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.
Particle Swarm Optimization Toolbox
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
Directory of Open Access Journals (Sweden)
Dongqi Liu
2016-03-01
Full Text Available This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable EV units with wind farms and thermal plants is proposed. A multi-objective particle swarm optimization (MOPSO algorithm and a fuzzy decision maker are put forward for the simultaneous optimization of grid operating cost, CO2 emissions, wind curtailment, and EV users’ cost. Simulations are done in a 30 node system containing three traditional thermal plants, two carbon capture and storage (CCS thermal plants, two wind farms, and six EV aggregations. Contrast of strategies under different EV charging/discharging price is also discussed. The results are presented to prove the effectiveness of the proposed strategy.
Dujko, S; White, R D; Petrović, Z Lj; Robson, R E
2010-04-01
A multiterm solution of the Boltzmann equation has been developed and used to calculate transport coefficients of charged-particle swarms in gases under the influence of electric and magnetic fields crossed at arbitrary angles when nonconservative collisions are present. The hierarchy resulting from a spherical-harmonic decomposition of the Boltzmann equation in the hydrodynamic regime is solved numerically by representing the speed dependence of the phase-space distribution function in terms of an expansion in Sonine polynomials about a Maxwellian velocity distribution at an internally determined temperature. Results are given for electron swarms in certain collisional models for ionization and attachment over a range of angles between the fields and field strengths. The implicit and explicit effects of ionization and attachment on the electron-transport coefficients are considered using physical arguments. It is found that the difference between the two sets of transport coefficients, bulk and flux, resulting from the explicit effects of nonconservative collisions, can be controlled either by the variation in the magnetic field strengths or by the angles between the fields. In addition, it is shown that the phenomena of ionization cooling and/or attachment cooling/heating previously reported for dc electric fields carry over directly to the crossed electric and magnetic fields. The results of the Boltzmann equation analysis are compared with those obtained by a Monte Carlo simulation technique. The comparison confirms the theoretical basis and numerical integrity of the moment method for solving the Boltzmann equation and gives a set of well-established data that can be used to test future codes and plasma models. PMID:20481843
Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization
Liyong Niu; Di Zhang
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 cha...
A Modified Particle Swarm Optimization Algorithm
Jie He; Hui Guo
2013-01-01
In optimizing the particle swarm optimization (PSO) that inevitable existence problem of prematurity and the local convergence, this paper base on this aspects is put forward a kind of modified particle swarm optimization algorithm, take the gradient descent method (BP algorithm) as a particle swarm operator embedded in particle swarm algorithm, and at the same time use to attenuation wall (Damping) approach to make fly off the search area of the particles of size remain unchanged and avoid t...
Particle Swarm Optimization with Flexible Swarm for Unconstrained Optimization
Kahramanlı, Humar; Allahverdi, Novruz
2013-01-01
Particle Swarm Optimization (PSO) algorithm inspired from behavior of bird flocking and fish schooling. It is well-known algorithm which has been used in many areas successfully. However it sometimes suffers from premature convergence. In resent year’s researches have been introduced a various approaches to avoid of this problem. This paper presents the particle swarm optimization algorithm with flexible swarm (PSO-FS). The new algorithm was evaluated on 14 functions often used to benchmark t...
Particle Swarm Optimization with Double Learning Patterns
Yuanxia Shen; Linna Wei; Chuanhua Zeng; Jian Chen
2015-01-01
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...
Particle Swarms in Statistical Physics
Bautu, Andrei; Bautu, Elena
2009-01-01
This chapter presented the basic traits of Particle Swarm Optimization and its applications for some well known problems in Statistical Physics. Recent research results presented in the literature for these problems prove that PSO can find high quality solutions in reasonable times (Butu et al, 2007; Butu & Butu, 2008). However, many questions are still open: how do the parameters setups relate to the problems tackled? how can we improve the basic PSO to get state of the are results? how can ...
Particle Swarm Optimization with Double Learning Patterns.
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. PMID:26858747
Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm
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...
Particle swarm genetic algorithm and its application
International Nuclear Information System (INIS)
To solve the problems of slow convergence speed and tendency to fall into the local optimum of the standard particle swarm optimization while dealing with nonlinear constraint optimization problem, a particle swarm genetic algorithm is designed. The proposed algorithm adopts feasibility principle handles constraint conditions and avoids the difficulty of penalty function method in selecting punishment factor, generates initial feasible group randomly, which accelerates particle swarm convergence speed, and introduces genetic algorithm crossover and mutation strategy to avoid particle swarm falls into the local optimum Through the optimization calculation of the typical test functions, the results show that particle swarm genetic algorithm has better optimized performance. The algorithm is applied in nuclear power plant optimization, and the optimization results are significantly. (authors)
Particle Swarm Transport in Fracture Networks
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
Particle Swarm Optimization and Genetic Algorithms
Elisa Valentina Oneţ
2009-01-01
This paper presents two evolutionary computation techniques: particle swarm optimization – part of swarm intelligence and genetic algorithms – part of the evolutionary algorithms. The basic algorithm for each is reviewed, in case of optimization problems in asearch space. It is presented how each evolutionary computation technique works, and the way in which features from one can be included into the other.
Selectively-informed particle swarm optimization
Yang Gao; Wenbo Du; Gang Yan
2015-01-01
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...
Particle swarm optimization for unsupervised robotic learning
Pugh, Jim; Zhang, Yizhen; Martinoli, Alcherio
2005-01-01
We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate the performance of both the original algorithmand the noise-resistantmethod for several numerical problems with added noise, as well as unsupervised learning of obstacle avoidance using one or more robots.
Multiswarm Particle Swarm Optimization with Transfer of the Best Particle
Xiao-peng Wei; Jian-xia Zhang; Dong-sheng Zhou; Qiang Zhang
2015-01-01
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 des...
An Improved Adaptive Dynamic Particle Swarm Optimization Algorithm
Hongbo Zhao; Lina Feng
2014-01-01
In order to overcome the weakness that particle swarm optimization algorithm is likely to fall into local minimum when the complex optimization problems are solved, a new adaptive dynamic particle swarm optimization algorithm is proposed. The paper introduces the evaluation index of particle swarm premature convergence to judge the state of particle swarm in the population space, for the sake of investigates the timing of taking effect of influence function. The influence function is adaptive...
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......, in problems with many local optima, such focus often leads to premature convergence that precludes reaching the intended objective. To remedy this problem in certain types of domains, this paper introduces Novelty-driven Particle Swarm Optimization (NdPSO), which is motivated by the novelty search algorithm...... in genetic programming, this paper implements NdPSO as an extension of the grammatical swarm method, which combines PSO with genetic programming. The resulting NdPSO implementation is tested in three different domains representative of those in which it might provide advantage over objective-driven PSO...
Improvement of Interior Ballistic Performance Utilizing Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
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.
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization
Yanhua Zhong; Changqing Yuan
2012-01-01
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 ...
Improved particle swarm optimization combined with chaos
International Nuclear Information System (INIS)
As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality
Heart Beat Classification Using Particle Swarm Optimization
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Ali Khazaee
2013-05-01
Full Text Available This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.
The cellular particle swarm optimization algorithm
International Nuclear Information System (INIS)
This work presents a variant of the Particle Swarm Optimization (PSO) original algorithm, the Cellular-PSO. Inspired by the cellular Genetic Algorithm (GA), particles in Cellular-PSO are arranged into a matrix of cells interconnected according to a given topology. Such topology defines particle's neighborhood, inside which social adaptation may occur. As a consequence, population diversity is increased and the optimization process becomes more efficient and robust. The proposed Cellular-PSO has been applied to the nuclear reactor core design optimization problem and comparative experiments demonstrated that it is superior to the standard PSO. (author)
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.
Particle Swarm Optimization Based Source Seeking
Zou, Rui; Kalivarapu, Vijay; Winer, Eliot; Oliver, James; Bhattacharya, Sourabh
2015-01-01
Signal source seeking using autonomous vehicles is a complex problem. The complexity increases manifold when signal intensities captured by physical sensors onboard are noisy and unreliable. Added to the fact that signal strength decays with distance, noisy environments make it extremely difficult to describe and model a decay function. This paper addresses our work with seeking maximum signal strength in a continuous electromagnetic signal source with mobile robots, using Particle Swarm Opti...
Emitter Location Finding using Particle Swarm Optimization
Kaya, I; Yazgan, A.; Cakir, O.; Tugcu, E.
2014-01-01
Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Resu...
SENSOR DEPLOYMENT USING PARTICLE SWARM OPTIMIZATION
NIKITHA KUKUNURU,; BABU RAO THELLA,; RAJYA LAKSHMI DAVULURI
2010-01-01
In Wireless Sensor Networks (WSN), sensors are randomly deployed in the sensor field which brings the coverage problem. It is a unique problem and in maximizing coverage, the sensors need to be placed in aposition such that the sensing capability of the network is fully utilized to ensure high quality of service. This can be achieved with minimum number of sensor nodes having maximum coverage in the network and the nodes are within the communication range. In this paper, particle swarm algori...
Monitoring of particle swarm optimization
Institute of Scientific and Technical Information of China (English)
Yuhui SHI; Russ EBERHART
2009-01-01
In this paper, several diversity measurements will be discussed and defined. As in other evolutionary algorithms, first the population position diversity will be discussed followed by the discussion and definition of population velocity diversity which is different from that in other evolutionary algorithms since only PSO has the velocity parameter. Furthermore, a diversity measurement called cognitive diversity is discussed and defined, which can reveal clustering information about where the current population of particles intends to move towards. The diversity of the current population of particles and the cognitive diversity together tell what the convergence/divergence stage the current population of particles is at and which stage it moves towards.
Hybrid Particle Swarm Optimization for Regression Testing
Directory of Open Access Journals (Sweden)
Dr. Arvinder Kaur
2011-05-01
Full Text Available Regression Testing ensures that any enhancement made to software will not affect specified functionality of software. The execution of all test cases can be long and complex to run; this makes it a costlier process. The prioritization of test cases can help in reduction in cost of regression testing, as it is inefficient to re- run each and every test case. In this research paper, the criterion considered is of maximum fault coverage in minimum execution time. In this research paper, the Hybrid Particle Swarm Optimization (HPSO algorithm has been used, to make regression testing efficient. The HPSO is acombination of Particle Swarm Optimization (PSO technique and Genetic Algorithms (GA, to widen the search space for the solution. The Genetic Algorithm (GA operators provides optimized way to performprioritization in regression testing and on blending it with Particle Swarm Optimization (PSO technique makes it effective and provides fast solution. The Genetic Algorithm (GA operator that has been used is Mutation operator which allows the search engine to evaluate all aspects of the search space. Here, AVERAGE PERCENTAGE OF FAULTS DETECTED (APFD metric has been used to represent the solution derived from HPSO for better transparency in proposed algorithm.
Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation
Maeda, Yutaka; Matsushita, Naoto
2009-01-01
In this paper, we presented hardware implementation of the particle swarm optimization algorithm which is combination of the ordinary particle swarm optimization and the simultaneous perturbation method. FPGA is used to realize the system. This algorithm utilizes local information of objective function effectively without lack of advantage of the original particle swarm optimization. Moreover, the FPGA implementation gives higher operation speed effectively using parallelism of the particle s...
SENSOR DEPLOYMENT USING PARTICLE SWARM OPTIMIZATION
Directory of Open Access Journals (Sweden)
NIKITHA KUKUNURU,
2010-10-01
Full Text Available In Wireless Sensor Networks (WSN, sensors are randomly deployed in the sensor field which brings the coverage problem. It is a unique problem and in maximizing coverage, the sensors need to be placed in aposition such that the sensing capability of the network is fully utilized to ensure high quality of service. This can be achieved with minimum number of sensor nodes having maximum coverage in the network and the nodes are within the communication range. In this paper, particle swarm algorithm was used to find the optimal positions of the sensors to determine the best coverage. This algorithm is an optimization technique which belongs to the fertile paradigm of swarm intelligence. It is a derivative free and is a very efficient global search algorithm with few algorithm parameters. Here, results are presented which shows that, PSO has good effect in solving coverage problem.
Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer
Yu-Jun Zheng; Hai-Feng Ling; Qiu Guan
2012-01-01
Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during ...
Chaotic Particle Swarm Optimization with Mutation for Classification
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 th...
NEW BINARY PARTICLE SWARM OPTIMIZATION WITH IMMUNITY-CLONAL ALGORITHM
Dina EL-Gammal; Amr Badr; Mostafa Abd El Azeim
2013-01-01
Particle Swarm Optimization used to solve a continuous problem and has been shown to perform well however, binary version still has some problems. In order to solve these problems a new technique called New Binary Particle Swarm Optimization using Immunity-Clonal Algorithm (NPSOCLA) is proposed This Algorithm proposes a new updating strategy to update the position vector in Binary Particle Swarm Optimization (BPSO), which further combined with Immunity-Clonal Algorithm to improve the optimiza...
