法给出鲁棒性列车运行图编制方法.以5个车站、5列旅客列车和7列货物列车构造模拟环境,采用该方法进行列车运行图编制模拟.结果表明:该方法可铺画出更优的列车运行图,计算效率高;在行车计划受到扰动后,虚拟车和缓冲时间槽释放资源,运行

  2. Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

    Directory of Open Access Journals (Sweden)

    Allaoua Boumediene

    2008-01-01

    Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.

  3. 粒子群优化算法的发展研究%Research development of particle swarm optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    黄文秀

    2014-01-01

    Particle swarm optimization algorithm (referred to as the particle swarm algorithm, PSO) is a new heuristic algorithm for global based on swarm intelligence, the algorithm simple concept, easy implementation, fast convergence speed, fewer parameter, easy programming, widely in recent years of academic research and application research. This paper first introduces the basic principle and working mechanism of PSO, then focuses on the improvement and application of this algorithm are explained, at last the prospect of the development trend of the algorithm.%粒子群优化算法(简称粒子群算法,PSO)是一种新兴的基于群体智能的启发式全局搜索算法,该算法概念简明、实现方便,收敛速度快、参数设置少,易编程,近年来受到学术界的广泛研究和应用。本文首先介绍PSO的基本原理和工作机制,然后着重就该算法研究的改进及应用进行阐述,最后对该算法的发展趋势进行展望。

  4. A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization

    Science.gov (United States)

    Bhattacharya, Sayantani; Konar, Amit; Das, Swagatam; Han, Sang Yong

    2009-01-01

    The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy. PMID:22303158

  5. FPGA Implementation of Parallel Particle Swarm Optimization Algorithm and Compared with Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    BEN AMEUR Mohamed sadek

    2016-08-01

    Full Text Available In this paper, a digital implementation of Particle Swarm Optimization algorithm (PSO is developed for implementation on Field Programmable Gate Array (FPGA. PSO is a recent intelligent heuristic search method in which the mechanism of algorithm is inspired by the swarming of biological populations. PSO is similar to the Genetic Algorithm (GA. In fact, both of them use a combination of deterministic and probabilistic rules. The experimental results of this algorithm are effective to evaluate the performance of the PSO compared to GA and other PSO algorithm. New digital solutions are available to generate a hardware implementation of PSO Algorithms. Thus, we developed a hardware architecture based on Finite state machine (FSM and implemented into FPGA to solve some dispatch computing problems over other circuits based on swarm intelligence. Moreover, the inherent parallelism of these new hardware solutions with a large computational capacity makes the running time negligible regardless the complexity of the processing.

  6. Optimum Network Reconfiguration and DGs Sizing With Allocation Simultaneously by Using Particle Swarm Optimization (PSO

    Directory of Open Access Journals (Sweden)

    M. N. M. Nasir

    2014-05-01

    Full Text Available This paper presents three stages of methodology. The first stage is to identify the switching operation for radial network configuration while observe the power losses and the voltage profile without Distributed Generation (DG. The second stage is based on previous paper which is feeder reconfiguration for loss reduction with DGs. The last stage is sizing and allocation DGs at buses with low voltage profile resulted from the first stage to improve the power losses and voltage profile also comparing the result with the second stage. The objective of this method proposed is to show that allocation of DGs simultaneously based on low voltage profile can improve network power losses and improvement of voltage profile. The result shows that improvement on network power losses is 54.92% from Distribution Network Reconfiguration (DNR method. All three stages were tested on standards IEEE 33 bus system by using Particle Swarm Optimization (PSO technique in MATLAB software. This method proved that improvement of power losses and voltage profile by switching and DGs allocation method.

  7. Multi-objective particle swarm algorithm based on fuzzy-learning sub-swarm%一种基于模糊学习子群的多目标粒子群算法

    Institute of Scientific and Technical Information of China (English)

    江勋林; 郭坚毅; 唐建; 凌海风

    2011-01-01

    为提高多目标粒子群算法的局部搜索能力,提出了一种模糊学习子群多目标粒子群算法(FLSMOP-SO).在搜索过程中,每个粒子模糊自适应学习生成不确定的p个粒子形成一个子群而不是只产生一个新粒子,然后在其中选择模糊满意解作为其下一代新粒子.对四个典型测试函数的实验结果表明,新算法比NSGAⅡ和MOPSO两种经典多目标优化算法有显著的优越性.%To improve the local search ability of the MOPSO,this paper put forward a new fuzzy learning sub-swarm multi-ob-jective particle swarm optimization ( FLSMOPSO). In the searching process, each particle in the swarm could have linear re-gressive p particles by self-adaptive learning to form a sub-warm rather than a single particle. Then selected a fuzzy satisfied so-lution particle as the new position of the particle. Comparative analysis to the two typical algorithm shows that the new algorithm have prominent advantages.

  8. Particle Swarm Optimization (PSO) for Magnetotelluric (MT) 1D Inversion Modeling

    Science.gov (United States)

    Grandis, Hendra; Maulana, Yahya

    2017-04-01

    Particle Swarm Optimization (PSO) is one of nature-inspired optimization algorithms that adopts swarm (insects, school of fish, flock of birds etc.) behaviour in search for food or common target in a collaborative manner. The particles (or agents) in the swarm learn from their neighbours as well as themselves regarding the promising area in the search space. The information is then used to update their position in order to reach the target. The search algorithm of a particle is dictated by the best position of that particle during the process (individual learning term) and the best particle in its surroundings (social learning term) at a particular iteration. In terms of optimization, the particles are models defined by their parameters, while the promising area in the model space is characterized by a low misfit associated with optimum models. Being a global search approach, PSO is suitable for nonlinear inverse problem resolution. The algorithm was applied to a simple minimization problem for illustration purpose. The application of PSO in geophysical inverse problem is demonstrated by inversion of synthetic magnetotelluric (MT) data associated with simple 1D models with satisfactory results in terms of model recovery as well as data misfit.

  9. Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm

    Science.gov (United States)

    Kia, Saeed; Sebt, Mohammad Hassan; Shahhosseini, Vahid

    2015-03-01

    Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR) to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane's elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar's weight, a 20% decrease in the concrete's weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.

  10. Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Kia Saeed

    2015-03-01

    Full Text Available Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane’s elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar’s weight, a 20% decrease in the concrete’s weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.

  11. Simulation of microcirculatory hemodynamics: estimation of boundary condition using particle swarm optimization.

    Science.gov (United States)

    Pan, Qing; Wang, Ruofan; Reglin, Bettina; Fang, Luping; Pries, Axel R; Ning, Gangmin

    2014-01-01

    Estimation of the boundary condition is a critical problem in simulating hemodynamics in microvascular networks. This paper proposed a boundary estimation strategy based on a particle swarm optimization (PSO) algorithm, which aims to minimize the number of vessels with inverted flow direction in comparison to the experimental observation. The algorithm took boundary values as the particle swarm and updated the position of the particles iteratively to approach the optimization target. The method was tested in a real rat mesenteric network. With random initial boundary values, the method achieved a minimized 9 segments with an inverted flow direction in the network with 546 vessels. Compared with reported literature, the current work has the advantage of a better fit with experimental observations and is more suitable for the boundary estimation problem in pulsatile hemodynamic models due to the experiment-based optimization target selection.

  12. Particle swarm optimisation driven low cost single event transient fault secured design during architectural synthesis

    Directory of Open Access Journals (Sweden)

    Anirban Sengupta

    2017-04-01

    Full Text Available Owing to aggressive shrinking in nanometre scale as well as faster devices, particle strike manifesting itself into transient fault spanning multiple cycle and multiple units will be the centre-focus of application specific datapath generated through high-level synthesis (HLS/architectural synthesis. Addressing each problem above separately leads to large area/delay overhead; thus tackling both problems concurrently, leads to huge incurred overhead. To tackle this complex problem, this paper proposes a novel low cost particle swarm optimisation driven dual modular redundant (DMR based HLS methodology for generation of a transient fault secured design secured against its temporal and spatial effects. The authors' approach provides a low cost optimised fault secured solution through a particle swarm optimisation exploration framework based on user area-delay constraints. Results indicated that proposed approach obtains an area overhead reduction of 34.08% and latency overhead reduction of 5.8% compared with a recent approach.

  13. A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem

    Directory of Open Access Journals (Sweden)

    Hao Yin

    2014-01-01

    Full Text Available For SLA-aware service composition problem (SSC, an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.

  14. A Novel Hybrid Statistical Particle Swarm Optimization for Multimodal Functions and Frequency Control of Hybrid Wind-Solar System

    Science.gov (United States)

    Verma, Harish Kumar; Jain, Cheshta

    2016-09-01

    In this article, a hybrid algorithm of particle swarm optimization (PSO) with statistical parameter (HSPSO) is proposed. Basic PSO for shifted multimodal problems have low searching precision due to falling into a number of local minima. The proposed approach uses statistical characteristics to update the velocity of the particle to avoid local minima and help particles to search global optimum with improved convergence. The performance of the newly developed algorithm is verified using various standard multimodal, multivariable, shifted hybrid composition benchmark problems. Further, the comparative analysis of HSPSO with variants of PSO is tested to control frequency of hybrid renewable energy system which comprises solar system, wind system, diesel generator, aqua electrolyzer and ultra capacitor. A significant improvement in convergence characteristic of HSPSO algorithm over other variants of PSO is observed in solving benchmark optimization and renewable hybrid system problems.

  15. Linear Array Geometry Synthesis with Minimum Side Lobe Level and Null Control Using Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search

    Science.gov (United States)

    Ghosh, Pradipta; Zafar, Hamim

    Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This paper describes the synthesis method of linear array geometry with minimum side lobe level and null control by the Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search (DMSPSO) which optimizes the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. The results of the DMSPSO algorithm have been shown to meet or beat the results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA),General Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way. Three design examples are presented that illustrate the use of the DMSPSO algorithm, and the optimization goal in each example is easily achieved.

  16. A Green Clustering Protocol for Mobile Sensor Network Using Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    Nurul Mu’azzah Abdul Latiff; NikNoordini NikAbdMalik; Abdul Halim Abdul Latiff

    2016-01-01

    Abstract-Energy consumption of sensor nodes is one of the crucial issues in prolonging the lifetime of wireless sensor networks. One of the methods that can improve the utilization of sensor nodes batteries is the clustering method. In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. We define a new fitness function that can optimize the energy consumption of the whole network and minimize the relative distance between cluster heads and their respective member nodes. We also take into account the mobility factor when defining the cluster membership, so that the sensor nodes can join the cluster that has the similar mobility pattern. The performance of the proposed protocol is compared with well-known clustering protocols developed for wireless sensor networks such as LEACH (low-energy adaptive clustering hierarchy) and protocols designed for sensor networks with mobile nodes called CM-IR (clustering mobility-invalid round). In addition, we also modify the improved version of LEACH called MLEACH-C, so that it is applicable to the mobile sensor nodes environment. Simulation results demonstrate that the proposed protocol using PSO algorithm can improve the energy consumption of the network, achieve better network lifetime, and increase the data delivered at the base station.