Visualization of particle swarm optimization on mobile platform
Kojić, Aleksandar
2012-01-01
The aim of the thesis is a presentation of particle swarm optimization on mobile operating system Android. First we describe the structure of the operating system Android and Android applications. We present the main features of particle swarm optimization, where it is found in the nature and how it is used in computer science. The second part of the thesis presents visualization of particle swarm optimization. We present an analysis, planning, developement and testing of the application.
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization
2014-01-01
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 opt...
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms
Garro, Beatriz A.; Roberto A. Vázquez
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 algori...
Lagrange Interpolation Learning Particle Swarm Optimization.
Kai, Zhang; Jinchun, Song; Ke, Ni; Song, Li
2016-01-01
In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles' diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle's historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO's comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence. PMID:27123982
Unit Commitment by Adaptive Particle Swarm Optimization
Saber, Ahmed Yousuf; Senjyu, Tomonobu; Miyagi, Tsukasa; Urasaki, Naomitsu; Funabashi, Toshihisa
This paper presents an Adaptive Particle Swarm Optimization (APSO) for Unit Commitment (UC) problem. APSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable size and load demand, adaptive criteria are applied on PSO parameters and the global best particle (knowledge) based on the diversity of fitness. In this proposed method, PSO parameters are automatically adjusted using Gaussian modification. To increase the knowledge, the global best particle is updated instead of a fixed one in each generation. To avoid the method to be frozen, idle particles are reset. The real velocity is digitized (0/1) by a logistic function for binary UC. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method.
Particle swarm optimisation based video abstraction
Directory of Open Access Journals (Sweden)
Magda B. Fayk
2010-04-01
Full Text Available Video abstraction is a basic step for intelligent access to video and multimedia databases which facilitates content-based video indexing, retrieving and browsing. This paper presents a new video abstraction scheme. The proposed method relies on two stages. First, video is divided into short segments. Second, keyframes in each segment are selected using particle swarm optimisation. A group of experiments show that the proposed technique is promising in regards to selecting the most significant keyframes despite a sustainment in overhead processing.
Dynamic Spectrum Sensing Through Accelerated Particle Swarm Optimization
Paschos, Alexandros E.; Kapinas, Vasileios M.; Hadjileontiadis, Leontios J.; Karagiannidis, George K.
2015-01-01
A novel optimization algorithm, called accelerated particle swarm optimization (APSO), is proposed for dynamic spectrum sensing in cognitive radio networks. While modified swarm-based optimization algorithms focus on slight variations of the standard mathematical formulas, in APSO, the acceleration variable of the particles in the swarm is also considered in the search space of the optimization problem. We show that the proposed APSO-based dynamic spectrum sensing technique is more efficient ...
Software Project Scheduling Management by Particle Swarm Optimization
Dinesh B. Hanchate; Rajankumar S. Bichkar
2014-01-01
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 si...
Multiobjective Particle Swarm Optimization Based on PAM and Uniform Design
Xiaoshu Zhu; Jie Zhang; Junhong Feng
2015-01-01
In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering algorithm and uniform design are respectively introduced to maintain the diversity of Pareto optimal solutions and the uniformity of the selected Pareto optimal solutions. In this paper, a novel algorithm, the multiobjective particle swarm optimization based on PAM and uniform desi...
Swarms of particles settling under gravity in a viscous fluid
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.
Orientational hysteresis in swarms of active particles in external field
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...
SwarmViz: An Open-Source Visualization Tool for Particle Swarm Optimization
Jornod, Guillaume; Di Mario, Ezequiel Leonardo; Navarro, Inaki; Martinoli, Alcherio
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic for solving high dimensional optimization problems. Due to the large number of dimensions usually employed with PSO, it is not trivial to visualize and monitor the progress of the algorithm. Because of this, adjusting the parameters that govern the dynamics of the swarm for a specific problem becomes challenging. In this article, we present SwarmViz, an open-source visualization tool for PSO. Through SwarmViz, users are able to set up PSO ...
Discrete particle swarm optimization for the minimum labelling Steiner tree problem
Consoli, S.; Moreno-Pérez, JA; Darby-Dowman, K; Mladenović, N
2009-01-01
Particle Swarm Optimization is a population-based method inspired by the social behaviour of individuals inside swarms in nature. Solutions of the problem are modelled as members of the swarm which fly in the solution space. The improvement of the swarm is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of inertia and the attraction of the members who lead the swarm. This work focuses on a recent Discrete Particle Swarm Optimization for...
Particle Swarm Optimisation with Spatial Particle Extension
DEFF Research Database (Denmark)
Krink, Thiemo; Vesterstrøm, Jakob Svaneborg; Riget, Jacques
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 to...
A Novel Particle Swarm Optimization Algorithm for Global Optimization
Chun-Feng Wang; Kui Liu
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...
International Nuclear Information System (INIS)
The automotive industry has undergone significant development in recent years. While this has eased peoples' lives, it has created serious issues such as air pollution, an energy crisis and traffic. A low pollution engine is needed for the future. This paper presented a research study that proposed 3 objectives to improve vehicles in terms of brake specific fuel consumption (BSFC), nitrogen oxide (NOx), and smoke opacity. Engine control input was optimized using particle swarm optimization (PSO) and multi-objective particle swarm optimization (MOPSO). The optimal results of PSO were obtained and validated through laboratory engine testing. The paper discussed the approach, simulation experiments, and results. The study showed that the optimal result of PSO did not always improve BSFC, NOx, and smoke opacity together. Therefore a MOPSO approach was proposed. The selected optimal solutions fulfilling different goals will be validated in future laboratory engine tests. 6 refs., 5 tabs., 4 figs.
Cosmological parameter estimation using Particle Swarm Optimization
International Nuclear Information System (INIS)
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
Chaos embedded particle swarm optimization algorithms
International Nuclear Information System (INIS)
This paper proposes new particle swarm optimization (PSO) methods that use chaotic maps for parameter adaptation. This has been done by using of chaotic number generators each time a random number is needed by the classical PSO algorithm. Twelve chaos-embedded PSO methods have been proposed and eight chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of PSO and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, some of the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.
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.
The SVM Classifier Based on the Modified Particle Swarm Optimization
Liliya Demidova; Evgeny Nikulchev; Yulia Sokolova
2016-01-01
The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particles' {\\guillemotleft}regeneration{\\guillemotright} is put on the basis of the modified particle swarm ...
Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis
J. J. Jamian; Abdullah, M. N.; Mokhlis, H.; M. W. Mustafa; Bakar, A. H. A.
2014-01-01
The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through ...
Transport of Particle Swarms Through Variable Aperture Fractures
Boomsma, E.; Pyrak-Nolte, L. J.
2012-12-01
Particle transport through fractured rock is a key concern with the increased use of micro- and nano-size particles in consumer products as well as from other activities in the sub- and near surface (e.g. mining, industrial waste, hydraulic fracturing, etc.). While particle transport is often studied as the transport of emulsions or dispersions, particles may also enter the subsurface from leaks or seepage that lead to particle swarms. Swarms are drop-like collections of millions of colloidal-sized particles that exhibit a number of unique characteristics when compared to dispersions and emulsions. Any contaminant or engineered particle that forms a swarm can be transported farther, faster, and more cohesively in fractures than would be expected from a traditional dispersion model. In this study, the effects of several variable aperture fractures on colloidal swarm cohesiveness and evolution were studied as a swarm fell under gravity and interacted with the fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with (1) a uniform aperture, (2) a converging region followed by a uniform region (funnel shaped), (3) a uniform region followed by a diverging region (inverted funnel), and (4) a cast of a an induced fracture from a carbonate rock. All of the samples consisted of two blocks that measured 100 x 100 x 50 mm. The minimum separation between these blocks determined the nominal aperture (0.5 mm to 20 mm). During experiments a fracture was fully submerged in water and swarms were released into it. The swarms consisted of a dilute suspension of 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. The variation in fracture aperture controlled swarm behavior. Diverging apertures caused a sudden loss of confinement that resulted in a rapid change in the swarm's shape as well as a sharp increase in its velocity
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.
The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.
Mingo López, Fernando de; Gómez Blas, Nuria; Arteta Albert, Alberto
2012-01-01
Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performanc...
Kui-Ting CHEN; Yijun Dai; Ke Fan; Takaaki Baba
2015-01-01
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 th...
Extending Particle Swarm Optimisers with Self-Organized Criticality
DEFF Research Database (Denmark)
Løvbjerg, Morten; Krink, Thiemo
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....
Parameter estimation of nonlinear econometric models using particle swarm optimization
Mark P Wachowiak; Smolíková-Wachowiak, Renáta; Smolík, Dušan
2010-01-01
Global optimization is an essential component of econometric modeling. Optimization in econometrics is often difficult due to irregular cost functions characterized by multiple local optima. The goal of this paper is to apply a relatively new stochastic global technique, particle swarm optimization, to the well-known but difficult disequilibrium problem. Because of its co-operative nature and balance of local and global search, particle swarm is successful in optimizing the disequ...
Chaotically encoded particle swarm optimization algorithm and its applications
International Nuclear Information System (INIS)
This paper proposes a novel particle swarm optimization (PSO) algorithm, chaotically encoded particle swarm optimization algorithm (CENPSOA), based on the notion of chaos numbers that have been recently proposed for a novel meaning to numbers. In this paper, various chaos arithmetic and evaluation measures that can be used in CENPSOA have been described. Furthermore, CENPSOA has been designed to be effectively utilized in data mining applications.
Nonlinear Adaptive Filters based on Particle Swarm Optimization
Faten BEN ARFIA; Ben Messaoud, Mohamed; Abid, Mohamed
2009-01-01
This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
Entropy Diversity in Multi-Objective Particle Swarm Optimization
Eduardo J. Solteiro Pires; José A. Tenreiro Machado; Paulo B. de Moura Oliveira
2013-01-01
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 Sha...
Phishing Website Detection Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Radha Damodaram & Dr.M.L.Valarmathi
2011-12-01
Full Text Available Fake websites is the process of attracting people to visit fraudulent websites and making them toenter confidential data like credit-card numbers, usernames and passwords. We present a novelapproach to overcome the difficulty and complexity in detecting and predicting fake website. Thereis an efficient model which is based on using Association and classification Data Mining algorithmsoptimizing with PSO algorithm. These algorithms were used to characterize and identify all thefactors and rules in order to classify the phishing website and the relationship that correlate themwith each other. It also used MCAR classification algorithm to extract the phishing training data setscriteria to classify their legitimacy. After classification, those results have been optimized with AntColony Optimization (ACO algorithm. But, this work has limitations like Sequences of randomdecisions (not independent and Time to convergence uncertain in the phishing classification. So toovercome this limitation we enhance Particle Swarm Optimization (PSO which finds a solution toan optimization problem in a search space, or model and predict social behaviour in the presenceof phishing websites. This will improve the correctly classified phishing websites. The experimentalresults demonstrated the feasibility of using PSO technique in real applications and its betterperformance. This project employs the JAVA technology.
K. Lenin; B.Ravindranath Reddy; M. Surya Kalavathi
2013-01-01
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 Cooperative Multiple Particle Swarm Optimization (CMPSO) and Spatial Extended Particle Swarm Optimization (SEPSO) in trying to overcome the Problem of premature convergence. CMPSO and SEPSO are applied to Reactive Power Optimization problem and are evaluated on standard IEEE 30Bus System. The resu...
Composite Particle Swarm Optimizer With Historical Memory for Function Optimization.
Li, Jie; Zhang, JunQi; Jiang, ChangJun; Zhou, MengChu
2015-10-01
Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms. PMID:26390177
A Novel Particle Swarm Optimization Algorithm for Global Optimization.
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. PMID:26955387
Network Traffic Prediction based on Particle Swarm BP Neural Network
Yan Zhu; Guanghua Zhang; Jing Qiu
2013-01-01
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 part...
Video Superresolution via Parameter-Optimized Particle Swarm Optimization
2014-01-01
Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS...
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.
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
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...... by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid...
Individual Parameter Selection Strategy for Particle Swarm Optimization
Cai, Xingjuan; Cui, Zhihua; Zeng, Jianchao; Tan, Ying
2009-01-01
This chapter proposes a new model incorporated with the characteristic differences for each particle, and the individual selection strategy for inertia weight, cognitive learning factor and social learning factor are discussed, respectively. Simulation results show the individual selection strategy maintains a fast search speed and robust. Further research should be made on individual structure for particle swarm optimization.
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.
Adaptive Method of Particle Swarm Optimization for Multimodal Function
Directory of Open Access Journals (Sweden)
Jiantao GUO
2015-08-01
Full Text Available Multimodal optimization or finding more than one optimum is needed in many scientific and engineering application fields. Niching methods of particle swarm optimization has been successfully applied in many areas since it is a powerful, yet simple population based optimization strategy. A comprehensive analysis of neighborhood selection and information sharing mechanism was given from two aspects including topology technique and measure criterion. Moreover, most of existing niching particle swarm optimization algorithms which include SPSO, NichePSO, ARPSO, r2PSO, FER-PSO, ANPSO, FIPS-PSO, nbestPSO etc were described and compared. Furthermore, advantages and disadvantages existing in these algorithms were pointed out. Therefore, some ideas to improve the performance of niching particle swarm optimization algorithms were proposed
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.
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...
Nonlinear Adaptive Filters based on Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Faten BEN ARFIA
2009-07-01
Full Text Available This paper presents a particle swarm optimization (PSO algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
A dynamic inertia weight particle swarm optimization algorithm
International Nuclear Information System (INIS)
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly
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.
International Nuclear Information System (INIS)
This document is a basic course on charged particle sources for post-graduate students and thematic schools on large facilities and accelerator physics. A simple but precise description of the creation and the emission of charged particles is presented. This course relies on every year upgraded reference documents. Following relevant topics are considered: electronic emission processes, technological and practical considerations on electron guns, positron sources, production of neutral atoms, ionization, plasma and discharge, different types of positive and negative ion sources, polarized particle sources, materials for the construction of ion sources, low energy beam production and transport. (N.T.)
A Multi Swarm Particle Filter for Mobile Robot Localization
Directory of Open Access Journals (Sweden)
Ramazan Havangi
2010-05-01
Full Text Available Particle filter (PF is widely used in mobile robot localization, since it is suitable for the nonlinear non-Gaussian system. Localization based on PF, However, degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot looses its diversity. One of the main reasons for loosing particle diversity is sample impoverishment. It occurs when likelihood lies in the tail of the proposed distribution. In this case, most of particle weights are insignificant. To solve those problems, a novel multi swarm particle filter is presented. The multi swarm particle filter moves the samples towards region of the state space where the likelihood is significant, without allowing them to go far away from the region of significant values for the proposed distribution. The simulation results show the effectiveness of the proposed algorithm.
A dynamic global and local combined particle swarm optimization algorithm
International Nuclear Information System (INIS)
Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.
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.
Ziwen Sun; Li Tao; Xinyu Wang; Zhiping Zhou
2015-01-01
Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization localization issues in wireless sensor networks. The multiobjective functions consist of the space distance constraint and the geometric topology constraint. The optimal solution is found by multiobjective particle swarm optimization algorithm. Dynamic method is adopted to maintain t...
Automatized Parameterization of DFTB Using Particle Swarm Optimization.
Chou, Chien-Pin; Nishimura, Yoshifumi; Fan, Chin-Chai; Mazur, Grzegorz; Irle, Stephan; Witek, Henryk A
2016-01-12
We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented. PMID:26587758
Optimum multiuser detection in cdma using particle swarm algorithm
International Nuclear Information System (INIS)
In this work, a novel optimum multiuser detector (MUD) based on a particle swarm algorithm is presented. The proposed algorithm outperforms the matched filter and the decorrelator multiuser detectors. Moreover, the performance under near-far scenario, the system capacity, and the computational complexity of the proposed detector are also investigated. (author)
Global Optimization by Particle Swarm Method:A Fortran Program
Mishra, SK
2006-01-01
Programs that work very well in optimizing convex functions very often perform poorly when the problem has multiple local minima or maxima. They are often caught or trapped in the local minima/maxima. Several methods have been developed to escape from being caught in such local optima. The Particle Swarm Method of global optimization is one of such methods. A swarm of birds or insects or a school of fish searches for food, protection, etc. in a very typical manner. If one of the members ...
CriPS: Critical Dynamics in Particle Swarm Optimization
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...
Extraction of Satellite Image using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Harish Kundra, V.K.Panchal, Sagar Arora, Karandeep Singh, Himashu Kaura, Jaspreet Singh Phool
2010-04-01
Full Text Available Of all tasks in photogrammetry the extraction of cartographic features is the most timeconsuming. Fully automatic acquisition of features like roads and buildings, however, appears tobe very difficult. The extraction of cartographic features form digital satellite imagery requiresinterpretation of this imagery. The knowledge one needs about the topographic objects and theirappearances in satellite images in order to recognize these objects and extract the relevantobject outlines is difficult to model and to implement in computer algorithms. This paperintroduces Particle Swarm Optimization based method of object extraction from Google Earthimage (satellite image. This paper deals with the land cover mapping by using swarm computingtechniques. The motivation of this paper is to explore the improved swarm computing algorithmsfor the satellite image object extraction.
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 ...
Humphries, Stanley
2013-01-01
Detailed enough for a text and sufficiently comprehensive for a reference, this volume addresses topics vital to understanding high-power accelerators and high-brightness-charged particle beams. Subjects include stochastic cooling, high-brightness injectors, and the free electron laser. Humphries provides students with the critical skills necessary for the problem-solving insights unique to collective physics problems. 1990 edition.
Charged Particle Optics Theory
Czech Academy of Sciences Publication Activity Database
Hawkes, P. W.; Lencová, Bohumila
-, č. 6 (2006), s. 6-8 Grant ostatní: EC 5RP(XE) G5RD-CT-2000-00344 Institutional research plan: CEZ:AV0Z20650511 Keywords : optics of charged particles * design of ion lithography system * spot profile * the finite element method Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering www.phantomsnet.net
Adaptive Method of Particle Swarm Optimization for Multimodal Function
Guo, Jiantao; Xinwu CHEN; Youchao TU
2015-01-01
Multimodal optimization or finding more than one optimum is needed in many scientific and engineering application fields. Niching methods of particle swarm optimization has been successfully applied in many areas since it is a powerful, yet simple population based optimization strategy. A comprehensive analysis of neighborhood selection and information sharing mechanism was given from two aspects including topology technique and measure criterion. Moreover, most of existing niching particle s...
Passengers’ Evacuation in Ships Based on Neighborhood Particle Swarm Optimization
Gan-Nan Yuan; Li-Na Zhang; Li-Qiang Liu; Kan Wang
2014-01-01
A new intelligent model to simulate evacuation behavior in ships called neighborhood particle swarm optimization is proposed. This model determines the rules of behavior and velocity updating formulas to solve staff conflicts. The individuals in evacuation are taken as particles in PSO and update their behaviors by individual attributes, neighborhood attributes, and social attributes. Putting the degree of freedom movement of ships into environment factor and using the real Ro-Ro ship informa...
Wang, Lingfeng; Singh, Chanan
2007-01-01
Source: Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, Book edited by: Felix T. S. Chan and Manoj Kumar Tiwari, ISBN 978-3-902613-09-7, pp. 532, December 2007, Itech Education and Publishing, Vienna, Austria
Energy group structure determination using particle swarm optimization
International Nuclear Information System (INIS)
Highlights: ► Particle swarm optimization is applied to determine broad group structure. ► A graph representation of the broad group structure problem is introduced. ► The approach is tested on a fuel-pin model. - Abstract: Multi-group theory is widely applied for the energy domain discretization when solving the Linear Boltzmann Equation. To reduce the computational cost, fine group cross libraries are often down-sampled into broad group cross section libraries. Cross section data collapsing generally involves two steps: Firstly, the broad group structure has to be determined; secondly, a weighting scheme is used to evaluate the broad cross section library based on the fine group cross section data and the broad group structure. A common scheme is to average the fine group cross section weighted by the fine group flux. Cross section collapsing techniques have been intensively researched. However, most studies use a pre-determined group structure, open based on experience, to divide the neutron energy spectrum into thermal, epi-thermal, fast, etc. energy range. In this paper, a swarm intelligence algorithm, particle swarm optimization (PSO), is applied to optimize the broad group structure. A graph representation of the broad group structure determination problem is introduced. And the swarm intelligence algorithm is used to solve the graph model. The effectiveness of the approach is demonstrated using a fuel-pin model
Support vector machine based on adaptive acceleration particle swarm optimization.
Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali
2014-01-01
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. PMID:24790584
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.
International Nuclear Information System (INIS)
This paper presents a new algorithm for solving the optimal contract capacities of a time-of-use (TOU) rates industrial customer. This algorithm is named iteration particle swarm optimization (IPSO). A new index, called iteration best is incorporated into particle swarm optimization (PSO) to improve solution quality and computation efficiency. Expanding line construction cost and contract recovery cost are considered, as well as demand contract capacity cost and penalty bill, in the selection of the optimal contract capacities. The resulting optimal contract capacity effectively reaches the minimum electricity charge of TOU rates users. A significant reduction in electricity costs is observed. The effects of expanding line construction cost and contract recovery cost on the selection of optimal contract capacities can also be estimated. The feasibility of the new algorithm is demonstrated by a numerical example, and the IPSO solution quality and computation efficiency are compared to those of other algorithms. (author)
International Nuclear Information System (INIS)
This paper presents a new algorithm for solving the optimal contract capacities of a time-of-use (TOU) rates industrial customer. This algorithm is named iteration particle swarm optimization (IPSO). A new index, called iteration best is incorporated into particle swarm optimization (PSO) to improve solution quality and computation efficiency. Expanding line construction cost and contract recovery cost are considered, as well as demand contract capacity cost and penalty bill, in the selection of the optimal contract capacities. The resulting optimal contract capacity effectively reaches the minimum electricity charge of TOU rates users. A significant reduction in electricity costs is observed. The effects of expanding line construction cost and contract recovery cost on the selection of optimal contract capacities can also be estimated. The feasibility of the new algorithm is demonstrated by a numerical example, and the IPSO solution quality and computation efficiency are compared to those of other algorithms
A Modified Particle Swarm Optimization on Search Tasking
Directory of Open Access Journals (Sweden)
Mohammad Naim Rastgoo
2015-03-01
Full Text Available Recently, more and more researches have been conducted on the multi-robot system by applying bio- inspired algorithms. Particle Swarm Optimization (PSO is one of the optimization algorithms that model a set of solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization problems, but has a defect when it is applied on search tasking. As the time progress, the global searching of PSO decreased and it converged on a small region and cannot search the other region, which is causing the premature convergence problem. In this study we have presented a simulated multi-robot search system to overcome the premature convergence problem. Experimental results show that the proposed algorithm has better performance rather than the basic PSO algorithm on the searching task.
Inspiring Particle Swarm Optimization on Multi-Robot Search System
Directory of Open Access Journals (Sweden)
Mohammad Naim Rastgoo
2014-10-01
Full Text Available Multi-robot Search system is one area that attracts many researchers. In the field of multi-robot system one of the problem is to design a system that allow the robot to work within a team to find a target. There are many methods that are used on the multi-robot systems. One of the methods is Particle Swarm Optimization (PSO that uses a virtual multi-agent search to find a target in a 2 dimensional search space. In this paper we present a multi-search algorithm by modifying the Particle Swarm Optimization algorithm to model an abstracted level theeffects of changing aspects and parameters of the system suchas number of robots.
Modified Particle Swarm Optimization for Hybrid Wireless Sensor Networks Coverage
Directory of Open Access Journals (Sweden)
Bing Cheng
2014-01-01
Full Text Available Efficient network coverage and connectivity are the requisites for most Wireless Sensor Network deployments, particularly those concerned with area monitoring. The Coverage Control Technology is one of the basic technologies of wireless sensor network, and is mainly concerned about how to prolong the network lifetime on the basis of meeting users’ perception demand. To optimize wireless sensor networks coverage, an algorithm which is based on particle swarm optimization with dynamic clonal selection is proposed. This algorithm controls the clonal quantity and variation range of particle which represents the locations of all mobile sensor nodes, by coverage rate and similarity among the swarm to avoiding being trapped in local optimum. By comparison of the simulation results with other algorithms, this optimization algorithm could improve the performance of network coverage more effectively.
Application of particle swarm techniques in sensor network configuration
Tillett, Jason; Yang, Shanchieh J.; Rao, Raghuveer; Sahin, Ferat
2005-05-01
A decentralized version of particle swarm optimization called the distributed particle swarm optimization (DPSO) approach is formulated and applied to the generation of sensor network configurations or topologies so that the deleterious effects of hidden nodes and asymmetric links on the performance of wireless sensor networks are minimized. Three different topology generation schemes, COMPOW, Cone-Based and the DPSO--based schemes are examined using ns-2. Simulations are executed by varying the node density and traffic rates. Results contrasting heterogeneous vs. homogeneous power reveal that an important metric for a sensor network topology may involve consideration of hidden nodes and asymmetric links, and demonstrate the effect of spatial reuse on the potency of topology generators.
A Diversity-Guided Particle Swarm Optimizer - the ARPSO
DEFF Research Database (Denmark)
Vesterstrøm, Jacob Svaneborg; Riget, Jacques
2002-01-01
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...... pressure or a too high gene flow between population individuals. With PSOs the fast information flow between particles seems to be the reason for clustering of particles. Diversity declines rapidly, leaving the PSO algorithm with great difficulties of escaping local optima. Consequently, the clustering...