  17. POLICE OFFICE MODEL IMPROVEMENT FOR SECURITY OF SWARM ROBOTIC SYSTEMS

    Directory of Open Access Journals (Sweden)

    I. A. Zikratov

    2014-09-01

    Full Text Available This paper focuses on aspects of information security for group of mobile robotic systems with swarm intellect. The ways for hidden attacks realization by the opposing party on swarm algorithm are discussed. We have fulfilled numerical modeling of potentially destructive information influence on the ant shortest path algorithm. We have demonstrated the consequences of attacks on the ant algorithm with different concentration in a swarm of subversive robots. Approaches are suggested for information security mechanisms in swarm robotic systems, based on the principles of centralized security management for mobile agents. We have developed the method of forming a self-organizing information security management system for robotic agents in swarm groups implementing POM (Police Office Model – a security model based on police offices, to provide information security in multi-agent systems. The method is based on the usage of police station network in the graph nodes, which have functions of identification and authentication of agents, identifying subversive robots by both their formal characteristics and their behavior in the swarm. We have suggested a list of software and hardware components for police stations, consisting of: communication channels between the robots in police office, nodes register, a database of robotic agents, a database of encryption and decryption module. We have suggested the variants of logic for the mechanism of information security in swarm systems with different temporary diagrams of data communication between police stations. We present comparative analysis of implementation of protected swarm systems depending on the functioning logic of police offices, integrated in swarm system. It is shown that the security model saves the ability to operate in noisy environments, when the duration of the interference is comparable to the time necessary for the agent to overcome the path between police stations.

  18. Determination of oil well production performance using artificial neural network (ANN linked to the particle swarm optimization (PSO tool

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Ahmadi

    2015-06-01

    In this work, novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network (ANN linked to the particle swarm optimization (PSO tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions. It was found that there is very good match between the modeling output and the real data taken from the literature, so that a very low average absolute error percentage is attained (e.g., <0.82%. The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.

  19. The Model of Ship's Magnetic Field Extrapolation Based on Neural Network Improved by Particle Swarm Optimization%基于粒子群改进神经网络的舰艇磁场推算模型

    Institute of Scientific and Technical Information of China (English)

    连丽婷; 肖昌汉; 杨明明; 周国华

    2011-01-01

    针对目前线性建模解决舰艇内外磁场推算问题时存在的困难,从非线性优化的角度出发,建立了内外磁场之间的误差反向传播神经网络预报模型.为了改善网络的固有缺陷,利用粒子群算法优化网络的初始权值与阈值,使其能够逃离局部最优点,增强了网络的鲁棒性.该方法避免了利用线性化方法存在的诸多困难,可实现舰艇内外磁场推算.利用船模实验对网络预测的准确性进行了验证,结果表明其换算精度较线性方法有所提高,满足工程实际需求.%The magnetic anomaly created by ferromagnetic submarines may endanger their invisibility. Nowadays, a new technique called closed-loop degaussing system can reduce the magnetic anomaly especially permanent one in real-time. To achieve it, a model which is able to predict off-board magnetic field from on board measurements was required. Many researchers settle the problem by a linear model. A back propagation neural network model was proposed to solve it. The model can escape local optimum thanks to optimizing the initial weight values and threshold values by particle swarm optimization algorithm. The method can avoid many problems from linear model and its high accuracy and good robustness was tested by a mockup experiment.

  20. Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure

    Directory of Open Access Journals (Sweden)

    Ritu Garg

    2013-05-01

    Full Text Available The problem of scheduling dependent tasks (DAG is an important version of scheduling, to efficiently exploit the computational capabilities of grid systems. The problem of scheduling tasks of a graph onto a set of different machines is an NP Complete problem. As a result, a number of heuristic and meta-heuristic approaches are used over the years due to their ability of providing high quality solutions with reasonable computation time. Discrete Particle Swarm Optimization is one such meta-heuristic used for solving the discrete problem of grid scheduling, but this method converge to sub optimal solutions due to premature convergence. To deal with premature convergence, in this paper we proposed the design and implementation of hierarchical discrete particle swarm optimization (H-DPSO for dependent task scheduling in grid environment. In H-DPSO particles are arranged in dynamic hierarchy where good particles lying above in hierarchy are having larger influence on the swarm. We consider the bi-objective version of problem to minimize makespan and total cost simultaneously as the optimization criteria. The H-DPSO based scheduler was evaluated under different application task graphs. Simulation analysis manifests that H-DPSO based scheduling is highly viable and effective approach for grid computing.

  1. CLUSTERING BASED ADAPTIVE IMAGE COMPRESSION SCHEME USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

    Directory of Open Access Journals (Sweden)

    M.Mohamed Ismail,

    2010-10-01

    Full Text Available This paper presents an image compression scheme with particle swarm optimization technique for clustering. The PSO technique is a powerful general purpose optimization technique that uses the concept of fitness.It provides a mechanism such that individuals in the swarm communicate and exchange information which is similar to the social behaviour of insects & human beings. Because of the mimicking the social sharing of information ,PSO directs particle to search the solution more efficiently.PSO is like a GA in that the population isinitialized with random potential solutions.The adjustment towards the best individual experience (PBEST and the best social experience (GBEST.Is conceptually similar to the cross over operaton of the GA.However it is unlike a GA in that each potential solution , called a particle is flying through the solution space with a velocity.Moreover the particles and the swarm have memory,which does not exist in the populatiom of GA.This optimization technique is used in Image compression and better results have obtained in terms of PSNR, CR and the visual quality of the image when compared to other existing methods.

  2. Viewpoint Selection Using Hybrid Simplex Search and Particle Swarm Optimization for Volume Rendering

    Directory of Open Access Journals (Sweden)

    Zhang You-sai,,,

    2012-09-01

    Full Text Available In this paper we proposed a novel method of viewpoint selection using the hybrid Nelder-Mead (NM simplex search and particle swarm optimization (PSO to improve the efficiency and the intelligent level of volume rendering. This method constructed the viewpoint quality evaluation function in the form of entropy by utilizing the luminance and structure features of the two-dimensional projective image of volume data. During the process of volume rendering, the hybrid NM-PSO algorithm intended to locate the globally optimal viewpoint or a set of the optimized viewpoints automatically and intelligently. Experimental results have shown that this method avoids redundant interactions and evidently improves the efficiency of volume rendering. The optimized viewpoints can focus on the important structural features or the region of interest in volume data and exhibit definite correlation with the perception character of human visual system. Compared with the methods based on PSO or NM simplex search, our method has the better performance of convergence rate, convergence accuracy and robustness.

  3. Automatic Tuning Of Proportional-Integral-Derivative (Pid Controller Using Particle Swarm Optimization (Pso Algorithm

    Directory of Open Access Journals (Sweden)

    S. J. Bassi

    2011-10-01

    Full Text Available The proportional-integral-derivative (PID controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI method of particle swarm optimization (PSO algorithm for tuning the optimal proportional-integral derivative (PID controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler- Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler- Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.

  4. A Method for Consensus Reaching in Product Kansei Evaluation Using Advanced Particle Swarm Optimization

    Science.gov (United States)

    2017-01-01

    Consumers' opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers' preference. However, how to identify and improve the reliability of consumers' Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers' opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers' opinions. Furthermore, the process of the proposed method is presented and the details are illustrated using an example of electronic scooter design evaluation. The case study reveals that the proposed method is promising for reaching a consensus through searching optimal solutions by PSO and improving the reliability of consumers' evaluation opinions toward design alternatives according to Kansei indexes. PMID:28316619

  5. An Optimized Device Sizing of Analog Circuits using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    K. Duraiswamy

    2012-01-01

    Full Text Available Problem statement: Day by day more and more products rely on analog circuits to improve the speed and reduce the power consumption(Products rely on analog circuits to improve the speed and reduce the power consumption day by day more and more.. For the VLSI implementation analog circuit design plays an important role. This analog circuit synthesis might be the most challenging and time-consumed task, because it does not only consist of topology and layout synthesis but also of component sizing. Approach: A Particle Swarm Optimization (PSO technique for the optimal design of analog circuits. Analog signal processing finds many applications and widely uses OpAmp based amplifiers, mixers, comparators. and filters. Results: A two-stage opamp (Miller Operational Trans-conductance Amplifier (OTA is considered for the synthesis that satisfies certain design specifications. Performance has been evaluated with the Simulation Program with Integrated Circuit Emphasis (SPICE circuit simulator until optimal sizes of the transistors are found. Conclusion: The output of the simulation for the two-stage opamp shows that the PSO technique is an accurate and promising approach in determining the device sizes in an analog circuit.

  6. Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization.

    Science.gov (United States)

    Niu, Liyong; Zhang, Di

    2015-01-01

    Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly.

  7. An Evolutionary Stochastic Approach for Efficient Image Retrieval using Modified Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Hadis Heidari

    2016-07-01

    Full Text Available Image retrieval system as a reliable tool can help people in reaching efficient use of digital image accumulation; also finding efficient methods for the retrieval of images is important. Color and texture descriptors are two basic features in image retrieval. In this paper, an approach is employed which represents a composition of color moments and texture features to extract low-level feature of an image. By assigning equal weights for different types of features, we can’t obtain good results, but by applying different weights to each feature, this problem is solved. In this work, the weights are improved using a modified Particle Swarm Optimization (PSO method for increasing average Precision of system. In fact, a novel method based on an evolutionary approach is presented and the motivation of this work is to enhance Precision of the retrieval system with an improved PSO algorithm. The average Precision of presented method using equally weighted features and optimal weighted features is 49.85% and 54.16%, respectively. 4.31% increase in the average Precision achieved by proposed technique can achieve higher recognition accuracy, and the search result is better after using PSO.

  8. Toeplitz block circulant matrix optimized with particle swarm optimization for compressive imaging

    Science.gov (United States)

    Tao, Huifeng; Yin, Songfeng; Tang, Cong

    2016-10-01

    Compressive imaging is an imaging way based on the compressive sensing theory, which could achieve to capture the high resolution image through a small set of measurements. As the core of the compressive imaging, the design of the measurement matrix is sufficient to ensure that the image can be recovered from the measurements. Due to the fast computing capacity and the characteristic of easy hardware implementation, The Toeplitz block circulant matrix is proposed to realize the encoded samples. The measurement matrix is usually optimized for improving the image reconstruction quality. However, the existing optimization methods can destroy the matrix structure easily when applied to the Toeplitz block circulant matrix optimization process, and the deterministic iterative processes of them are inflexible, because of requiring the task optimized to need to satisfy some certain mathematical property. To overcome this problem, a novel method of optimizing the Toeplitz block circulant matrix based on the particle swarm optimization intelligent algorithm is proposed in this paper. The objective function is established by the way of approaching the target matrix that is the Gram matrix truncated by the Welch threshold. The optimized object is the vector composed by the free entries instead of the Gram matrix. The experimental results indicate that the Toeplitz block circulant measurement matrix can be optimized while preserving the matrix structure by our method, and result in the reconstruction quality improvement.