Roundness error assessment based on particle swarm optimization
International Nuclear Information System (INIS)
Roundness error assessment is always a nonlinear optimization problem without constraints. The method of particle swarm optimization (PSO) is proposed to evaluate the roundness error. PSO is an evolution algorithm derived from the behavior of preying birds. PSO regards each feasible solution as a particle (point in n-dimensional space). It initializes a swarm of random particles in the feasible region. All particles always trace two particles in which one is the best position itself; another is the best position of all particles. According to the inertia weight and two best particles, all particles update their positions and velocities according to the fitness function. After iterations, it converges to an optimized solution. The reciprocal of the error assessment objective function is adopted as the fitness. In this paper the calculating procedures with PSO are given. Finally, an assessment example is used to verify this method. The results show that the method proposed provides a new way for other form and position error assessment because it can always converge to the global optimal solution
EXPERIENCE WITH SYNCHRONOUS GENERATOR MODEL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE
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...
Genetic algorithm and particle swarm optimization combined with Powell method
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.
Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization
2014-01-01
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 Pa...
Modified Particle Swarm Optimization for Hybrid Wireless Sensor Networks Coverage
Bing Cheng
2014-01-01
Efficient network coverage and connectivity are the requisites for most Wireless Sensor Network deployments, particularly those concerned with area monitoring. The Coverage Control Technology is one of the basic technologies of wireless sensor network, and is mainly concerned about how to prolong the network lifetime on the basis of meeting users’ perception demand. To optimize wireless sensor networks coverage, an algorithm which is based on particle swarm optimization with dynamic clo...
Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization
Zhang, Junguo; Lei, Yutong; Chen, Chen; Lin, Fantao
2016-01-01
Node deployment is the key problem of wireless sensor network technology. For a directional sensor network, the perceived probability model reflects the quality of the network. The problem of the probability node deployment is too little of the distribution of the nodes asymmetrical. In this paper, we study the probability model of directional perceived nodes and propose an improved deterministic deployment algorithm based on particle swarm optimization to increase perceived probability. By a...
Reactive Power Optimization Using Quantum Particle Swarm Optimization
K. Thanushkodi; K.S.Chandragupta Mauryan; A. Sakthisuganya
2012-01-01
Problem statement: The problem of controlling a power system is not an easy task; it is subjected to various constraints. There are at risks of voltage instability problems due to highly stressed operating conditions caused by increased load demand and other constraints in the power system network. Approach: This study presents the implementation of Quantum Particle Swarm Optimization (QPSO) in solving the Reactive Power Optimization (RPO) problem. The main aim of this algorithm is the minimi...
Particle swarm optimization for complex nonlinear optimization problems
Alexandridis, Alex; Famelis, Ioannis Th.; Tsitouras, Charalambos
2016-06-01
This work presents the application of a technique belonging to evolutionary computation, namely particle swarm optimization (PSO), to complex nonlinear optimization problems. To be more specific, a PSO optimizer is setup and applied to the derivation of Runge-Kutta pairs for the numerical solution of initial value problems. The effect of critical PSO operational parameters on the performance of the proposed scheme is thoroughly investigated.
Optimasi Desain Heat Exchanger dengan Menggunakan Metode Particle Swarm Optimization
Rifnaldi Veriyawan; Totok Ruki Biyanto
2014-01-01
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 pe...
Query Optimization in Grid Databases Using with Particle Swarm Optimization
Mahdi Mahjour-Bonab; Javad Sohafi-Bonab
2012-01-01
Query Optimization is one of fundamental problems in grid databases. Especially, when the databases are replicated and stored in different nodes of the network. with regard to the point that query in grid databases can be processed in different sites, The problem of choosing suitable sites to execute query is very important. In this article, to choose the sites particle swarm optimization algorithm has been used. To this purpose one function has been used as fitness function in a way that it ...
High speed end-milling optimisation using Particle Swarm Intelligence
F. Cus; Zuperl, U.; V. Gecevska
2007-01-01
Purpose: In this paper, Particle Swarm Optimization (PSO), which is a recently developed evolutionary technique, is used to efficiently optimize machining parameters simultaneously in high-speed milling processes where multiple conflicting objectives are present.Design/methodology/approach: Selection of machining parameters is an important step in process planning therefore a new methodology based on PSO is developed to optimize machining conditions. Artificial neural network simulation model...
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.
Voltage Profile Improvement of distribution system Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Yamini Arora
2014-09-01
Full Text Available Distributed generations (DGs play an important role in distribution networks. Distributed generation (DG exists in distribution systems and is installed by either the utility or the customers. Distributed Generators (DGs are now commonly used in distribution systems to reduce the power disruption in the power system network. Due to the installation of DGs in the system, the total power loss can be reduced and voltage profile of the buses can be improved due to this power quality of the distribution system is improved. Studies show that non-optimal locations and non-optimal sizes of DG units may lead to losses increase, together with bad effect on voltage profile. So, this paper aims at determining optimal DG allocation and sizing. To do so, the optimization technique named Particle Swarm Optimization (PSO is used .this Particle Swarm Optimization (PSO approach, capable to establish the optimal DG allocation and sizing on a distribution network. This paper presents optimal placement and estimation of distributed generation (DG capacity using Particle Swarm Optimization (PSO approach in the distribution systems to reduce the real power losses and to gain voltage profile improvement. The proposed (PSO based approach is tested on an IEEE 30-bus test system.
A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations
Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw
2005-01-01
A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.
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.
DIVERSE DEPICTION OF PARTICLE SWARM OPTIMIZATION FOR DOCUMENT CLUSTERING
Directory of Open Access Journals (Sweden)
K. Premalatha
2011-01-01
Full Text Available Document clustering algorithms play an important task towards the goal of organizing huge amounts of documents into a small number of significant clusters. Traditional clustering algorithms will search only a small sub-set of possible clustering and as a result, there is no guarantee that the solution found will be optimal. This paper presents different representation of particle in Particle Swarm Optimization (PSO for document clustering. Experiments results are examined with document corpus. It demonstrates that the Discrete PSO algorithm statistically outperforms the Binary PSO and Simple PSO for document Clustering.
Particle swarm as optimization tool in complex nuclear engineering problems
International Nuclear Information System (INIS)
Due to its low computational cost, gradient-based search techniques associated to linear programming techniques are being used as optimization tools. These techniques, however, when applied to multimodal search spaces, can lead to local optima. When finding solutions for complex multimodal domains, random search techniques are being used with great efficacy. In this work we exploit the swarm optimization algorithm search power capacity as an optimization tool for the solution of complex high dimension and multimodal search spaces of nuclear problems. Due to its easy and natural representation of high dimension domains, the particle swarm optimization was applied with success for the solution of complex nuclear problems showing its efficacy in the search of solutions in high dimension and complex multimodal spaces. In one of these applications it enabled a natural and trivial solution in a way not obtained with other methods confirming the validity of its application. (author)
Binary Particle Swarm Optimization based Biclustering of Web usage Data
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...
A quantum particle swarm optimizer with chaotic mutation operator
International Nuclear Information System (INIS)
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator. The application of chaotic sequences based on chaotic Zaslavskii map instead of random sequences in QPSO is a powerful strategy to diversify the QPSO population and improve the QPSO's performance in preventing premature convergence to local minima. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design
Chaotic Rough Particle Swarm Optimization Algorithms
Alatas, Bilal; AKIN, ERHAN
2007-01-01
In this chapter chaotic rough PSO, CRPSO, algorithms that use rough decision variables and rough particles that are based on notion of rough patterns have been proposed. Different chaotic maps have been embedded to adapt the parameters of PSO algorithm. This has been done by using of chaotic number generators each time a random number is needed by the classical PSO algorithm. Twelve PSO methods have been proposed and four chaotic maps have been analyzed in the data mining application. It has ...
Putra, Sayid Aidhil
2016-01-01
At this research is studied by using of model particle swarm optimization algorithm and genetic algorithm at case of lecturing scheduling. This research aim to know the level stability of particle swarm optimization algorithm and genetic algorithm in reaching of the best generation (iteration), then analyse the work process of the particle swarm optimization algorithm and genetic algorithm to lecturing scheduling. Base on research result is got that way of job genetic algorithm...
A Bayesian Interpretation of the Particle Swarm Optimization and Its Kernel Extension
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...
Zheping Yan; Chao Deng; Benyin Li; Jiajia Zhou
2014-01-01
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 par...
Inverse Transient Radiative Analysis in Two-Dimensional Turbid Media by Particle Swarm Optimizations
Yatao Ren; Hong Qi; Qin Chen; Liming Ruan
2015-01-01
Three intelligent optimization algorithms, namely, the standard Particle Swarm Optimization (PSO), the Stochastic Particle Swarm Optimization (SPSO), and the hybrid Differential Evolution-Particle Swarm Optimization (DE-PSO), were applied to solve the inverse transient radiation problem in two-dimensional (2D) turbid media irradiated by the short pulse laser. The time-resolved radiative intensity signals simulated by finite volume method (FVM) were served as input for the inverse analysis. Th...
Particle swarm optimization of ascent trajectories of multistage launch vehicles
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
Particle swarm optimization for the clustering of wireless sensors
Tillett, Jason C.; Rao, Raghuveer M.; Sahin, Ferat; Rao, T. M.
2003-07-01
Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network.
Finite element model selection using Particle Swarm Optimization
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...
Nanodosimetry of charged particles
International Nuclear Information System (INIS)
In last year's annual report, the authors described the development of an ultra-miniature counter (UMC), described some of its physical characteristics, and presented some first measurements with this counter of microdosimetric spectra for neutrons (15 MeV) and photons (137Cs). It remains to investigate in more detail the operational characteristics of the UMC and if possible, to make a comparison of relevant physical parameters such as gain and multiplication radius with the Segur theory. In order to accomplish these objectives, it is necessary to build a wall-less version of the UMC, which will be amenable to calibration and investigation with collimated beams of charged particles. The design of such a counter has been worked out in principle. Investigations into the optimal design of electrode structures and dimensions are being carried out at present. The main problem occurs with the design of the grid structure which is required to define the outer boundary of the collecting volume. Our initial attempts would make it appear at present that a counter of 1 to 1.5 mm diameter is feasible. This should be more than adequate to provide an appropriately wall-less counter within a reasonable size cavity. It will probably be about a year before a working counter can be produced. In the interim, it is possible to make some initial efforts into the investigation of operational characteristics of a wall-less UMC by making use of similar design, but at a somewhat larger size. These first attempts at measurement of charged particle microdosimetric spectra at nanometer site provide added evidence that these counters can yield useful microdosimetric data at far smaller site sizes than heretofore attempted. They also provide added incentive and encouragement for development of wall-less UMC
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
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.
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.
Robot Path Planning Based on Random Coding Particle Swarm Optimization
Kun Su; YuJia Wang; XinNan Hu
2015-01-01
Mobile robot navigation is to find an optimal path to guide the movement of the robot, so path planning is guaranteed to find a feasible optimal path. However, the path planning problem must be solve two problems, i.e., the path must be kept away from obstacles or avoid the collision with obstacles and the length of path should be minimized. In this paper, a path planning algorithm based on random coding particle swarm optimization (RCPSO) algorithm is proposed to get the optimal collision-fr...
Multidimensional particle swarm optimization for machine learning and pattern recognition
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
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.
Thermal design of an electric motor using Particle Swarm Optimization
International Nuclear Information System (INIS)
In this paper, flow inside an electric machine called starter-alternator is studied parametrically with CFD in order to be used by a thermal lumped model coupled to an optimization algorithm using Particle Swarm Optimization (PSO). In a first case, the geometrical parameters are symmetric allowing us to model only one side of the machine. The optimized thermal results are not conclusive. In a second case, all the parameters are independent. In this case, the flow is strongly influenced by the dissymmetry. Optimization results are this time a clear improvement compared to the original machine.
Directing orbits of chaotic systems by particle swarm optimization
International Nuclear Information System (INIS)
This paper applies a novel evolutionary computation algorithm named particle swarm optimization (PSO) to direct the orbits of discrete chaotic dynamical systems towards desired target region within a short time by adding only small bounded perturbations, which could be formulated as a multi-modal numerical optimization problem with high dimension. Moreover, the synchronization of chaotic systems is also studied, which can be dealt with as an online problem of directing orbits. Numerical simulations based on Henon Map demonstrate the effectiveness and efficiency of PSO, and the effects of some parameters are also investigated
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)
Differential Evolution and Particle Swarm Optimization for Partitional Clustering
DEFF Research Database (Denmark)
Krink, Thiemo; Paterlini, Sandra
2006-01-01
Many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms 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...
PID control for chaotic synchronization using particle swarm optimization
International Nuclear Information System (INIS)
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.
OPTIMIZATION OF GRID RESOURCE SCHEDULING USING PARTICLE SWARM OPTIMIZATION ALGORITHM
Directory of Open Access Journals (Sweden)
S. Selvakrishnan
2010-10-01
Full Text Available Job allocation process is one of the big issues in grid environment and it is one of the research areas in Grid Computing. Hence a new area of research is developed to design optimal methods. It focuses on new heuristic techniques that provide an optimal or near optimal solution for large grids. By learning grid resource scheduling and PSO (Particle Swarm Optimization algorithm, this proposed scheduler allocates an application to a host from a pool of available hosts and applications by selecting the best match. PSO-based algorithm is more effective in grid resources scheduling with the favor of reducing the executing time and completing time.
Reactive Power Contribution of Multiple STATCOM using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
S. Uma Mageswaran
2013-02-01
Full Text Available Reactive power is vital for reliability, power quality, transmission line loss and voltage stability. Rapid industrial development makes the power system is stressed. This stressed power system has more loss and low voltage profile, generator has its limitation and could not generate sufficient reactive power, to overcome this situation Flexible AC Transmission System (FACTS devices are used. This paper makes use of one such FACTS device namely STATCOM to relief power system stress by injective adequate reactive power. Particle Swarm Optimization (PSO technique is used to optimize the STATCOM location and reactive power injection. Test case IEEE-30 bus system is considered for the simulation.
Learning Bayesian Networks from Data by Particle Swarm Optimization
Institute of Scientific and Technical Information of China (English)
无
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.
Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio
DEFF Research Database (Denmark)
Pinto, Tiago; Vale, Zita; Sousa, Tiago;
2014-01-01
Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors......, 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, the...
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
to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow......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 aiming...
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.
Robot Path Planning Based on Random Coding Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Kun Su
2015-04-01
Full Text Available Mobile robot navigation is to find an optimal path to guide the movement of the robot, so path planning is guaranteed to find a feasible optimal path. However, the path planning problem must be solve two problems, i.e., the path must be kept away from obstacles or avoid the collision with obstacles and the length of path should be minimized. In this paper, a path planning algorithm based on random coding particle swarm optimization (RCPSO algorithm is proposed to get the optimal collision-free path. Dijstra algorithm is applied to search a sub-optimal collision-free path in our algorithm; then the RCPSO algorithm is developed to tackle this optimal path planning problem in order to generate the global optimal path. The crossover operator of genetic algorithm and random coding are introduced into the particle swarm optimization to optimize the location of the sub-optimal path. The experiment results show that the proposed method is effective and feasible compared with different algorithms.
PWR power distribution flattening using Quantum Particle Swarm intelligence
International Nuclear Information System (INIS)
Highlights: ► Quantum Particle Swarm Optimization (QPSO) is applied to ICFMO. ► A differential mutation operator is added to enhance QPSO performance (QPSO-DM). ► PSO, QPSO and QPSO-DM are tested on Bushehr Nuclear Power Plant (BNPP). ► It is observed that QPSO-DM is comparable to PSO and QPSO on ICFMO. - Abstract: In-core fuel management optimization (ICFMO) is one of the most challenging concepts of nuclear engineering. Most of the strategies implemented for optimizing fuel loading pattern in nuclear power reactors are based on maximizing core multiplication factor in order to extract maximum energy and reducing power peaking factor from a predetermined value to maintain fuel integrity. In this investigation a new method using Quantum Particle Swarm Optimization (QPSO) algorithm has been developed in order to flatten power density distribution in WWER-1000 Bushehr Nuclear Power Plant (BNPP) and thereby provide a better safety margin. The result and convergence of this method show that QPSO performs very well and is comparable to PSO. Furthermore, an operator has been added to QPSO as a mutation operator. This algorithm, called QPSO-DM, shows a better performance on ICFMO than PSO and QPSO. MATLAB software was used to map PSO, QPSO and QPSO-DM for loading pattern optimization. Multi-group constants generated by WIMS for different fuel configurations were fed into CITATION to obtain the power density distribution
A Particle Swarm Optimization Based Edge Preserving Impulse Noise Filter
Directory of Open Access Journals (Sweden)
S. M.M. Roomi,
2010-01-01
Full Text Available Problem statement: Image sensors and communication channels often introduce impulse noise in image transmission. The most common filters available to remove such noise are median filter and its variants but the major drawbacks identified with them are blurring of edge detail and low noise suppression. To preserve the sharp and useful information in the image, the filtering algorithms are required to have intelligence incorporated in them. Approach: This research proposed a particle swarm optimization based approach in the design of filter. The filter weights were adapted and optimized directionally to restore a corrupted pixel in a mean square sense. Results: This results in replacement of noisy pixels by near originals along its edge direction. Various objective parameters like Mean Absolute Error (MAE, percentage of noise elimination, percentage of pixels spoiled showed that the proposed recursive no-reference filter performs 4dB better than the competing filters. Conclusion: This research aimed at presenting a new filtering framework for impulse noise removal using Particle Swarm Optimization (PSO.
Multivariable optimization of liquid rocket engines using particle swarm algorithms
Jones, Daniel Ray
Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.
ADAPTIVE LIFTING BASED IMAGE COMPRESSION SCHEME WITH PARTICLE SWARM OPTIMIZATION TECHNIQUE
Nishat kanvel; Dr.S.Letitia,; Dr.Elwin Chandra Monie
2010-01-01
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.
Microwave-based medical diagnosis using particle swarm optimization algorithm
Modiri, Arezoo
This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level
Particle Swarm Inspired Underwater Sensor Self-Deployment
Du, Huazheng; Xia, Na; Zheng, Rong
2014-01-01
Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation. PMID:25195852
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.
Study on attitude determination based on discrete particle swarm optimization
Institute of Scientific and Technical Information of China (English)
VU; Khuong
2010-01-01
Attitude determination is a key technology in aerospace, sailing and land-navigation etc. In the method of double difference phase measurement, it is a crucial topic to solve the carrier phase integer ambiguity, which is shown to be a combination optimization problem, and thus efficient heuristic algorithms are needed. In this paper, we propose a discrete particle swarm optimization (DPSO)-based solution which aims at searching for the optimal integer ambiguity directly without decorrelation of ambiguity, and computing the baseline vector consequently. A novel flat binary particle encoding approach and corresponding revision operation are presented. Furthermore, domain knowledge is incorporated to significantly improve the convergence rate. Through extensive experiments, we demonstrate that the proposed algorithm outperforms a classic algorithm by up to 80% in time efficiency with solution quality guaranteed. The experiment results show that this algorithm is efficient, robust, and suitable for dynamic attitude determination.
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 t...
Heavy charged particle therapy
International Nuclear Information System (INIS)
A pilot study of heavy charged particles with heavy ion medical accelerator in Chiba (HIMAC) for advanced H and N cancer has been carried out from June 1994 at National Institute of Radiological Sciences (NIRS). As of the beginning of August 1994, three patients were treated by 290 MeV carbon ions. The patients had adenocarcinoma of the cheek mucosa, squamous cell carcinoma of the ethmoid sinus and adenoid cystic carcinoma of the sublingual gland. Patients were immobilized by individual head coach and thermosplint facial shell. Individual collimators and bolus were also prepared for each ports. Dose fractionation for the initial pilot study group was 16.2 GyE/18 fractions/6 weeks, which would be equivalent to standard fractionation of 60.0 Gy/30 fractions/6 weeks with photons. This dose fractionation was considered to be 20% lesser than 75 GyE/37.5 fractions/7.5 weeks, which is estimated to be maximum tolerance dose for advanced H and N cancers. HIMAC worked well and there was no major trouble causing any treatment delay. Acute skin reactions of 3 patients were 2 cases of bright erythema with patchy moist desquamation and one of dull erythema, which were evaluated as equivalent reaction with irradiated dose. Acute mucosa reactions appeared to have lesser reaction than predicted mucositis. Tumor reactions of three patients were partial reaction (PR) at the end of treatment and nearly complete remission (CR) after 6 months of treatment. From October 1994, we started to treat patients with advanced H and N cancer with 10% high dose than previous dose. And new candidates of pilot study with non small cell lung cancer, brain tumor and carcinoma of the tongue were entered into pilot study. At the end of February 1995, a total of 21 patients were treated by carbon ions. (J.P.N.)
Binary Particle Swarm Optimization based Biclustering of Web Usage Data
Rathipriya, R.; Thangavel, K.; Bagyamani, J.
2011-07-01
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 marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms.
Video Superresolution via Parameter-Optimized Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Yunyi Yan
2014-01-01
Full Text Available Video superresolution (VSR aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO. We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR, sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality.
A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm
2014-01-01
We compare 27 modifications of the original particle swarm optimization (PSO) algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO), the second strategy periodically partitions the population into equal...
An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network
Ming Li; Wenqiang Du; Fuzhong Nian
2014-01-01
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 tim...
2014-01-01
A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value i...
Performance Analysis of Mimo Radar Waveform Using Accelerated Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
B. Roja Reddy
2012-08-01
Full Text Available The Accelerated Particle Swarm Optimization Algorithm is promoted to numerically design orthogonal Discrete Frequency Waveforms and Modified Discrete Frequency Waveforms (DFCWs with good correlation properties for MIMO radar. We employ Accelerated Particle Swarm Optimization algorithm (ACC_PSO, Particles of a swarm communicate good positions, velocity and accelerations to each other as well as dynamically adjust their own position, velocity and acceleration derived from the best of all particles. The simulation results show that the proposed algorithm is effective for the design of DFCWs signal used in MIMO radar.
2014-01-01
This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles’ activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of partic...
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.
Strategic bidding in electricity markets using particle swarm optimization
International Nuclear Information System (INIS)
Profit maximization for power companies is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. In this paper, we present two particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market. The first method uses a conventional PSO technique to find solutions. The second method uses a decomposition technique in conjunction with the PSO approach. This new decomposition-based PSO dramatically outperforms the conventional form of PSO. We show that for nonlinear cost functions PSO solutions provide higher expected profits than marginal cost-based bidding. (author)
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.
PWR fuel management optimization using continuous particle swarm intelligence
International Nuclear Information System (INIS)
The objective of nuclear fuel management is to minimize the cost of electrical energy generation subject to operational and safety constraints. In the present work, a core reload optimization package using continuous version of particle swarm optimization, CRCPSO, which is a combinatorial and discrete one has been developed and mapped on nuclear fuel loading pattern problems. This code is applicable to all types of PWR cores to optimize loading patterns. To evaluate the system, flattening of power inside a WWER-1000 core is considered as an objective function although other variables such as Keff along power peaking factor, burn up and cycle length can be included. Optimization solutions, which improve the safety aspects of a nuclear reactor, may not lead to economical designs. The system performed well in comparison to the developed loading pattern optimizer using Hopfield along SA and GA.
Parameter estimation for chaotic systems by particle swarm optimization
International Nuclear Information System (INIS)
Parameter estimation for chaotic systems is an important issue in nonlinear science and has attracted increasing interests from various research fields, which could be essentially formulated as a multi-dimensional optimization problem. As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications, owing to its simple concept, easy implementation and quick convergence. However, to the best of our knowledge, there is no published work on PSO for estimating parameters of chaotic systems. In this paper, a PSO approach is applied to estimate the parameters of Lorenz system. Numerical simulation and the comparisons demonstrate the effectiveness and robustness of PSO. Moreover, the effect of population size on the optimization performances is investigated as well
Economic dispatch using particle swarm optimization. A review
International Nuclear Information System (INIS)
Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. (author)
Order-2 Stability Analysis of Particle Swarm Optimization.
Liu, Qunfeng
2015-01-01
Several stability analyses and stable regions of particle swarm optimization (PSO) have been proposed before. The assumption of stagnation and different definitions of stability are adopted in these analyses. In this paper, the order-2 stability of PSO is analyzed based on a weak stagnation assumption. A new definition of stability is proposed and an order-2 stable region is obtained. Several existing stable analyses for canonical PSO are compared, especially their definitions of stability and the corresponding stable regions. It is shown that the classical stagnation assumption is too strict and not necessary. Moreover, among all these definitions of stability, it is shown that our definition requires the weakest conditions, and additional conditions bring no benefit. Finally, numerical experiments are reported to show that the obtained stable region is meaningful. A new parameter combination of PSO is also shown to be good, even better than some known best parameter combinations. PMID:24738856
Solving constrained optimization problems with hybrid particle swarm optimization
Zahara, Erwie; Hu, Chia-Hsin
2008-11-01
Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.
OPTIMIZING LOCALIZATION ROUTE USING PARTICLE SWARM-A GENETIC APPROACH
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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.
Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers
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...
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.
Cosmological parameter estimation using Particle Swarm Optimization (PSO)
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...