  9. Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Liyong Niu

    2015-01-01

    Full Text Available Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly.

  10. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Po-Chen Cheng

    2015-06-01

    Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

  11. Automatic boiling water reactor control rod pattern design using particle swarm optimization algorithm and local search

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Cheng-Der, E-mail: jdwang@iner.gov.tw [Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan, ROC (China); Lin, Chaung [National Tsing Hua University, Department of Engineering and System Science, 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan (China)

    2013-02-15

    Highlights: ► The PSO algorithm was adopted to automatically design a BWR CRP. ► The local search procedure was added to improve the result of PSO algorithm. ► The results show that the obtained CRP is the same good as that in the previous work. -- Abstract: This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained.

  12. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  13. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Science.gov (United States)

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  14. Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

    Directory of Open Access Journals (Sweden)

    Mi-Yuan Shan

    2013-01-01

    Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.

  15. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.

    Directory of Open Access Journals (Sweden)

    Ahmad Abubaker

    Full Text Available This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO and the Multi-Objective Simulated Annealing (MOSA. Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

  16. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.

    Science.gov (United States)

    Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

    2015-01-01

    This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

  17. Optimal risky bidding strategy for a generating company by self-organising hierarchical particle swarm optimisation

    Energy Technology Data Exchange (ETDEWEB)

    Boonchuay, Chanwit [Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology (Thailand); Ongsakul, Weerakorn, E-mail: ongsakul@ait.asi [Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology (Thailand)

    2011-02-15

    In this paper, an optimal risky bidding strategy for a generating company (GenCo) by self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC) is proposed. A significant risk index based on mean-standard deviation ratio (MSR) is maximised to provide the optimal bid prices and quantities. The Monte Carlo (MC) method is employed to simulate rivals' behaviour in competitive environment. Non-convex operating cost functions of thermal generating units and minimum up/down time constraints are taken into account. The proposed bidding strategy is implemented in a multi-hourly trading in a uniform price spot market and compared to other particle swarm optimisation (PSO). Test results indicate that the proposed SPSO-TVAC approach can provide a higher MSR than the other PSO methods. It is potentially applicable to risk management of profit variation of GenCo in spot market.

  18. Crop classification by forward neural network with adaptive chaotic particle swarm optimization.

    Science.gov (United States)

    Zhang, Yudong; Wu, Lenan

    2011-01-01

    This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10(-7) s.

  19. Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

    Science.gov (United States)

    Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

    2010-01-01

    This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

  20. A novel neutron energy spectrum unfolding code using particle swarm optimization

    Science.gov (United States)

    Shahabinejad, H.; Sohrabpour, M.

    2017-07-01

    A novel neutron Spectrum Deconvolution using Particle Swarm Optimization (SDPSO) code has been developed to unfold the neutron spectrum from a pulse height distribution and a response matrix. The Particle Swarm Optimization (PSO) imitates the bird flocks social behavior to solve complex optimization problems. The results of the SDPSO code have been compared with those of the standard spectra and recently published Two-steps Genetic Algorithm Spectrum Unfolding (TGASU) code. The TGASU code have been previously compared with the other codes such as MAXED, GRAVEL, FERDOR and GAMCD and shown to be more accurate than the previous codes. The results of the SDPSO code have been demonstrated to match well with those of the TGASU code for both under determined and over-determined problems. In addition the SDPSO has been shown to be nearly two times faster than the TGASU code.

  1. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2011-05-01

    Full Text Available This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM based texture features. Then, the features were reduced by principle component analysis (PCA. Finally, a two-hidden-layer forward neural network (NN was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO. K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP, adaptive BP (ABP, momentum BP (MBP, Particle Swarm Optimization (PSO, and Resilient back-propagation (RPROP methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.

  2. Reliability Allocation of Underwater Experiment System Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Lu Xiong

    2013-06-01

    Full Text Available The problem of system reliability allocation is often solved by direct search method. The shortage, which affects the application of this method, is the large calculation amount of complex system architecture. Particle Swarm Optimization (PSO is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. The particle swarm optimization, which attracted the interest of researchers. In this paper, a kind of PSO algorithm is proposed to solve underwater experimental system reliability problems. In addition, the reliability of the system model is established as well, the model is numerically simulated by PSO algorithm and examples are provided. The results show that compared to other algorithms, PSO has a better adaptability and can solve the optimal solution more stably without the precocious weakness, which is more suitable for reliability optimization of a system underwater with a more complex structure.

  3. Application of Particle Swarm Optimization to Formative E-Assessment in Project Management

    Directory of Open Access Journals (Sweden)

    Maria-Iuliana DASCALU

    2011-01-01

    Full Text Available The current paper describes the application of Particle Swarm Optimization algorithm to the formative e-assessment problem in project management. The proposed approach resolves the issue of personalization, by taking into account, when selecting the item tests in an e-assessment, the following elements: the ability level of the user, the targeted difficulty of the test and the learning objectives, represented by project management concepts which have to be checked. The e-assessment tool in which the Particle Swarm Optimization algorithm is integrated is also presented. Experimental results and comparison with other algorithms used in item tests selection prove the suitability of the proposed approach to the formative e-assessment domain. The study is presented in the framework of other evolutionary and genetic algorithms applied in e-education.

  4. Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

    Directory of Open Access Journals (Sweden)

    Jianwen Guo

    2016-01-01

    Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

  5. Application of support vector machine and particle swarm optimization in micro near infrared spectrometer

    Science.gov (United States)

    Xiong, Yuhong; Liu, Yunxiang; Shu, Minglei

    2016-10-01

    In the process of actual measurement and analysis of micro near infrared spectrometer, genetic algorithm is used to select the wavelengths and then partial least square method is used for modeling and analyzing. Because genetic algorithm has the disadvantages of slow convergence and difficult parameter setting, and partial least square method in dealing with nonlinear data is far from being satisfactory, the practical application effect of partial least square method based on genetic algorithm is severely affected negatively. The paper introduces the fundamental principles of particle swarm optimization and support vector machine, and proposes a support vector machine method based on particle swarm optimization. The method can overcome the disadvantage of partial least squares method based on genetic algorithm to a certain extent. Finally, the method is tested by an example, and the results show that the method is effective.

  6. Nuclear Power Plant Construction Scheduling Problem with Time Restrictions: A Particle Swarm Optimization Approach

    Directory of Open Access Journals (Sweden)

    Shang-Kuan Chen

    2016-01-01

    Full Text Available In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.

  7. Fuzzy Adaptive Particle Swarm Optimization for Power Loss Minimisation in Distribution Systems Using Optimal Load Response

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte;

    2014-01-01

    power loss minimization in distribution systems. In this paper, a new method to achieve power loss minimization in distribution systems by using a price signal to guide the demand side management is proposed. A fuzzy adaptive particle swarm optimization (FAPSO) is used as a tool for the power loss...... minimization study. Simulation results show that the proposed approach is an effective measure to achieve power loss minimization in distribution systems....

  8. Stabilizing Gain Selection of Networked Variable Gain Controller to Maximize Robustness Using Particle Swarm Optimization

    CERN Document Server

    Pan, Indranil; Ghosh, Soumyajit; Gupta, Amitava; 10.1109/PACC.2011.5978958

    2012-01-01

    Networked Control Systems (NCSs) are often associated with problems like random data losses which might lead to system instability. This paper proposes a method based on the use of variable controller gains to achieve maximum parametric robustness of the plant controlled over a network. Stability using variable controller gains under data loss conditions is analyzed using a suitable Linear Matrix Inequality (LMI) formulation. Also, a Particle Swarm Optimization (PSO) based technique is used to maximize parametric robustness of the plant.

  9. Democratic Inspired Particle Swarm Optimization for Multi-Robot Exploration Task

    OpenAIRE

    Moslah, Oussama; Hachaïchi, Yassine; Lahbib, Younes

    2016-01-01

    In this paper, we propose a new method for exploring an unknown environment with a team of homogeneous mobile robots. The goal of our approach is to minimize the exploration time. The challenge in multi-robot exploration is how to develop distributed algorithm to govern the colony of robots while choosing its new direction so that they simultaneously explore different regions. In this paper we use the extended version of Particle Swarm Optimization (PSO) to robotic applications, which is refe...

  10. Design of a Fractional Order PID Controller Using Particle Swarm Optimization Technique

    CERN Document Server

    Maiti, Deepyaman; Konar, Amit

    2008-01-01

    Particle Swarm Optimization technique offers optimal or suboptimal solution to multidimensional rough objective functions. In this paper, this optimization technique is used for designing fractional order PID controllers that give better performance than their integer order counterparts. Controller synthesis is based on required peak overshoot and rise time specifications. The characteristic equation is minimized to obtain an optimum set of controller parameters. Results show that this design method can effectively tune the parameters of the fractional order controller.

  11. Field computation in non-linear magnetic media using particle swarm optimization

    Energy Technology Data Exchange (ETDEWEB)

    Adly, A.A. E-mail: amradlya@intouch.com; Abd-El-Hafiz, S.K

    2004-05-01

    This paper presents an automated particle swarm optimization approach using which field computations may be carried out in devices involving non-linear magnetic media. Among the advantages of the proposed approach are its ability to handle complex geometries and its computational efficiency. The proposed approach has been implemented and computations were carried out for an electromagnet subject to different DC excitation conditions. These computations showed good agreement with the results obtained by the finite-element approach.

  12. Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System

    OpenAIRE

    Olusanya, Micheal O.; Arasomwan, Martins A.; Aderemi O. Adewumi

    2015-01-01

    This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients’ blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessi...

  13. A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines

    OpenAIRE

    2015-01-01

    Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algori...

  14. A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

    OpenAIRE

    Lizhi Cui; Zhihao Ling; Josiah Poon; Poon, Simon K.; Junbin Gao; Paul Kwan

    2014-01-01

    This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO), for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM) model is designed to transform these p...

  15. Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

    OpenAIRE

    Jianwen Guo; Zhenzhong Sun; Hong Tang; Xuejun Jia; Song Wang; Xiaohui Yan; Guoliang Ye; Guohong Wu

    2016-01-01

    All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test fun...

  16. Particle Swarm Optimization/Greedy-Search Algorithm for Helicopter Mission Assignment in Disaster Relief

    OpenAIRE

    Andreeva-Mori, Adriana; KOBAYASHI, Keiji; Shindo, Masato; アンドレエバ森, アドリアナ; 小林, 啓二; 真道, 雅人

    2015-01-01

    In the immediate aftermath of a large-scale disaster, optimal helicopter rescue mission assignment is critical to saving many lives. However, the current practice in the field is mostly human centered. The Japan Aerospace Exploration Agency has been developing a decision support system for aircraft operation in order to promptly plan and execute rescue missions. The current research focuses on evacuation missions in particular and investigates the potential of particle swarm optimization with...