Strategic bidding in electricity markets using particle swarm optimization
Energy Technology Data Exchange (ETDEWEB)
Yucekaya, Ahmet D.; Valenzuela, Jorge [Department of Industrial and Systems Engineering, 207 Dunstan Hall, Auburn University, AL 36849-5347 (United States); Dozier, Gerry [Applied Computational Intelligence Lab., Computer Science and Software Engineering, 109 Dunstan Hall, Auburn University, AL 36849-5347 (United States)
2009-02-15
Profit maximization for power companies is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. In this paper, we present two particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market. The first method uses a conventional PSO technique to find solutions. The second method uses a decomposition technique in conjunction with the PSO approach. This new decomposition-based PSO dramatically outperforms the conventional form of PSO. We show that for nonlinear cost functions PSO solutions provide higher expected profits than marginal cost-based bidding. (author)
International Nuclear Information System (INIS)
Momentum - transfer approximation is applied to momentum and energy balance equations describing reacting particle swarms in gases in crossed electric and magnetic fields. Transport coefficients of charged particles undergoing both inelastic and reactive, non-particle-conserving collisions with a gas of neutral molecules are calculated. Momentum - transfer theory (MTT) has been developed mainly by Robson and collaborators. It has been applied to a single reactive gas and mixtures of reactive gases in electric field only. MTT has also been applied in crossed electric and magnetic fields recently and independently of our work but the reactive collisions were not considered. Consider a swarm of electrons of charge e and mass m moving with velocity rvec v through a neutral gas under the influence of an applied electric rvec E and magnetic rvec B field. The collision processes which we shall investigate are limited to elastic, inelastic and reactive collisions of electrons with gas molecules. Here we interpret reactive collisions as collisions which produce change in number of the swarm particles. Reactive collisions involve creation (ionization by electron impact) or loss (electron attachment) of swarm particles. We consider only single ionization in approximation of the mass ratio m/m00 are masses of electrons and neutral particles, respectively. We assume that the stage of evolution of the swarm is the hydrodynamic limit (HDL). In HDL, the space - time dependence of all properties is carried by the number density n of swarm particles
Object Detection In Image Using Particle Swarm Optimization
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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.
International Nuclear Information System (INIS)
Suitable for both the specialist and non-specialist, this book develops all statements from first principles. Key chapters of the book focus upon how to design particle-optical systems, the systematics of image abberations, the effects of fringing fields, systematics of beams, and solutions for particle-optical systems. An undergraduate background in physics and mathematics is required for this work
Mao, Jiandong; Li, Jinxuan
2015-10-01
Particle size distribution is essential for describing direct and indirect radiation of aerosols. Because the relationship between the aerosol size distribution and optical thickness (AOT) is an ill-posed Fredholm integral equation of the first type, the traditional techniques for determining such size distributions, such as the Phillips-Twomey regularization method, are often ambiguous. Here, we use an approach based on an improved particle swarm optimization algorithm (IPSO) to retrieve aerosol size distribution. Using AOT data measured by a CE318 sun photometer in Yinchuan, we compared the aerosol size distributions retrieved using a simple genetic algorithm, a basic particle swarm optimization algorithm and the IPSO. Aerosol size distributions for different weather conditions were analyzed, including sunny, dusty and hazy conditions. Our results show that the IPSO-based inversion method retrieved aerosol size distributions under all weather conditions, showing great potential for similar size distribution inversions.
2014-01-01
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 parti...
Measuring momentum for charged particle tomography
Morris, Christopher; Fraser, Andrew Mcleod; Schultz, Larry Joe; Borozdin, Konstantin N.; Klimenko, Alexei Vasilievich; Sossong, Michael James; Blanpied, Gary
2010-11-23
Methods, apparatus and systems for detecting charged particles and obtaining tomography of a volume by measuring charged particles including measuring the momentum of a charged particle passing through a charged particle detector. Sets of position sensitive detectors measure scattering of the charged particle. The position sensitive detectors having sufficient mass to cause the charged particle passing through the position sensitive detectors to scatter in the position sensitive detectors. A controller can be adapted and arranged to receive scattering measurements of the charged particle from the charged particle detector, determine at least one trajectory of the charged particle from the measured scattering; and determine at least one momentum measurement of the charged particle from the at least one trajectory. The charged particle can be a cosmic ray-produced charged particle, such as a cosmic ray-produced muon. The position sensitive detectors can be drift cells, such as gas-filled drift tubes.
Pixelated source optimization for optical lithography via particle swarm optimization
Wang, Lei; Li, Sikun; Wang, Xiangzhao; Yan, Guanyong; Yang, Chaoxing
2016-01-01
Source optimization is one of the key techniques for achieving higher resolution without increasing the complexity of mask design. An efficient source optimization approach is proposed on the basis of particle swarm optimization. The pixelated sources are encoded into particles, which are evaluated by using the pattern error as the fitness function. Afterward, the optimization is implemented by updating the velocities and positions of these particles. This approach is demonstrated using three mask patterns, including a periodic array of contact holes, a vertical line/space design, and a complicated pattern. The pattern errors are reduced by 69.6%, 51.5%, and 40.3%, respectively. Compared with the source optimization approach via genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. Compared with the source optimization approach via gradient descent method, the proposed approach does not need the calculation of gradients, and it has a strong adaptation to various lithographic models, fitness functions, and resist models. The robustness of the proposed approach to initial sources is also verified.
Sarkar, Soham; Das, Swagatam
In recent years particle swarm optimization emerges as one of the most efficient global optimization tools. In this paper, a hybrid particle swarm with differential evolution operator, termed DEPSO, is applied for the synthesis of linear array geometry. Here, the minimum side lobe level and null control, both are obtained by optimizing the spacing between the array elements by this technique. Moreover, a statistical comparison is also provided to establish its performance against the results obtained by Genetic Algorithm (GA), classical Particle Swarm Optimization (PSO), Tabu Search Algorithm (TSA), Differential Evolution (DE) and Memetic Algorithm (MA).
Energy Technology Data Exchange (ETDEWEB)
Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; 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.
International Nuclear Information System (INIS)
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
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
Basu, Mousumi
2015-07-01
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.
Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning
Institute of Scientific and Technical Information of China (English)
无
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.
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.
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.
High speed end-milling optimisation using Particle Swarm Intelligence
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F. Cus
2007-06-01
Full Text Available Purpose: In this paper, Particle Swarm Optimization (PSO, which is a recently developed evolutionary technique, is used to efficiently optimize machining parameters simultaneously in high-speed milling processes where multiple conflicting objectives are present.Design/methodology/approach: Selection of machining parameters is an important step in process planning therefore a new methodology based on PSO is developed to optimize machining conditions. Artificial neural network simulation model (ANN for milling operation is established with respect to maximum production rate, subject to a set of practical machining constraints. An ANN predictive model is used to predict cutting forces during machining and PSO algorithm is used to obtain optimum cutting speed and feed rate.Findings: The simulation results show that compared with genetic algorithms (GA and simulated annealing (SA, the proposed algorithm can improve the quality of the solution while speeding up the convergence process. PSO is proved to be an efficient optimization algorithm.Research limitations/implications: Machining time reductions of up to 30% are observed. In addition, the new technique is found to be efficient and robust.Practical implications: The results showed that integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry.Originality/value: An algorithm for PSO is developed and used to robustly and efficiently find the optimum machining conditions in end-milling. The new computational technique has several advantages and benefits and is suitable for use combined with ANN based models where no explicit relation between inputs and outputs is available. This research opens the door for a new class of optimization techniques which are based on Evolution Computation in
Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm
International Nuclear Information System (INIS)
Highlights: • An improved thermodynamic model taking into account irreversibility parameter was developed. • A multi-objective optimization method for designing Stirling engine was investigated. • Multi-objective particle swarm optimization algorithm was adopted in the area of Stirling engine for the first time. - Abstract: In the recent years, the interest in Stirling engine has remarkably increased due to its ability to use any heat source from outside including solar energy, fossil fuels and biomass. A large number of studies have been done on Stirling cycle analysis. In the present study, a mathematical model based on thermodynamic analysis of Stirling engine considering regenerative losses and internal irreversibilities has been developed. Power output, thermal efficiency and the cycle irreversibility parameter of Stirling engine are optimized simultaneously using Particle Swarm Optimization (PSO) algorithm, which is more effective than traditional genetic algorithms. In this optimization problem, some important parameters of Stirling engine are considered as decision variables, such as temperatures of the working fluid both in the high temperature isothermal process and in the low temperature isothermal process, dead volume ratios of each heat exchanger, volumes of each working spaces, effectiveness of the regenerator, and the system charge pressure. The Pareto optimal frontier is obtained and the final design solution has been selected by Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Results show that the proposed multi-objective optimization approach can significantly outperform traditional single objective approaches
Dust particle charging in sheath
International Nuclear Information System (INIS)
The charging and the screening of spherical dust particles in sheaths near the wall were studied using computer simulation. The three-dimensional PIC/MCC method and molecular dynamics method were applied to describe plasma particles motion and interaction with macroscopic dust grain. Calculations were carried out at different neutral gas pressures and wall potentials. Values of the charge of the dust particles and spatial distributions of plasma parameters are obtained by modelling. The results have shown that the charge of the dust particles in the sheath, as well as the spatial distribution of the ions and electrons near the dust particles, depend strongly on the wall potential. It is shown that for large negative values of the wall potential the negative charge of a dust particle decreases due to the decline of the electron density in its vicinity. In addition, the flow of energy of the ions on the surface of dust particles is increased due to better focusing effect of the dust particle field on ions.
Constrained Fuzzy Predictive Control Using Particle Swarm Optimization
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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.
GPU-Based Asynchronous Global Optimization with Particle Swarm
International Nuclear Information System (INIS)
The recent upsurge in research into general-purpose applications for graphics processing units (GPUs) has made low cost high-performance computing increasingly more accessible. Many global optimization algorithms that have previously benefited from parallel computation are now poised to take advantage of general-purpose GPU computing as well. In this paper, a global parallel asynchronous particle swarm optimization (PSO) approach is employed to solve three relatively complex, realistic parameter estimation problems in which each processor performs significant computation. Although PSO is readily parallelizable, memory bandwidth limitations with GPUs must be addressed, which is accomplished by minimizing communication among individual population members though asynchronous operations. The effect of asynchronous PSO on robustness and efficiency is assessed as a function of problem and population size. Experiments were performed with different population sizes on NVIDIA GPUs and on single-core CPUs. Results for successful trials exhibit marked speedup increases with the population size, indicating that more particles may be used to improve algorithm robustness while maintaining nearly constant time. This work also suggests that asynchronous operations on the GPU may be viable in stochastic population-based algorithms to increase efficiency without sacrificing the quality of the solutions.
Reactive Power Optimization Using Quantum Particle Swarm Optimization
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K. Thanushkodi
2012-01-01
Full Text Available Problem statement: The problem of controlling a power system is not an easy task; it is subjected to various constraints. There are at risks of voltage instability problems due to highly stressed operating conditions caused by increased load demand and other constraints in the power system network. Approach: This study presents the implementation of Quantum Particle Swarm Optimization (QPSO in solving the Reactive Power Optimization (RPO problem. The main aim of this algorithm is the minimization of the real power loss and to improvise the voltage in the system. In this new algorithm, the particles were made to perform studies on itself and also the best ones in the system. Results: The implementations of QPSO were carried on modified IEEE 14 bus system for obtaining solution to the reactive power optimization and the output results are found predominant with classical PSO. Conclusion: This technique is used to find the best solution and also the convergence time is reduced. The proposed QPSO method is demonstrated and results are compared with traditional optimization methods.
Query Optimization in Grid Databases Using with Particle Swarm Optimization
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Mahdi Mahjour-Bonab
2012-11-01
Full Text Available Query Optimization is one of fundamental problems in grid databases. Especially, when the databases are replicated and stored in different nodes of the network. with regard to the point that query in grid databases can be processed in different sites, The problem of choosing suitable sites to execute query is very important. In this article, to choose the sites particle swarm optimization algorithm has been used. To this purpose one function has been used as fitness function in a way that it takes into account the required memory to execute a certain query. Also, the time needed to execute a query and the cost to do so or both of them have been taken into account to perform a query suitably in certain site which is effective in allocating the site to perform a certain query. In this article different repetitions on different particles with regard to cost and time needed to execute a query in different sites have been conducted and the simulation results have been compared with each other.
The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm
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
Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction
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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.
Discrete Particle Swarm Optimization with Scout Particles for Library Materials Acquisition
Yi-Ling Wu; Tsu-Feng Ho; Shyong Jian Shyu; Lin, Bertrand M. T.
2013-01-01
Materials acquisition is one of the critical challenges faced by academic libraries. This paper presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget, limitation of the number of materials in each category and each language. To tackle the constrained problem, we propose a discrete particle swarm optimization (DP...
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.
Chaotic particle swarm optimization for economic dispatch considering the generator constraints
International Nuclear Information System (INIS)
Chaotic particle swarm optimization (CPSO) methods are optimization approaches based on the proposed particle swarm optimization (PSO) with adaptive inertia weight factor (AIWF) and chaotic local search (CLS). In this paper, two CPSO methods based on the logistic equation and the Tent equation are presented to solve economic dispatch (ED) problems with generator constraints and applied in two power system cases. Compared with the traditional PSO method, the convergence iterative numbers of the CPSO methods are reduced, and the solutions generation costs decrease around 5 $/h in the six unit system and 24 $/h in the 15 unit system. The simulation results show that the CPSO methods have good convergence property. The generation costs of the CPSO methods are lower than those of the traditional particle swarm optimization algorithm, and hence, CPSO methods can result in great economic effect. For economic dispatch problems, the CPSO methods are more feasible and more effective alternative approaches than the traditional particle swarm optimization algorithm
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.