  17. Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

    OpenAIRE

    Xunlin Jiang; Haifeng Ling; Jun Yan; Bo Li; Zhao Li

    2013-01-01

    Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, ...

  18. Particle Swarm Optimization Recurrent Neural Network Based Z-source Inverter Fed Induction Motor Drive

    OpenAIRE

    R. Selva Santhose Kumar; S.M. Girirajkumar

    2014-01-01

    In this study, the proposal is made for Particle Swarm Optimization (PSO) Recurrent Neural Network (RNN) based Z-Source Inverter Fed Induction Motor Drive. The proposed method is used to enhance the performance of the induction motor while reducing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque and speed. Here, the PSO technique uses the induction motor speed and reference speed as the input parameters. From the input parameters, it optim...

  19. Feature Selection Using Particle Swarm Optimization for Predicting the Risk of Cardiovascular Disease in Type-II Diabetic Patients

    Directory of Open Access Journals (Sweden)

    P. Radha

    2014-11-01

    Full Text Available Diabetes is the most common disease nowadays in all populations and in all age groups. A wide range of computational methods and tools for data analysis are available to predict the T2D patients with CVD risk factors. Efficient predictive modelling is required for medical researchers and practitioners to improve the prediction accuracy of the classification methods .The aim of this research was to identify significant factors influencing type 2 diabetes control with CVD risk factors, by applying particle swarm optimization feature selection system to improve prediction accuracy and knowledge discovery. Proposed system consists of four major steps such as preprocessing and dimensionality reduction of type 2 diabetes with CVD factors, Attribute Value Measurement, Feature Selection, and Hybrid Prediction Model. In proposed methods the pre-processing and dimensionality reduction of the patients records is performed by using Kullback Leiber Divergence(KLD Principal component analysis (PCA, then attribute values measurement is performed using Fast Correlation-Based Filter Solution(FCBFS, feature selection is performed by using Particle Swarm Optimization (PSO, finally hybrid prediction model which uses Improved Fuzzy C Means (IFCM clustering algorithm aimed at validating chosen class label of given data and subsequently applying Extreme Learning Machine(ELM classification algorithm to the result set.

  20. Study on Ice Regime Forecast Based on SVR Optimized by Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG; Fu-qiang; RONG; Fei

    2012-01-01

    [Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.

  1. An image reconstruction algorithm for electrical capacitance tomography based on simulated annealing particle swarm optimization

    Directory of Open Access Journals (Sweden)

    P. Wang

    2015-04-01

    Full Text Available In this paper, we introduce a novel image reconstruction algorithm with Least Squares Support Vector Machines (LS-SVM and Simulated Annealing Particle Swarm Optimization (APSO, named SAP. This algorithm introduces simulated annealing ideas into Particle Swarm Optimization (PSO, which adopts cooling process functions to replace the inertia weight function and constructs the time variant inertia weight function featured in annealing mechanism. Meanwhile, it employs the APSO procedure to search for the optimized resolution of Electrical Capacitance Tomography (ECT for image reconstruction. In order to overcome the soft field characteristics of ECT sensitivity field, some image samples with typical flow patterns are chosen for training with LS-SVM. Under the training procedure, the capacitance error caused by the soft field characteristics is predicted, and then is used to construct the fitness function of the particle swarm optimization on basis of the capacitance error. Experimental results demonstrated that the proposed SAP algorithm has a quick convergence rate. Moreover, the proposed SAP outperforms the classic Landweber algorithm and Newton-Raphson algorithm on image reconstruction.

  2. An Improved GMM and GMR Based on Particle Swarm Optimization with an Application in Extra Virgin Olive Oil Analysis%基于粒子群优化算法的高斯混合模型和高斯混合回归用于橄榄油品质分析

    Institute of Scientific and Technical Information of China (English)

    孙晓丹; 李新会; 石伟民; 申琦

    2015-01-01

    随着橄榄油掺假现象日趋严重,寻找一种简单有效的鉴伪分析方法至关重要。采用基于粒子群优化的高斯混合模型和高斯混合回归结合傅里叶变换红外光谱对橄榄油掺假样品进行定性和定量分析,取得了较好的分析结果。%In recent years,olive oil adulteration phenomenon has become increasingly serious. Looking for a simple and effective detection method is very important. In this paper,an improved Gaussian mixture model and Gaussian mixture regression based on particle swarm combined with FT-IR spectrum was used to classify and quantify the adulteration in olive oil and has obtained a good analysis result.

  3. Vector bundle constraint for particle swarm optimization and its application to active contour modeling

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Active contour modeling (ACM) has been shown to be a powerful method in object boundary extraction. In this paper,a new ACM based on vector bundle constraint for particle swarm optimization (VBCPSO-ACM) is proposed. Different from the traditional.particle swarm optimization (PSO), in the process of velocity update, a vector bundle is predefined for each particle and velocity update of the particle is restricted to its bundle. Applying this idea to ACM, control points on the contour are treated as particles in PSO and the evolution of the contour is driven by the particles. Meanwhile, global searching is shifted to local searching in ACM by decreasing the number of neighbors and inertia. In addition, the addition and deletion of particles on the active contour make this new model possible for representing the real boundaries more precisely. The proposed VBCPSO-ACM can avoid self-intersection during contour evolving and also extract inhomogeneous boundaries. The simulation results proved its great performance in performing contour extraction.

  4. 基于改进粒子群算法的采购—库存—生产协同计划问题研究%Study on Collaborative Planning of Purchasing, Inventory and Production Based on Improved Particle Swarming Algorithm

    Institute of Scientific and Technical Information of China (English)

    张明伟; 李波

    2015-01-01

    In this paper, in view of the problems in the job-shop production scheduling, purchasing and raw material inventory decision-making of the manufacturing enterprises, we proposed the purchasing-inventory-production collaborative planning model from the perspective of supply chain integration, proposed the improved particle swarming algorithm to solve the model, presented the specific process for the realization of the algorithm, and at the end, through a practical case, proved the efficiency and robustness of the improved particle swarm algorithm.%针对加工企业Job-Shop生产调度、采购与原材料库存决策存在相互独立的问题,从供应链集成角度提出了采购—库存—生产协同计划模型,以协同优化企业生产调度、原材料采购计划及原材料库存决策.提出了求解该模型的改进粒子群算法,借鉴遗传算法的交叉算子代替粒子群算法中的速度算子,在初始粒子群中加入启发式因子以提高算法的计算速度与解的质量,并给出了算法的具体实现过程.最后,通过实例验证了改进粒子群算法的效率与鲁棒性,并证明了采购—库存—生产协同计划模型的可行性与有效性.

  5. OPTIMIZATION OF A PULTRUSION PROCESS USING FINITE DIFFERENCE AND PARTICLE SWARM ALGORITHMS

    Directory of Open Access Journals (Sweden)

    L. S. Santos

    2015-06-01

    Full Text Available AbstractPultrusion is one of several manufacturing processes for reinforced polymer composites. In this process fibers are continuously pulled through a resin bath and, after impregnation, the fiber-resin assembly is cured in a heated forming die. In order to obtain a polymeric composite with good properties (high and uniform degree of cure and a process with a minimum of wasted energy, an optimization procedure is necessary to calculate the optimal temperature profile. The present work suggests a new strategy to minimize the energy rate taking into account the final quality of the product. For this purpose the particle swarm optimization (PSO algorithm and the computer code DASSL were used to solve the differential algebraic equation that represents the mathematical model. The results of the optimization procedure were compared with results reported in the literature and showed that this strategy may be a good alternative to find the best operational point and to test other heat policies in order to improve the material quality and minimize the energy cost. In addition, the robustness and fast convergence of the algorithm encourage industrial implementation for the inference of the degree of cure and optimization.

  6. MIMO-Radar Waveform Design for Beampattern Using Particle-Swarm-Optimisation

    KAUST Repository

    Ahmed, Sajid

    2012-07-31

    Multiple input multiple output (MIMO) radars have many advantages over their phased-array counterparts: improved spatial resolution; better parametric identifiably and greater flexibility to acheive the desired transmit beampattern. The desired transmit beampatterns using MIMO-radar requires the waveforms to have arbitrary auto- and cross-correlations. To design such waveforms, generally a waveform covariance matrix, R, is synthesised first then the actual waveforms are designed. Synthesis of the covariance matrix, R, is a constrained optimisation problem, which requires R to be positive semidefinite and all of its diagonal elements to be equal. To simplify the first constraint the covariance matrix is synthesised indirectly from its square-root matrix U, while for the second constraint the elements of the m-th column of U are parameterised using the coordinates of the m-hypersphere. This implicitly fulfils both of the constraints and enables us to write the cost-function in closed form. Then the cost-function is optimised using a simple particle-swarm-optimisation (PSO) technique, which requires only the cost-function and can optimise any choice of norm cost-function. © 2012 IEEE.

  7. A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Yingxian Zhang

    2014-01-01

    Full Text Available We propose a generalization belief propagation (BP decoding algorithm based on particle swarm optimization (PSO to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm.

  8. Particle Swarm Optimization Algorithm Coupled with Finite Element Limit Equilibrium Method for Geotechnical Practices

    Directory of Open Access Journals (Sweden)

    Hongjun Li

    2012-01-01

    Full Text Available This paper proposes a modified particle swarm optimization algorithm coupled with the finite element limit equilibrium method (FELEM for the minimum factor of safety and the location of associated noncircular critical failure surfaces for various geotechnical practices. During the search process, the stress compatibility constraints coupled with the geometrical and kinematical compatibility constraints are firstly established based on the features of slope geometry and stress distribution to guarantee realistic slip surfaces from being unreasonable. Furthermore, in the FELEM, based on rigorous theoretical analyses and derivation, it is noted that the physical meaning of the factor of safety can be formulated on the basis of strength reserving theory rather than the overloading theory. Consequently, compared with the limit equilibrium method (LEM and the shear strength reduction method (SSRM through several numerical examples, the FELEM in conjunction with the improved search strategy is proved to be an effective and efficient approach to routine analysis and design in geotechnical practices with a high level of confidence.

  9. A fuzzy-based particle swarm optimisation approach for task assignment in home healthcare

    Directory of Open Access Journals (Sweden)

    Mutingi, Michael

    2014-11-01

    Full Text Available Home healthcare (HHC organisations provide coordinated healthcare services to patients at their homes. Motivated by the ever-increasing need for home-based care, the assignment of tasks to available healthcare staff is a common and complex problem in homecare organisations. Designing high quality task schedules is critical for improving worker morale, job satisfaction, service efficiency, service quality, and competitiveness over the long term. The desire is to provide high quality task assignment schedules that satisfy the patient, the care worker, and the management. This translates to maximising schedule fairness in terms of workload assignments, avoiding task time window violation, and meeting management goals as much as possible. However, in practice, these desires are often subjective as they involve imprecise human perceptions. This paper develops a fuzzy multi-criteria particle swarm optimisation (FPSO approach for task assignment in a home healthcare setting in a fuzzy environment. The proposed approach uses a fuzzy evaluation method from a multi-criteria point of view. Results from illustrative computational experiments show that the approach is promising.

  10. Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization

    Science.gov (United States)

    Tan, Weng Chun; Mat Isa, Nor Ashidi

    2016-01-01

    In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581

  11. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    Directory of Open Access Journals (Sweden)

    C. V. Subbulakshmi

    2015-01-01

    Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  12. A Novel Optimal Control Method for Impulsive-Correction Projectile Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Ruisheng Sun

    2016-01-01

    Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.

  13. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier.

    Science.gov (United States)

    Subbulakshmi, C V; Deepa, S N

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  14. Immune particle swarm optimization of linear frequency modulation in acoustic communication

    Institute of Scientific and Technical Information of China (English)

    Haipeng Ren; Yang Zhao

    2015-01-01

    With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channels because it suffers from more serious multipath effect, fewer available bandwidths and quite complex noise. Since the signals experience a serious distortion after being transmitted through the underwater acoustic channel, the underwater acoustic communication experiences a high bit error rate (BER). To solve this problem, carrier waveform inter-displacement (CWID) modulation is proposed. It has been proved that CWID modulation is an effective method to decrease BER. The linear frequency modulation (LFM) carrier-waves are used in CWID modulation. The performance of the communication using CWID modulation is sensitive to the change of the frequency band of LFM carrier-waves. The immune particle swarm optimization (IPSO) is introduced to search for the optimal frequency band of the LFM carrier-waves, due to its excel ent performance in solving complicated optimization problems. The multi-objective and multi-peak optimization nature of the IPSO gives a suitable description of the relationship between the upper band and the lower band of the LFM carrier-waves. Simulations verify the improved perfor-mance and effectiveness of the optimization method.

  15. Adaptive impedance control of a hydraulic suspension system using particle swarm optimisation

    Science.gov (United States)

    Fateh, Mohammad Mehdi; Moradi Zirkohi, Majid

    2011-12-01

    This paper presents a novel active control approach for a hydraulic suspension system subject to road disturbances. A novel impedance model is used as a model reference in a particular robust adaptive control which is applied for the first time to the hydraulic suspension system. A scheme is introduced for selecting the impedance parameters. The impedance model prescribes a desired behaviour of the active suspension system in a wide range of different road conditions. Moreover, performance of the control system is improved by applying a particle swarm optimisation algorithm for optimising control design parameters. Design of the control system consists of two interior loops. The inner loop is a force control of the hydraulic actuator, while the outer loop is a robust model reference adaptive control (MRAC). This type of MRAC has been applied for uncertain linear systems. As another novelty, despite nonlinearity of the hydraulic actuator, the suspension system and the force loop together are presented as an uncertain linear system to the MRAC. The proposed control method is simulated on a quarter-car model. Simulation results show effectiveness of the method.

  16. Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Aristeidis Antonakis

    2017-04-01

    Full Text Available In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.

  17. Power and time slot allocation in cognitive relay networks using particle swarm optimization.

    Science.gov (United States)

    Derakhshan-Barjoei, Pouya; Dadashzadeh, Gholamreza; Razzazi, Farbod; Razavizadeh, S Mohammad

    2013-01-01

    The two main problems in cognitive radio networks are power and time slot allocation problems which require a precise analysis and guarantee the quality of service in both the primary and secondary users. In this paper, these two problems are considered and a method is proposed to solve the resulting optimization problem. Our proposed method provides an improved performance in solving the constrained nonlinear multiobject optimization for the power control and beamforming in order to reach the maximum capacity and proper adaption of time slots, and as a result a new scheme for joint power and time slot allocation in cognitive relay networks is proposed. We adopt space diversity access as the secondary users access scheme and divide the time between multiple secondary users according to their contribution to primary user's transmission. Helping primary users provides more opportunities for secondary users to access the channel since the primary users can release the channel sooner. In contrast, primary network leases portion of channel access time to the secondary users for their transmission using particle swarm optimization (PSO). Numerical studies show good performance of the proposed scheme with a dynamic cost function in a nonstationary environment.

  18. Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Muhammad Imran

    2014-01-01

    Full Text Available One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF coupled with support vector machine (SVM has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO. The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

  19. Optimal mechanical harvester route planning for sugarcane f ield operations using particle swarm optimization

    Directory of Open Access Journals (Sweden)

    Woraya Neungmatcha

    2015-06-01

    Full Text Available Since current agricultural production systems such as the sugarcane supply system in the sugar industry are developing towards larger and more complicated systems, there is consequently increasing use of agricultural machinery. Even though mechanization can help to increase the sugarcane yield, if the mechanical operation efficiency is low, then higher harvest costs and machinery shortages will occur. Global route planning for mechanical harvesters is one of the most important problems in the field of sugarcane harvesting and transporting operations. Improved efficiency and realistic implementation can be achieved by applying advanced planning methods for the execution of field operations, especially considering the field accessibility aspect. To address this issue, participative research was undertaken with a sugar milling company to produce and implement a mixed integer programming model that represents the mechanical harvester route plan. Particle swarm optimization was applied to find a solution to the model, leading to potential cost savings versus schedules produced manually by the mill officer. The model was also applied to explore regional planning options for a more integrated harvesting and transport system.

  20. Application of particle swarm optimization to interpret Rayleigh wave dispersion curves

    Science.gov (United States)

    Song, Xianhai; Tang, Li; Lv, Xiaochun; Fang, Hongping; Gu, Hanming

    2012-09-01

    Rayleigh waves have been used increasingly as an appealing tool to obtain near-surface shear (S)-wave velocity profiles. However, inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this study, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme based on particle swarm optimization (PSO). PSO is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate calculation efficiency and stability of PSO to inversion of surface wave data, we first inverted three noise-free and three noise-corrupted synthetic data sets. Then, we made a comparative analysis with genetic algorithms (GA) and a Monte Carlo (MC) sampler and reconstructed a histogram of model parameters sampled on a low-misfit region less than 15% relative error to further investigate the performance of the proposed inverse procedure. Finally, we inverted a real-world example from a waste disposal site in NE Italy to examine the applicability of PSO on Rayleigh wave dispersion curves. Results from both synthetic and field data demonstrate that particle swarm optimization can be used for quantitative interpretation of Rayleigh wave dispersion curves. PSO seems superior to GA and MC in terms of both reliability and computational efforts. The great advantages of PSO are fast in locating the low misfit region and easy to implement. Also there are only three parameters to tune (inertia weight or constriction factor, local and global acceleration constants). Theoretical results exist to explain how to tune these parameters.

  1. 基于改进粒子群算法的无人机爬升轨迹优化%Climb Trajectory Optimization of UAV Based on Improved Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    张培培; 王新民; 陈晓; 魏宏坤

    2012-01-01

    研究无人机控制优化爬升性能问题,由于单独提高速度或节省燃油问题,均存在互相影响.为了使无人机能够快速、省油地爬升到预定高度,综合考虑了油耗和时间这两个因素.在分析了无人机爬升段数学模型的基础上,提出将油耗和时间的综合运营成本作为优化指标,并提出了一种改进粒子群算法的无人机爬升轨迹优化方法.将无人机轨迹优化问题转化为有约束的参数优化问题,并用改进粒子群算法进行参数优化,从而得到综合指标最优的爬升轨迹.对某无人机实例进行爬升轨迹优化,仿真结果比传统方法更节省了运营成本,证明了改进方法的优越性.%In order to achieve the desired height economically and quickly, this paper considered the two factors of fuel cost and time. First, the paper studied the mathematical model of UAV climb trajectory optimization, and then made the total cost of fuel and time as the performance index. Second, this paper proposed a method of Unmanned Aerial Vehicle(UAV) climb trajectory optimization based on particle swarm optimization(PSO) and turned the problem of UAV trajectory optimization into the problem of constrained parameter optimization, and found the optimal parameters using PSO. In the end, validation was made with a UAV, and the result turned to be saving more operational costs, which proves the method is better.

  2. 改进粒子群算法的电动汽车时空优化分配策略%A space-time allocation strategy optimized by improved particle swarm algorithm for electric vehicle

    Institute of Scientific and Technical Information of China (English)

    李慧媛; 郭兴众; 牛明强

    2014-01-01

    This paper proposes a new concentrated charging scheduling strategy,which considers both spatial al-location and temporal allocation of electric vehicle and uses the number of charging electric in each time interval and each charging station vehicles as decision variables.For achieving averagly distributed charging electric vehi-cles in space-time,the mathematical models of load variance and the number of charging vehicles variance are set up,an optimized space-time allocation strategy is proposed to make loads reached equilibrium in time and space, and basic particle swarm optimization (PSO)algorithm combines the linear decreasing weight and asynchronous change learning factor methods to solve the models.Finally,a simulation based on a certain area's load curve is made,revealing that the poposed centralized charging scheduling strategy can not only lower the peak-valley difference,but aslo reduce the load fluctuation.%兼顾待充电汽车的时间分配和空间分配,以每个时段每个充电站的充电电动汽车数量为决策变量,建立了集中充电时段内充电负荷方差和充电站充电汽车数量方差的数学模型。提出时空优化分配策略,使待充电汽车在时空上达到均衡分配,并在基本粒子群算法基础上结合了线性递减权重和异步变化学习因子方法。基于纽约州独立系统交易运行机构(NYISO)的原始负荷数据进行算例仿真。结果表明,文中提出的电动汽车集中充电调度策略在时空上优化分配待充电汽车,达到了降低负荷峰谷差、减小负荷波动的目的。

  3. A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Shao-Fei Jiang

    2014-01-01

    Full Text Available Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO. This paper presents an improved MPSCO algorithm (IMPSCO firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA. The results show threefold: (1 the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2 the damage location can be accurately detected using the damage threshold proposed in this paper; and (3 compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

  4. An Immune Cooperative Particle Swarm Optimization Algorithm for Fault-Tolerant Routing Optimization in Heterogeneous Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yifan Hu

    2012-01-01

    Full Text Available The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs applications, and has recently been attracting growing research interests. In order to maintain k disjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.

  5. A hybrid particle swarm optimization approach with neural network and set pair analysis for transmission network planning

    Institute of Scientific and Technical Information of China (English)

    刘吉成; 颜苏莉; 乞建勋

    2008-01-01

    Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.

  6. Particle swarm optimization with random keys applied to the nuclear reactor reload problem

    Energy Technology Data Exchange (ETDEWEB)

    Meneses, Anderson Alvarenga de Moura [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear; Fundacao Educacional de Macae (FUNEMAC), RJ (Brazil). Faculdade Professor Miguel Angelo da Silva Santos; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mails: ameneses@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; schirru@lmp.ufrj.br

    2007-07-01

    In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)

  7. Fuzzy Neural Networks Learning by Variable-Dimensional Quantum-behaved Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Zhao

    2013-10-01

    Full Text Available The evolutionary learning of fuzzy neural networks (FNN consists of structure learning to determine the proper number of fuzzy rules and parameters learning to adjust the network parameters. Many optimization algorithms can be applied to evolve FNN. However the search space of most algorithms has fixed dimension, which can not suit to dynamic structure learning of FNN. We propose a novel technique, which is named the variable-dimensional quantum-behaved particle swarm optimization algorithm (VDQPSO, to address the problem. In the proposed algorithm, the optimum dimension, which is unknown at the beginning, is updated together with the position of swarm. The optimum dimension converged at the end of the optimization process corresponds to a unique FNN structure where the optimum parameters can be achieved. The results of the prediction of chaotic time series experiment show that the proposed technique is effective. It can evolve to optimum or near-optimum FNN structure and optimum parameters.