Directory of Open Access Journals (Sweden)
Liu Dong
2016-01-01
Full Text Available Simultaneous reconstruction of temperature field and radiative properties including scattering albedo and extinction coefficient is presented in a two-dimensional (2-D rectangular, absorbing, emitting and isotropically scattering gray medium from the knowledge of the exit radiative intensities received by charge-coupled device (CCD cameras at boundary surfaces. The inverse problem is formulated as a non-linear optimization problem and solved by stochastic particle swarm optimization. The effects of particle swarm size, generation number, measurement errors, and optical thickness on the accuracy of the estimation, and computing time were investigated and the results show that the temperature field and radiative properties can be reconstructed well for the exact and noisy data, but radiative properties are harder to obtain than temperature field. Moreover, the extinction coefficient is more difficult to reconstruct than scattering albedo.
A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm
Alexandre Szabo; Leandro Nunes de Castro
2013-01-01
The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first pro...
Opposition-Based Barebones Particle Swarm for Constrained Nonlinear Optimization Problems
Hui Wang
2012-01-01
This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performa...
Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem
Rong-Jiang Ma; Nan-Yang Yu; Jun-Yi Hu
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 result...
Robust design of broadband EUV multilayer beam splitters based on particle swarm optimization
International Nuclear Information System (INIS)
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
Ehsan Sarrafan
2014-01-01
In this study, a new discrete parallel Particle Swarm Optimization (PSO) method is presented for long term Transmission Network Expansion Planning (TNEP) with security constraints. The procedure includes obtaining the expansion planning with the minimum investment cost using a model based on DC load flow formulation. (N-1) contingency is included in this model. The Particle Swarm Optimization algorithm presented in this study is used to solve the planning problem for two different models: wit...
2014-01-01
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 ...
Multiuser detection using soft particle swarm optimization along with radial basis function
Zubair, Muhammad; CHOUDHRY, Muhammad Aamer Saleem; Qureshi, Ijaz Mansoor
2014-01-01
The multiuser detection (MUD) problem was addressed as a pattern classification problem. Due to their strength in solving nonlinear separable problems, radial basis functions, aided by soft particle swarm optimization, were proposed to perform MUD for a synchronous direct sequence code division multiple access system. The proposed solution was shown to exhibit performance better than a number of other suboptimum detectors including the genetic algorithm and the classical particle swarm optimi...
Optimization of sheet components locating scheme based on improved particle swarm optimization
Zhang, Heng; Xing, Yanfeng
2015-01-01
The current sheet components locating scheme optimization needs a great deal of finite element analysis, which limits the fixture design efficiency. To reduce finite element analysis times, this paper proposes a modified particle swarm optimization algorithm based on the inertia weight, and through the secondary development of finite element software, the improved particle swarm optimization is applied to fixture locating scheme design. Taking the front fender of some vehicle as the living ex...
Razan A. Jamous; EssamEl.Seidy; Assem A. Tharwat; Bayoumi Ibrahim Bayoum
2015-01-01
Particle Swarm Optimization (PSO) has become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. The usage of the Particle Swarm Optimization technique in coping with Portfolio Selection problems is the most important applications of PSO to predict the stocks that have maximum profit with minimum risk, using some common indicators that give advice of buy and sell. This paper gives the reader the state of the art of the vario...
Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems
Zahra Beheshti; Siti Mariyam Shamsuddin; Sarina Sulaiman
2014-01-01
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 topologi...
Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine
Yang Liu; Bo He; Diya Dong; Yue Shen; Tianhong Yan; Rui Nian; Amaury Lendasse
2015-01-01
A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, inclu...
RAHMANI, Shima; NIASATI, Mohsen
2015-01-01
The gravitational search algorithm is one of the new heuristic search optimization methods which are based on gravity law. Despite having high capability, this approach suffers from low search speed duo to lack of memory. To overcome this problem, the particle swarm optimization method has been used. Therefore, in this paper, hybrid particle swarm optimization and gravitational search algorithm has been used to find the solution of optimal power flow. Performance of the proposed method has be...
An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight
2014-01-01
Particle swarm optimization algorithm (PSO) is a global stochastic tool, which has ability to search the global optima. However, PSO algorithm is easily trapped into local optima with low accuracy in convergence. In this paper, in order to overcome the shortcoming of PSO algorithm, an improved particle swarm optimization algorithm (IPSO), based on two forms of exponential inertia weight and two types of centroids, is proposed. By means of comparing the optimization ability of IPSO algorithm w...
Xiaodong Yu; Yingjie Lei; Shaohua Yue; Feixiang Meng
2015-01-01
In order to overcome the long training time caused by searching optimal basic functions based on greedy strategy from a redundant basis function dictionary for the intuitionistic fuzzy kernel matching pursuit (IFKMP), the particle swarm optimization algorithm with powerful ability of global search and quick convergence rate is applied to speed up searching optimal basic function data in function dictionary. The approach of intuitionistic fuzzy kernel matching pursuit based on particle swarm o...
Mozaffari, Mohammad Hamed; Lee, Won-Sook
2016-01-01
This paper proposes a new image thresholding segmentation approach using the heuristic method, Convergent Heterogeneous Particle Swarm Optimization algorithm. The proposed algorithm incorporates a new strategy of searching the problem space by dividing the swarm into subswarms. Each subswarm particles search for better solution separately lead to better exploitation while they cooperate with each other to find the best global position. The consequence of the aforementioned cooperation is bett...
Tao Zhang; Tiesong Hu; Jia-wei Chen; Zhongping Wan; Xuning Guo
2012-01-01
An elite quantum behaved particle swarm optimization (EQPSO) algorithm is proposed, in which an elite strategy is exerted for the global best particle to prevent premature convergence of the swarm. The EQPSO algorithm is employed for solving bilevel multiobjective programming problem (BLMPP) in this study, which has never been reported in other literatures. Finally, we use eight different test problems to measure and evaluate the proposed algorithm, including low dimension and high dimension ...
Dorin Sendrescu
2013-01-01
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 sc...
Directory of Open Access Journals (Sweden)
Imran Rahman
2015-01-01
Full Text Available Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA has been applied and compared with another member of swarm family, particle swarm optimization (PSO, considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.
Energy Technology Data Exchange (ETDEWEB)
Johnson, W.L.; Crane, J.K.; Hendricks, C.D.
1980-08-29
Advanced target designs require thicker (approx. 300 ..mu..m) coatings and better surface finishes that can be produced with current coating techniques. An advanced coating technique is proposed to provide maximum control of the coating flux and optimum manipulation of the shell during processing. In this scheme a small beam of ions or particles of known incident energy are collided with a levitated spherical mandrel. Precise control of the incident energy and angle of the deposition flux optimizes the control of the coating morphology while controlled rotation and noncontact support of the shell minimizes the possibility of particulate or damage generated defects. Almost infinite variability of the incident energy and material in this process provides increased flexibility of the target designs which can be physically realized.
International Nuclear Information System (INIS)
Advanced target designs require thicker (approx. 300 μm) coatings and better surface finishes that can be produced with current coating techniques. An advanced coating technique is proposed to provide maximum control of the coating flux and optimum manipulation of the shell during processing. In this scheme a small beam of ions or particles of known incident energy are collided with a levitated spherical mandrel. Precise control of the incident energy and angle of the deposition flux optimizes the control of the coating morphology while controlled rotation and noncontact support of the shell minimizes the possibility of particulate or damage generated defects. Almost infinite variability of the incident energy and material in this process provides increased flexibility of the target designs which can be physically realized
Charged particle acceleration with plasmas
International Nuclear Information System (INIS)
Under certain conditions it is possible to create spatial charge waves (OCE) in a plasma (ionized gas) through some disturbance mechanism, the phenomenon produces electric fields of high intensity that are propagated at velocities near to a c. When charged particles are connected to such OCE they may be accelerated to very high energies in short distances. At present electric fields of approximately 107 V/cm have been observed. (Author). 4 refs
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.
Face Recognition Using Particle Swarm Optimization-Based Selected Features
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Rabab M. Ramadan
2009-06-01
Full Text Available Feature selection (FS is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO. PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. The algorithm is applied to coefficients extracted by two feature extraction techniques: the discrete cosine transforms (DCT and the discrete wavelet transform (DWT. The proposedPSO-based feature selection algorithm is utilized to search the feature space for the optimal feature subset where features are carefully selected according to a well defined discrimination criterion. Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index. The classifier performance and the length of selected feature vector are considered for performance evaluation using the ORL facedatabase. Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.
A New Particle Swarm Optimization Based Stock Market Prediction Technique
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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.
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.
Optimasi Desain Heat Exchanger dengan Menggunakan Metode Particle Swarm Optimization
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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.
Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization
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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.
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.
Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization
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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.
Parallel particle swarm optimization algorithm in nuclear problems
International Nuclear Information System (INIS)
Particle Swarm Optimization (PSO) is a population-based metaheuristic (PBM), in which solution candidates evolve through simulation of a simplified social adaptation model. Putting together robustness, efficiency and simplicity, PSO has gained great popularity. Many successful applications of PSO are reported, in which PSO demonstrated to have advantages over other well-established PBM. However, computational costs are still a great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time consuming objective functions. To overcome such difficulty, parallel computation has been used. The default advantage of parallel PSO (PPSO) is the reduction of computational time. Master-slave approaches, exploring this characteristic are the most investigated. However, much more should be expected. It is known that PSO may be improved by more elaborated neighborhood topologies. Hence, in this work, we develop several different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by communication strategies in multiprocessor architectures. The proposed PPSOs have been applied to two complex and time consuming nuclear engineering problems: reactor core design and fuel reload optimization. After exhaustive experiments, it has been concluded that: PPSO still improves solutions after many thousands of iterations, making prohibitive the efficient use of serial (non-parallel) PSO in such kind of realworld problems; and PPSO with more elaborated communication strategies demonstrated to be more efficient and robust than the master-slave model. Advantages and peculiarities of each model are carefully discussed in this work. (author)
High-Dimensional Adaptive Particle Swarm Optimization on Heterogeneous Systems
International Nuclear Information System (INIS)
Much work has recently been reported in parallel GPU-based particle swarm optimization (PSO). Motivated by the encouraging results of these investigations, while also recognizing the limitations of GPU-based methods for big problems using a large amount of data, this paper explores the efficacy of employing other types of parallel hardware for PSO. Most commodity systems feature a variety of architectures whose high-performance capabilities can be exploited. In this paper, high-dimensional problems and those that employ a large amount of external data are explored within the context of heterogeneous systems. Large problems are decomposed into constituent components, and analyses are undertaken of which components would benefit from multi-core or GPU parallelism. The current study therefore provides another demonstration that ''supercomputing on a budget'' is possible when subtasks of large problems are run on hardware most suited to these tasks. Experimental results show that large speedups can be achieved on high dimensional, data-intensive problems. Cost functions must first be analysed for parallelization opportunities, and assigned hardware based on the particular task
Particle swarm optimization algorithm based low cost magnetometer calibration
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
Delay induced instabilities in self-propelling swarming particles
Forgoston, Eric; Schwartz, Ira
2008-03-01
We consider a general model of self-propelling biological or artificial individuals interacting through a pairwise attractive force in a two-dimensional system in the presence of noise and communication time delay. Previous work has shown that a large enough noise intensity will cause a translating swarm of individuals to transition to a rotating swarm with a stationary center of mass. In this work, we use numerical simulations to show that with the addition of a time delay, the model possesses a transition that depends on the size of the coupling parameter. This transition is independent of the swarm state (traveling or rotating) and is characterized by the alignment of all of the individuals along with a swarm oscillation. By considering the mean field equations without noise, we show that the time delay induced transition is associated with a Hopf bifurcation. The analytical result yields good agreement with numerical computations of the value of the coupling parameter at the Hopf point.
International Nuclear Information System (INIS)
As a part of resolving optical properties in atmosphere radiative transfer calculations, this paper focuses on obtaining aerosol optical thicknesses (AOTs) in the visible and near infrared wave band through indirect method by gleaning the values of aerosol particle size distribution parameters. Although various inverse techniques have been applied to obtain values for these parameters, we choose a stochastic particle swarm optimization (SPSO) algorithm to perform an inverse calculation. Computational performances of different inverse methods are investigated and the influence of swarm size on the inverse problem of computation particles is examined. Next, computational efficiencies of various particle size distributions and the influences of the measured errors on computational accuracy are compared. Finally, we recover particle size distributions for atmospheric aerosols over Beijing using the measured AOT data (at wavelengths λ=0.400, 0.690, 0.870, and 1.020 μm) obtained from AERONET at different times and then calculate other AOT values for this band based on the inverse results. With calculations agreeing with measured data, the SPSO algorithm shows good practicability.
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.