  8. Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments

    Energy Technology Data Exchange (ETDEWEB)

    Kurt Derr; Milos Manic

    2009-05-01

    Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.

  9. Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

    Institute of Scientific and Technical Information of China (English)

    Ratnakar Dash; Pankaj Kumar Sa; Banshidhar Majhi

    2012-01-01

    This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM)for blind image restoration.In this work,SVM is used to solve a regression problem.Support vector regression (SVR)has been utilized to obtain a true mapping of images from the observed noisy blurred images.The parameters of SVR are optimized through particle swarm optimization (PSO) technique.The restoration error function has been utilized as the fitness function for PSO.The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness.The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.

  10. Binary particle swarm optimization algorithm assisted to design of plasmonic nanospheres sensor

    Science.gov (United States)

    Kaboli, Milad; Akhlaghi, Majid; Shahmirzaee, Hossein

    2016-04-01

    In this study, a coherent perfect absorption (CPA)-type sensor based on plasmonic nanoparticles is proposed. It consists of a plasmonic nanospheres array on top of a quartz substrate. The refractive index changes above the sensor surface, which is due to the appearance of gas or the absorption of biomolecules, can be detected by measuring the resulting spectral shifts of the absorption coefficient. Since the CPA efficiency depends strongly on the number of plasmonic nanoparticles and the locations of nanoparticles, binary particle swarm optimization (BPSO) algorithm is used to design an optimized array of the plasmonic nanospheres. This optimized structure should be maximizing the absorption coefficient only in the one frequency. BPSO algorithm, a swarm of birds including a matrix with binary entries responsible for controlling nanospheres in the array, shows the presence with symbol of ('1') and the absence with ('0'). The sensor can be used for sensing both gas and low refractive index materials in an aqueous environment.

  11. OPTIMIZATION OF PLY STACKING SEQUENCE OF COMPOSITE DRIVE SHAFT USING PARTICLE SWARM ALGORITHM

    Directory of Open Access Journals (Sweden)

    CHANNAKESHAVA K. R.

    2011-06-01

    Full Text Available In this paper an attempt has been made to optimize ply stacking sequence of single piece E-Glass/Epoxy and Boron /Epoxy composite drive shafts using Particle swarm algorithm (PSA. PSA is a population based evolutionary stochastic optimization technique which is a resent heuristic search method, where mechanics are inspired by swarming or collaborative behavior of biological population. PSA programme is developed to optimize the ply stacking sequence with an objective of weight minimization by considering design constraints as torque transmission capacity, fundamental natural frequency, lateral vibration and torsional buckling strength having number of laminates, ply thickness and stacking sequence as design variables. The weight savings of the E-Glass/epoxy and Boron /Epoxy shaft from PAS were 51% and 85 % of the steel shaft respectively. The optimum results of PSA obtained are compared with results of genetic algorithm (GA results and found that PSA yields better results than GA.

  12. 基于铁路无线通信环境下改进的PSO算法在多用户检测中的应用%Application of Improved Particle Swarm Optimization Algorithm to Multi-user Detection Based on Railway Wireless Communication Environment

    Institute of Scientific and Technical Information of China (English)

    刘子轶; 张荣新; 郭景武

    2016-01-01

    随着无线通信技术在铁路系统中迅猛发展和广泛应用,对通信系统装备的抗干扰能力提出更高更新的要求,因此必须研究有效的抗干扰技术以对付日益严重的干扰威胁。在粒子群算法中采用量子力学和经典力学的理论,将量子离散粒子群算法应用于多用户检测技术。在铁路无线通信系统中,可以提高铁路信道质量,降低数据通信误码率,解决列车与调度指挥中心之间的高数据通信中误码率高的问题,并且在抗多址干扰能力明显优于传统检测器。%With the rapid development of railway wireless communication, more rigorous requirements for anti-interference ability of the communication system devices are put forward. Therefore, it is necessary to study the effective anti-jamming technology to deal with the growing threat of interference. Quantum mechanics and classical mechanics are used in particle swarm optimization algorithm and quantum discrete particle swarm algorithm is applied to multi-user detection. In the wireless railway communication system, it improves the quality of the channel, reduce bit error rate, and solve the problem of high bit error rate in high data communication between the train and the dispatch command center, and proves superior to traditional detectors in terms of anti multiple access interference.

  13. Cooperative Task Allocation for Multiple UAVs Based on Improved Multi-objective Quantum-behaved Particle Swarm Optimization Algorithm%基于改进的多目标量子行为粒子群优化算法的多无人机协同任务分配

    Institute of Scientific and Technical Information of China (English)

    施展; 陈庆伟

    2012-01-01

    为在给定的时间内以最小代价和最大效益完成任务,建立了多无人机协同任务分配问题的多目标优化模型.采用改进的多目标量子行为粒子群优化算法求解最优任务分配方案,定义了一种从所求候选方案中选取最优分配方案的自主选择准则.对比分析多目标粒子群优化、多目标进化算法和该文算法所求的最优分配方案.仿真结果表明该文算法能够较快地求解问题,而且所求最优任务分配方案的性能优于其它三种算法.%To accomplish ordered missions with the least cost and most benefit in given time,a multi-objective optimization model for cooperative task allocation of multiple unmanned aerial vehicles (UAVs)is established. An improved multi-objective quantum-behaved particle swarm optimization algorithm is used to solve the optimal task allocation scheme. An autonomous selection criterion for optimal allocation scheme choice in the obtained optional schemes is defined. The optimal allocation schemes solved by the multi-objective particle swarm optimization, the multi-objective evolutionary algorithm and the algorithm in this paper are contrastly analyzed. The simulation results show the proposed approach can solute the problem fast, and the performance of the task allocation scheme of this approach is better than others.

  14. NEURAL NETWORK TRAINING WITH PARALLEL PARTICLE SWARM OPTIMIZER

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Feed forward neural net works such as multi-layer perceptron,radial basis function neural net-works,have been widely applied to classification,function approxi mation and data mining.Evolu-tionary computation has been explored to train neu-ral net works as a very promising and competitive al-ternative learning method,because it has potentialto produce global mini mum in the weight space.Recently,an emerging evolutionary computationtechnique,Particle Swar m Opti mization(PSO)be-comes a hot topic because of i...

  15. Particle swarm optimization of neural network CAD systems with clinically relevant objectives

    Science.gov (United States)

    Habas, Piotr A.; Zurada, Jacek M.; Elmaghraby, Adel S.; Tourassi, Georgia D.

    2007-03-01

    Neural networks (NN) are typically developed to minimize the squared difference between the network's output and the target value for a set of training patterns; namely the mean squared error (MSE). However, lower MSE does not necessarily translate into a clinically more useful decision model. The purpose of this study was to investigate the particle swarm optimization (PSO) algorithm as an alternative way of NN optimization with clinically relevant objective functions (e.g., ROC and partial ROC area indices). The PSO algorithm was evaluated with respect to a NN-based CAD system developed to discriminate mammographic regions of interest (ROIs) that contained masses from normal regions based on 8 computer-extracted morphology-oriented features. Neural networks were represented as points (particle locations) in a D-dimensional search/optimization space where each dimension corresponded to one adaptable NN parameter. The study database of 1,337 ROIs (681 with masses, 656 normal) was split into two subsets to implement two-fold cross-validation sampling scheme. Neural networks were optimized with the PSO algorithm and the following objective functions (1) MSE, (2) ROC area index AUC, and (3) partial ROC area indices TPFAUC with TPF=0.90 and TPF=0.98. For comparison, performance of neural networks of the same architecture trained with the traditional backpropagation algorithm was also evaluated. Overall, the study showed that when the PSO algorithm optimized network parameters using a particular training objective, the NN test performance was superior with respect to the corresponding performance index. This was particularly true for the partial ROC area indices where statistically significant improvements were observed.

  16. Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem

    OpenAIRE

    Lifang He; Songwei Huang

    2016-01-01

    The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowworm swarm optimization (IGSO) algorithm has been presented to find the optimal multilevel th...

  17. Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization

    Science.gov (United States)

    Zhang, Xingwu; Gao, Robert X.; Yan, Ruqiang; Chen, Xuefeng; Sun, Chuang; Yang, Zhibo

    2016-08-01

    Crack is one of the crucial causes of structural failure. A methodology for quantitative crack identification is proposed in this paper based on multivariable wavelet finite element method and particle swarm optimization. First, the structure with crack is modeled by multivariable wavelet finite element method (MWFEM) so that the vibration parameters of the first three natural frequencies in arbitrary crack conditions can be obtained, which is named as the forward problem. Second, the structure with crack is tested to obtain the vibration parameters of first three natural frequencies by modal testing and advanced vibration signal processing method. Then, the analyzed and measured first three natural frequencies are combined together to obtain the location and size of the crack by using particle swarm optimization. Compared with traditional wavelet finite element method, MWFEM method can achieve more accurate vibration analysis results because it interpolates all the solving variables at one time, which makes the MWFEM-based method to improve the accuracy in quantitative crack identification. In the end, the validity and superiority of the proposed method are verified by experiments of both cantilever beam and simply supported beam.

  18. Free vibration control of smart composite beams using particle swarm optimized self-tuning fuzzy logic controller

    Science.gov (United States)

    Zorić, Nemanja D.; Simonović, Aleksandar M.; Mitrović, Zoran S.; Stupar, Slobodan N.; Obradović, Aleksandar M.; Lukić, Nebojša S.

    2014-10-01

    This paper deals with active free vibrations control of smart composite beams using particle-swarm optimized self-tuning fuzzy logic controller. In order to improve the performance and robustness of the fuzzy logic controller, this paper proposes integration of self-tuning method, where scaling factors of the input variables in the fuzzy logic controller are adjusted via peak observer, with optimization of membership functions using the particle swarm optimization algorithm. The Mamdani and zero-order Takagi-Sugeno-Kang fuzzy inference methods are employed. In order to overcome stability problem, at the same time keeping advantages of the proposed self-tuning fuzzy logic controller, this controller is combined with the LQR making composite controller. Several numerical studies are provided for the cantilever composite beam for both single mode and multimodal cases. In the multimodal case, a large-scale system is decomposed into smaller subsystems in a parallel structure. In order to represent the efficiency of the proposed controller, obtained results are compared with the corresponding results in the cases of the optimized fuzzy logic controllers with constant scaling factors and linear quadratic regulator.

  19. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

    Science.gov (United States)

    Huang, Daizheng; Wu, Zhihui

    2017-01-01

    Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

  20. A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

    Directory of Open Access Journals (Sweden)

    Ying-Yi Hong

    2016-01-01

    Full Text Available This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1′′ constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.

  1. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

    Science.gov (United States)

    Huang, Daizheng; Wu, Zhihui

    2017-01-01

    Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods. PMID:28222194

  2. Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

    Science.gov (United States)

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-07-30

    In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

  3. Reliability-based design optimization for flexible mechanism with particle swarm optimization and advanced extremum response surface method

    Institute of Scientific and Technical Information of China (English)

    张春宜; 宋鲁凯; 费成巍; 郝广平; 刘令君

    2016-01-01

    To improve the computational efficiency of the reliability-based design optimization (RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method (PSO-AERSM) was proposed by integrating particle swarm optimization (PSO) algorithm and advanced extremum response surface method (AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.

  4. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

    Science.gov (United States)

    Li, Xuejun; Xu, Jia; Yang, Yun

    2015-01-01

    Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

  5. PID Controller Design of Nonlinear System using a New Modified Particle Swarm Optimization with Time-Varying Constriction Coefficient

    Directory of Open Access Journals (Sweden)

    Alrijadjis .

    2014-12-01

    Full Text Available The proportional integral derivative (PID controllers have been widely used in most process control systems for a long time. However, it is a very important problem how to choose PID parameters, because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for nonlinear systems. In this paper, a new modified particle swarm optimization (PSO is presented to search for optimal PID parameters for such system. The proposed algorithm is to modify constriction coefficient which is nonlinearly decreased time-varying for improving the final accuracy and the convergence speed of PSO. To validate the control performance of the proposed method, a typical nonlinear system control, a continuous stirred tank reactor (CSTR process, is illustrated. The results testify that a new modified PSO algorithm can perform well in the nonlinear PID control system design in term of lesser overshoot, rise-time, settling-time, IAE and ISE. Keywords: PID controller, Particle Swarm Optimization (PSO,constriction factor, nonlinear system.

  6. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

    Directory of Open Access Journals (Sweden)

    Xuejun Li

    2015-01-01

    Full Text Available Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO and Particle Swarm Optimization (PSO have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

  7. Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

    Directory of Open Access Journals (Sweden)

    Xiao Fu

    2013-01-01

    Full Text Available Quantum-behaved particle swarm optimization (QPSO algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.

  8. Swarm of bees and particles algorithms in the problem of gradual failure reliability assurance

    Directory of Open Access Journals (Sweden)

    M. F. Anop

    2015-01-01

    Full Text Available Probability-statistical framework of reliability theory uses models based on the chance failures analysis. These models are not functional and do not reflect relation of reliability characteristics to the object performance. At the same time, a significant part of the technical systems failures are gradual failures caused by degradation of the internal parameters of the system under the influence of various external factors.The paper shows how to provide the required level of reliability at the design stage using a functional model of a technical object. Paper describes the method for solving this problem under incomplete initial information, when there is no information about the patterns of technological deviations and degradation parameters, and the considered system model is a \\black box" one.To this end, we formulate the problem of optimal parametric synthesis. It lies in the choice of the nominal values of the system parameters to satisfy the requirements for its operation and take into account the unavoidable deviations of the parameters from their design values during operation. As an optimization criterion in this case we propose to use a deterministic geometric criterion \\reliability reserve", which is the minimum distance measured along the coordinate directions from the nominal parameter value to the acceptability region boundary rather than statistical values.The paper presents the results of the application of heuristic swarm intelligence methods to solve the formulated optimization problem. Efficiency of particle swarm algorithms and swarm of bees one compared with undirected random search algorithm in solving a number of test optimal parametric synthesis problems in three areas: reliability, convergence rate and operating time. The study suggests that the use of a swarm of bees method for solving the problem of the technical systems gradual failure reliability ensuring is preferred because of the greater flexibility of the

  9. 改进粒子群算法在水库优化调度中的应用%Application of Improved Particle Swarm Optimization Algorithm in Optimal Operation of Reservoir

    Institute of Scientific and Technical Information of China (English)

    丁根宏; 曹文秀

    2014-01-01

    The optimal operation model for flood control of reservoir is a high-dimensional multimodal extremum problem in gen-eral, and intelligent optimization algorithm is usual y used to solve such problems. Part icle swarm opt imization ( PSO) algorithm is widely used in the optimal operation of reservoir due to its simplicit y; however, there are shortcom ings in the PSO algorithm such as premature convergence, low efficiency in global convergence, and deficiency in local search capabilit y. Logistic equation and mut at ion operator are introduced to increase the diversity of population and convergence factor is introduced to improve the convergence rate during the iterative process. The improved PSO algorithm w as applied in the optimal operation for flood con-trol of the Dongzhen Reservoir and Mulanxi Watershed. The resulted showed that the maximum w at er level is 6. 35 m and the maximum flow rate is 959. 2 m3 / s at the pivotal w atercourse. The solutions were much better than that obtained from the pres-ent reservoir control regulations ( maximum w ater level of 6. 93 m and maximum flow rate of 1 139. 5 m3 / s) and that determined by the standard PSO algorithm ( maximum water level of 6. 51 m and maximum flow rate of 1 066. 3 m3 / s) .%水库防洪优化调度模型一般属于高维多峰极值问题,通常采用智能优化算法加以求解。粒子群算法由于其简单易行被广泛应用于水库优化调度中,但是该算法存在局部搜索能力不足、早熟收敛、全局收敛性差等问题。针对这些问题,通过引入 Logistic 方程和变异算子来提高种群的多样性,采用收敛因子来提高算法的收敛速度,并将改进的粒子群算法应用到东圳水库与木兰溪流域的防洪优化调度中,求得关键处河道的最高水位为6.35 m,最大流量为959.2 m3/ s。这一结果与现行规则下的运行结果(最高水位6.93 m,最大流量1139.5 m3/ s)和常规粒子群算法计算结果(最高水位6.51 m

  10. Optimization of Gain, Impedance, and Bandwidth of Yagi-Uda Array Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Munish Rattan

    2008-01-01

    Full Text Available Particle swarm optimization (PSO is a new, high-performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It is a global optimization technique-like genetic algorithm (GA but has less computational cost compared to GA. In this paper, PSO has been used to optimize the gain, impedance, and bandwidth of Yagi-Uda array. To evaluate the performance of designs, a method of moments code NEC2 has been used. The results are comparable to those obtained using GA.

  11. Optimal control of switched linear systems based on Migrant Particle Swarm Optimization algorithm

    Science.gov (United States)

    Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng

    2009-10-01

    The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.

  12. Application of Fuzzy C-Means Clustering Algorithm Based on Particle Swarm Optimization in Computer Forensics

    Science.gov (United States)

    Wang, Deguang; Han, Baochang; Huang, Ming

    Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.

  13. Image Filtering using All Neighbor Directional Weighted Pixels: Optimization using Particle Swarm Optimization

    CERN Document Server

    Mandal, J K

    2012-01-01

    In this paper a novel approach for de noising images corrupted by random valued impulses has been proposed. Noise suppression is done in two steps. The detection of noisy pixels is done using all neighbor directional weighted pixels (ANDWP) in the 5 x 5 window. The filtering scheme is based on minimum variance of the four directional pixels. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) has also been used for searching the parameters of detection and filtering operators required for optimal performance. Results obtained shows better de noising and preservation of fine details for highly corrupted images.

  14. Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

    Science.gov (United States)

    Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

    2010-06-01

    This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

  15. Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell (PEMFC)

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO receding optimization applied to the PEMFC predictive control yielded good performance.

  16. Application of Advanced Particle Swarm Optimization Techniques to Wind-thermal Coordination

    DEFF Research Database (Denmark)

    Singh, Sri Niwas; Østergaard, Jacob; Yadagiri, J.

    2009-01-01

    wind-thermal coordination algorithm is necessary to determine the optimal proportion of wind and thermal generator capacity that can be integrated into the system. In this paper, four versions of Particle Swarm Optimization (PSO) techniques are proposed for solving wind-thermal coordination problem....... A pseudo code based algorithm is suggested to deal with the equality constraints of the problem for accelerating the optimization process. The simulation results show that the proposed PSO methods are capable of obtaining higher quality solutions efficiently in wind-thermal coordination problems....

  17. Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.

    Science.gov (United States)

    Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee

    2014-10-01

    Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

  18. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

    Directory of Open Access Journals (Sweden)

    Francisco S. de Albuquerque Filho

    2013-01-01

    Full Text Available This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

  19. Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid approximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimization (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.

  20. A particle-swarm-based approach of power system stability enhancement with unified power flow controller

    Energy Technology Data Exchange (ETDEWEB)

    Al-Awami, Ali T.; Abido, M.A. [Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Box 784 (Saudi Arabia); Abdel-Magid, Y.L. [Electrical Engineering Program, The Petroleum Institute, P.O. Box 2533, Abu Dhabi (United Arab Emirates)

    2007-03-15

    The use of the supplementary controllers of a unified power flow controller (UPFC) to damp low frequency oscillations in a weakly connected system is investigated. The potential of the UPFC supplementary controllers to enhance the dynamic stability is evaluated by measuring the electromechanical controllability through singular value decomposition (SVD) analysis. Individual designs of the UPFC controllers and power system stabilizer (PSS) using particle-swarm optimization (PSO) technique are discussed. The effectiveness of the proposed controllers on damping low frequency oscillations is tested through eigenvalue analysis and non-linear time simulation. (author)

  1. Control of the Bed Temperature of a Circulating Fluidized Bed Boiler by using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    AYGUN, H.

    2012-05-01

    Full Text Available Circulating fluidized bed boilers are increasingly used in the power generation due to their higher combustion efficiency and lower pollutant emissions. Such boilers require an effective control of the bed temperature, because it influences the boiler combustion efficiency and the rate of harmful emissions. A Particle-Swarm-Optimization-Proportional-Integrative-Derivative (PSO-PID controller for the bed temperature of a circulating fluidized bed boiler is presented. In order to prove the capability of the proposed controller, its performances are compared at different boiler loads with those of a Fuzzy Logic (FL controller. The simulation results demonstrate some advantages of the proposed controller.

  2. Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

    Directory of Open Access Journals (Sweden)

    Alma Y. Alanis

    2013-01-01

    Full Text Available This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN trained with a novel algorithm based on extended Kalman filter (EKF and particle swarm optimization (PSO, using an online series-parallel con…figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.

  3. A particle swarm model for estimating reliability and scheduling system maintenance

    Science.gov (United States)

    Puzis, Rami; Shirtz, Dov; Elovici, Yuval

    2016-05-01

    Modifying data and information system components may introduce new errors and deteriorate the reliability of the system. Reliability can be efficiently regained with reliability centred maintenance, which requires reliability estimation for maintenance scheduling. A variant of the particle swarm model is used to estimate reliability of systems implemented according to the model view controller paradigm. Simulations based on data collected from an online system of a large financial institute are used to compare three component-level maintenance policies. Results show that appropriately scheduled component-level maintenance greatly reduces the cost of upholding an acceptable level of reliability by reducing the need in system-wide maintenance.

  4. Optimization of Determinant Factors of Satellite Electrical Power System with Particle Swarm Optimization (PSO Algorithm

    Directory of Open Access Journals (Sweden)

    Mojtaba Biglarahmadi

    2014-03-01

    Full Text Available Weight and dimension, cost, and performance are determinant factors for design, fabrication, and launch the satellites which are related to the mission type of the satellites. Each satellite includes several subsystems such as Electrical Power Subsystem (EPS, Navigation Subsystem, Thermal Subsystem, etc. The purpose of this paper is to optimize these determinant factors by Particle Swarm Optimization (PSO algorithm, for Electrical Power Subsystem. This paper considers the effects of selecting various types of Photovoltaic (PV cells and batteries on weight and dimension, cost, and performance of the satellite. We have used two various types of PVs and two various type of batteries in optimization of the Electrical Power Subsystem (EPS

  5. Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Shuang Zhao

    2015-01-01

    Full Text Available Task resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO algorithms with adaptive change of parameter (viz., inertial weight and acceleration coefficients according to the evolution state evaluation is presented. This adaptation helps to avoid premature convergence and explore the search space more efficiently. Simulations are carried out to test proposed algorithm, test reveal that the algorithm can achieving significant optimization of makespan.

  6. Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

    DEFF Research Database (Denmark)

    Soares, Joao; Vale, Zita; Canizes, Bruno

    2013-01-01

    This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aimi...... calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method....

  7. A LINEAR PRECODING STRATEGY BASED ON PARTICLE SWARM OPTIMIZATION IN MULTICELL COOPERATIVE TRANSMISSION

    Institute of Scientific and Technical Information of China (English)

    Zhang Rui; Song Rongfang

    2011-01-01

    An optimal linear precoding scheme based on Particle Swarm Optimization (PSO),which aims to maximize the system capacity of the cooperative transmission in the downlink channel,is proposed for a multicell multiuser single input single output system.With such a scheme,the optimal precoding vector could be easily searched for each user according to a simplified objective function.Simulation results show that the proposed scheme can obtain larger average spectrum efficiency and a better Bit Error Rate (BER) performance than Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) algorithm.

  8. A Survey on Particle Swarm Optimization for Use in Distributed Generation Placement and Sizing

    Directory of Open Access Journals (Sweden)

    Arif Syed Muhammad

    2016-01-01

    Full Text Available This paper surveys the research and development of Particle Swarm Optimization (PSO algorithm for use in selecting a suitable position for Distributed Generation (DG units within a distribution network. Our discussion first covers the algorithm development of PSO and its use in neural networks. After establishing the foundations of PSO, we then explore its use in sizing and sitting of DG units in distribution network. Combining PSO with other optimization techniques for attaining better results is also discussed in this paper.

  9. Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Xunlin Jiang

    2013-01-01

    Full Text Available Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN and particle swarm optimization (PSO. A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.

  10. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Na Tian

    2015-01-01

    Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

  11. Bluetooth Based Chaos Synchronization Using Particle Swarm Optimization and Its Applications to Image Encryption

    Directory of Open Access Journals (Sweden)

    Tzu-Hsiang Hung

    2012-06-01

    Full Text Available This study used the complex dynamic characteristics of chaotic systems and Bluetooth to explore the topic of wireless chaotic communication secrecy and develop a communication security system. The PID controller for chaos synchronization control was applied, and the optimum parameters of this PID controller were obtained using a Particle Swarm Optimization (PSO algorithm. Bluetooth was used to realize wireless transmissions, and a chaotic wireless communication security system was developed in the design concept of a chaotic communication security system. The experimental results show that this scheme can be used successfully in image encryption.

  12. Bluetooth based chaos synchronization using particle swarm optimization and its applications to image encryption.

    Science.gov (United States)

    Yau, Her-Terng; Hung, Tzu-Hsiang; Hsieh, Chia-Chun

    2012-01-01

    This study used the complex dynamic characteristics of chaotic systems and Bluetooth to explore the topic of wireless chaotic communication secrecy and develop a communication security system. The PID controller for chaos synchronization control was applied, and the optimum parameters of this PID controller were obtained using a Particle Swarm Optimization (PSO) algorithm. Bluetooth was used to realize wireless transmissions, and a chaotic wireless communication security system was developed in the design concept of a chaotic communication security system. The experimental results show that this scheme can be used successfully in image encryption.

  13. Optimization of hydrofoil for tidal current turbine based on particle swarm optimization and computational fluid dynamic method

    Directory of Open Access Journals (Sweden)

    Zhang De-Sheng

    2016-01-01

    Full Text Available Both efficiency and cavitation performance of the hydrofoil are the key technologies to design the tidal current turbine. In this paper, the hydrofoil efficiency and lift coefficient were improved based on particle swarm optimization method and XFoil codes. The cavitation performance of the optimized hydrofoil was also discussed by the computational fluid dynamic. Numerical results show the efficiency of the optimized hydrofoil was improved 11% ranging from the attack angle of 0-7° compared to the original NACA63-818 hydrofoil. The minimum pressure on leading edge of the optimized hydrofoil dropped above 15% at the high attack angle conditions of 10°, 15°, and 20°, respectively, which is benefit for the hydrofoil to avoiding the cavitation.

  14. Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Li Ran

    2017-01-01

    Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.

  15. A particle swarm approach to solve vehicle routing problem with uncertain demand: A drug distribution case study

    Directory of Open Access Journals (Sweden)

    Babak Farhang Moghadam

    2010-07-01

    Full Text Available During the past few years, there have tremendous efforts on improving the cost of logistics using varieties of Vehicle Routing Problem (VRP models. In fact, the recent rise on fuel prices has motivated many to reduce the cost of transportation associated with their business through an improved implementation of VRP systems. We study a specific form of VRP where demand is supposed to be uncertain with unknown distribution. A Particle Swarm Optimization (PSO is proposed to solve the VRP and the results are compared with other existing methods. The proposed approach is also used for real world case study of drug distribution and the preliminary results indicate that the method could reduce the unmet demand significantly.

  16. A New Mutated Quantum-Behaved Particle Swarm Optimizer for Digital IIR Filter Design

    Directory of Open Access Journals (Sweden)

    Wenbo Xu

    2009-01-01

    Full Text Available Adaptive infinite impulse response (IIR filters have shown their worth in a wide range of practical applications. Because the error surface of IIR filters is multimodal in most cases, global optimization techniques are required for avoiding local minima. In this paper, we employ a global optimization algorithm, Quantum-behaved particle swarm optimization (QPSO that was proposed by us previously, and its mutated version in the design of digital IIR filter. The mechanism in QPSO is based on the quantum behaviour of particles in a potential well and particle swarm optimization (PSO algorithm. QPSO is characterized by fast convergence, good search ability, and easy implementation. The mutated QPSO (MuQPSO is proposed in this paper by using a random vector in QPSO to increase the randomness and to enhance the global search ability. Experimental results on three examples show that QPSO and MuQPSO are superior to genetic algorithm (GA, differential evolution (DE algorithm, and PSO algorithm in quality, convergence speed, and robustness.

  17. Dynamic Network Traffic Flow Prediction Model based on Modified Quantum-Behaved Particle Swarm Optimization

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    Hongying Jin

    2013-10-01

    Full Text Available This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow prediction process, and data collecting module can collect and return the data through the destination device. Thirdly, the dynamic network traffic flow prediction model is implemented based on BP Neural Network. Particularly, in this paper, the BP Neural Network is trained by a modified quantum-behaved particle swarm optimization(QPSO. We modified the QPSO by utilizing chaos signals to implement typical logistic mapping and pursuing the fitness function of a particle by a set of optimal parameters. Afterwards, based on the above process, dynamic network traffic flow prediction model is illustrated. Finally, a series of experiments are conduct to make performance evaluation, and related analyses for experimental results are also given

  18. Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

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    Fereydoun Naghibi

    2016-12-01

    Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was

  19. Slow light performance enhancement of Bragg slot photonic crystal waveguide with particle swarm optimization algorithm

    Science.gov (United States)

    Abedi, Kambiz; Mirjalili, Seyed Mohammad

    2015-03-01

    Recently, majority of current research in the field of designing Phonic Crystal Waveguides (PCW) focus in extracting the relations between output slow light properties of PCW and structural parameters through a huge number of tedious non-systematic simulations in order to introduce better designs. This paper proposes a novel systematic approach which can be considered as a shortcut to alleviate the difficulties and human involvements in designing PCWs. In the proposed method, the problem of PCW design is first formulated as an optimization problem. Then, an optimizer is employed in order to automatically find the optimum design for the formulated PCWs. Meanwhile, different constraints are also considered during optimization with the purpose of applying physical limitations to the final optimum structure. As a case study, the structure of a Bragg-like Corrugation Slotted PCWs (BCSPCW) is optimized by using the proposed method. One of the most computationally powerful techniques in Computational Intelligence (CI) called Particle Swarm Optimization (PSO) is employed as an optimizer to automatically find the optimum structure for BCSPCW. The optimization process is done by considering five constraints to guarantee the feasibility of the final optimized structures and avoid band mixing. Numerical results demonstrate that the proposed method is able to find an optimum structure for BCSPCW with 172% and 100% substantial improvements in the bandwidth and Normalized Delay-Bandwidth Product (NDBP) respectively compared to the best current structure in the literature. Moreover, there is a time domain analysis at the end of the paper which verifies the performance of the optimized structure and proves that this structure has low distortion and attenuation simultaneously.

  20. Application of particle swarm optimization to parameter search in dynamical systems

    Science.gov (United States)

    Matsushita, Haruna; Saito, Toshimichi

    This paper proposes an application of the particle swarm optimization (PSO) to analysis of switched dynamical systems (SDS). This is the first application of PSO to bifurcation analysis. We consider the application to an example of the SDS which relates to a simplified model of photovoltaic systems such that the input is a single solar cell and is converted to the output via a boost converter. Our SDS includes a piecewise linear current-controlled voltage source that is a simplified model of the solar cell and the switching rule is a variant of peak-current-controlled switching. We derive two equations that give period-doubling bifurcation set and the maximum power point (MPP) for the parameter: they are objective of the analysis. The two equations are transformed into an multi objective problem (MOP) described by the hybrid fitness function consisting of two functions evaluating the validity of parameters and criteria. The proposed method permits increase (deteriorate) of some component below the criterion and the increase can help to exclude the bad component. This criteria effect helps an improvement of trade-off problems in existing MOP solvers. Furthermore, by using the piecewise exact solution and return map for the simulation, the MOP is described exactly and the PSO can find the precise (approximate) solution. From simulation results, we confirm that the PSO for the MOP can easily find the solution parameters although a standard numerical calculation needs huge calculation amount. The efficiency of the proposed algorithm is confirmed by measuring in terms of accuracy, computation amount and robustness.