Huang, Haibin; Zhuang, Yufei
2015-08-01
This paper proposes a method that plans energy-optimal trajectories for multi-satellite formation reconfiguration in deep space environment. A novel co-evolutionary particle swarm optimization algorithm is stated to solve the nonlinear programming problem, so that the computational complexity of calculating the gradient information could be avoided. One swarm represents one satellite, and through communication with other swarms during the evolution, collisions between satellites can be avoided. In addition, a dynamic depth first search algorithm is proposed to solve the redundant search problem of a co-evolutionary particle swarm optimization method, with which the computation time can be shorten a lot. In order to make the actual trajectories optimal and collision-free with disturbance, a re-planning strategy is deduced for formation reconfiguration maneuver.
Zhi-gang Jia; Xing-xuan Wang
2015-01-01
An adaptive evolutionary strategy in standard particle swarm optimization is introduced. Adaptive evolution particle swarm optimization is constructed to improve the capacity of global search. A method based on adaptive evolution particle swarm optimization for identification of continuous system with time delay is proposed. The basic idea is that the identification of continuous system with time delay is converted to an optimization of continuous nonlinear function. The adaptive evolution pa...
Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization
Wang Chun-Feng; Liu Kui; Shen Pei-Ping
2014-01-01
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, t...
OPTIMIZATION OF PLY STACKING SEQUENCE OF COMPOSITE DRIVE SHAFT USING PARTICLE SWARM ALGORITHM
CHANNAKESHAVA K. R.; Mohan Kumar, S.; K Manjunath
2011-01-01
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 b...
Discrete Particle Swarm Optimization with Scout Particles for Library Materials Acquisition
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Yi-Ling Wu
2013-01-01
Full Text Available Materials acquisition is one of the critical challenges faced by academic libraries. This paper presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget, limitation of the number of materials in each category and each language. To tackle the constrained problem, we propose a discrete particle swarm optimization (DPSO with scout particles, where each particle, represented as a binary matrix, corresponds to a candidate solution to the problem. An initialization algorithm and a penalty function are designed to cope with the constraints, and the scout particles are employed to enhance the exploration within the solution space. To demonstrate the effectiveness and efficiency of the proposed DPSO, a series of computational experiments are designed and conducted. The results are statistically analyzed, and it is evinced that the proposed DPSO is an effective approach for the studied problem.
Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization
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.
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...
Magnetic guidance of charged particles
Dubbers, Dirk
2015-01-01
Many experiments and devices in physics use static magnetic fields to guide charged particles from a source onto a detector, and we ask the innocent question: What is the distribution of particle intensity over the detector surface? One should think that the solution to this seemingly simple problem is well known. We show that, even for uniform guide fields, this is not the case and present analytical point spread functions (PSF) for magnetic transport that deviate strongly from previous results. The "magnetic" PSF shows unexpected singularities, which were recently also observed experimentally, and which make detector response very sensitive to minute changes of position, field amplitude, or particle energy. In the field of low-energy particle physics, these singularities may become a source of error in modern high precision experiments, or may be used for instrument tests, for instance in neutrino mass retardation spectrometers.
Magnetic guidance of charged particles
Directory of Open Access Journals (Sweden)
Dirk Dubbers
2015-09-01
Full Text Available Many experiments and devices in physics use static magnetic fields to guide charged particles from a source onto a detector, and we ask the innocent question: What is the distribution of particle intensity over the detector surface? One should think that the solution to this seemingly simple problem is well known. We show that, even for uniform guide fields, this is not the case, and we present analytical point spread functions (PSF for magnetic transport that deviate strongly from previous results. The “magnetic” PSF shows unexpected singularities, which were recently also observed experimentally, and which make detector response very sensitive to minute changes of position, field amplitude, or particle energy. In the field of low-energy particle physics, these singularities may become a source of error in modern high precision experiments, or may be used for instrument tests.
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.
Purely hydrodynamic origin for swarming of swimming particles
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).
Geometrical charged-particle optics
Rose, Harald
2012-01-01
This second edition is an extended version of the first edition of Geometrical Charged-Particle Optics. The updated reference monograph is intended as a guide for researchers and graduate students who are seeking a comprehensive treatment of the design of instruments and beam-guiding systems of charged particles and their propagation in electromagnetic fields. Wave aspects are included in this edition for explaining electron holography, the Aharanov-Bohm effect and the resolution of electron microscopes limited by diffraction. Several methods for calculating the electromagnetic field are presented and procedures are outlined for calculating the properties of systems with arbitrarily curved axis. Detailed methods are presented for designing and optimizing special components such as aberration correctors, spectrometers, energy filters monochromators, ion traps, electron mirrors and cathode lenses. In particular, the optics of rotationally symmetric lenses, quadrupoles, and systems composed of these elements are...
Scattering theory relevant to the linear transport of particle swarms
International Nuclear Information System (INIS)
The long-time behavior of the velocity distribution of a spatially uniform diluted guest population of charged particles moving within a host medium under the influence of a D.C. electric field is studied within the framework of scattering theory. The authors prove the existence of wave and scattering operators for a simplified one-dimensional model of the linearized Boltzmann equation. The theory is applied to the study of the long-term behavior of electrons and the occurrence of traveling waves in runaway processes
Medium energy charged particle spectrometer
International Nuclear Information System (INIS)
The charged particle spectrometer E8 on HELIOS A and B will be described in some detail. It covers proton energies from 80 keV to 6 MeV, electrons from 20 keV to 2 MeV, and positrons from 150 to 550 keV. Its flight performance will be discussed. From examples of measurements the capability of the instrument will be demonstrated. (orig.)
Computations in Charged Particle Optics
Czech Academy of Sciences Publication Activity Database
Oral, Martin; Radlička, Tomáš
Brno: Institute of Scientific Instruments AS CR, v. v. i, 2014, s. 23-24. ISBN 978-80-87441-12-1. [Workshop of Interesting Topics of SEM and ESEM. Mikulov (CZ), 26.08.2014-31.08.2014] R&D Projects: GA MŠk EE.2.3.20.0103 Institutional support: RVO:68081731 Keywords : charged Particle Optics * computations Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering
Heavy charged particle radiotherapy trial
International Nuclear Information System (INIS)
Through mid-1985, a total of 49 patients received heavy-charged-particle irradiation for chordoma, chondrosarcoma, meningioma, or neurilemmoma of the base of skull or juxtaspinal area. The mean tumor dose was 68 Gray-equivalent, ranging from 26 to 80. Control within the irradiated area was obtained in 35 of 49. The median follow up in all 49 patients is 21 months, with a range from 3-90 months. Serious complications were seen in a small number of patients, with cranial nerve injury in two, transverse myelitis in one, and brain necrosis in three patients. In 42 patients with tumors of other histologies and/or sites, including tumors of paranasal sinuses, retroperitoneum, soft tissue and miscellaneous other sites, heavy charged particles were also used to deliver a higher tumor dose than possible with standard irradiation techniques. In the group, 21/42 (50%) have had local tumor control, also a good result considering the extent and the range of tumor types treated. The authors believe that there are a number of sites in addition to the juxtaspinal/base of skull tumors that will show long term benefit from treatment with heavy charged particles
Applying Sequential Particle Swarm Optimization Algorithm to Improve Power Generation Quality
Directory of Open Access Journals (Sweden)
Abdulhafid Sallama
2014-10-01
Full Text Available Swarm Optimization approach is a heuristic search method whose mechanics are inspired by the swarming or collaborative behaviour of biological populations. It is used to solve constrained, unconstrained, continuous and discrete problems. Swarm intelligence systems are widely used and very effective in solving standard and large-scale optimization, provided that the problem does not require multi solutions. In this paper, particle swarm optimisation technique is used to optimise fuzzy logic controller (FLC for stabilising a power generation and distribution network that consists of four generators. The system is subject to different types of faults (single and multi-phase. Simulation studies show that the optimised FLC performs well in stabilising the network after it recovers from a fault. The controller is compared to multi-band and standard controllers.
Direct charged particle imaging sensors
International Nuclear Information System (INIS)
CMOS image sensors optimized for charged particle imaging applications, such as electron microscopy and particle physics, have been designed and characterized. These directly image charged particles without reliance on performance-degrading hybrid technologies such as the use of scintillating materials. Based on standard CMOS active pixel sensor (APS) technology, the sensor arrays uses an 8-20 μm epitaxial layer that acts as a thicker sensitive region for the generation and collection of ionization electrons resulting from impinging high-energy particles. This results in a 100% fill factor and a far larger signal per incident electron than a standard CMOS photodiode could provide. A 512x550 pixels prototype has been fabricated and used extensively in an electron microscope, including having been used to take sample images. Temporal noise was measured to be 0.9 mV RMS, and the dynamic range was 60 dB. Power consumption at 70 frames/s is 20 mW. The full-width half-maximum of the collected ionization electron distribution was found to be 5.5 μm, yielding a spatial resolution of approximately 2.3 μm for individual incident electrons, and the modulation transfer function of the sensor at the Nyquist limit is to be 32%
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.
Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades
Li, H.; Chandrashekhara, K.
2015-09-01
Composite blade manufacturing for hydrokinetic turbine application is quite complex and requires extensive optimization studies in terms of material selection, number of layers, stacking sequence, ply thickness and orientation. To avoid a repetitive trial-and-error method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite lay-up optimization. Layer numbers, ply thickness and ply orientations were optimized using standard particle swarm optimization to minimize the weight of the composite blade while satisfying failure evaluation. To address the discrete combinatorial optimization problem of blade stacking sequence, a novel permutation discrete particle swarm optimization model was also developed to maximize the out-of-plane load-carrying capability of the composite blade. A composite blade design with significant material saving and satisfactory performance was presented. The proposed methodology offers an alternative and efficient design solution to composite structural optimization which involves complex loading and multiple discrete and combinatorial design parameters.
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
The dynamics of a charged particle
Rohrlich, Fritz
2008-01-01
Using physical arguments, I derive the physically correct equations of motion for a classical charged particle from the Lorentz-Abraham-Dirac equations (LAD) which are well known to be physically incorrect. Since a charged particle can classically not be a point particle because of the Coulomb field divergence, my derivation accounts for that by imposing a basic condition on the external force. That condition ensures that the particle's finite size charge distribution looks like a point charg...
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.
Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method
International Nuclear Information System (INIS)
Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.
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.
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.
Directory of Open Access Journals (Sweden)
Tao Zhang
2012-01-01
Full Text Available An elite quantum behaved particle swarm optimization (EQPSO algorithm is proposed, in which an elite strategy is exerted for the global best particle to prevent premature convergence of the swarm. The EQPSO algorithm is employed for solving bilevel multiobjective programming problem (BLMPP in this study, which has never been reported in other literatures. Finally, we use eight different test problems to measure and evaluate the proposed algorithm, including low dimension and high dimension BLMPPs, as well as attempt to solve the BLMPPs whose theoretical Pareto optimal front is not known. The experimental results show that the proposed algorithm is a feasible and efficient method for solving BLMPPs.
Directory of Open Access Journals (Sweden)
Jianli Ding
2013-03-01
Full Text Available In this study, we set the average taxi time of flight as the objective of the gate and runway assignment problem. We present a gate and runway combinatorial optimization model with several restrictions such as restrictions of gate and runway time, type of aircraft and service. We design a Discrete Particle Swarm Optimization (DPSO algorithm to solve this problem. Inspired by the genetic algorithm and combined with the neighborhood search, we propose a new location update strategy. Finally, numerical experiments were carried out on two cases where gate supplication is adequate and it’s not, experimental results show that the discrete particle swarm algorithm achieved very good results.
ACTIVITY BASED PERSON IDENTIFICATION USING PARTICLE SWARM OPTIMIZATION ALGORITHM
Sruthy Sebastian
2013-01-01
This paper presents a generic non-invasive person identification method that exploitsdiscriminative power of different activities performed by the same person. A multi-camera setup is used tocapture the human body from different viewing angles. Person identification, activity recognition, andviewing angle specification results are obtained for all the available cameras independently. Utilizing aparticle swarm optimization (PSO) and linear discriminant analysis (LDA) based algorithm, an unknow...
Interactions of charged dust particles in clouds of charges
Gundienkov, Vladimir; Yakovlenko, Sergey
2004-03-01
Two charged dust particles inside a cloud of charges are considered as Debye atoms forming a Debye molecule. Cassini coordinates are used for the numerical solution of the Poisson-Boltzmann equation for the charged cloud. The electric force acting on a dust particle by the other dust particle was determined by integrating the electrostatic pressure on the surface of the dust particle. It is shown that attractive forces appear when the following two conditions are satisfied. First, the average distance between dust particles should be approximately equal to two Debye radii. Second, attraction takes place when similar charges are concentrated predominantly on the dust particles. If the particles carry a small fraction of total charge of the same polarity, repulsion between the particles takes place at all distances. We apply our results to the experiments with thermoemission plasma and to the experiments with nuclear-pumped plasma.
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.
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.
DEFF Research Database (Denmark)
Vesterstrøm, Jacob Svaneborg; Thomsen, Rene
2004-01-01
Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in...... outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA....
New approach of decomposition of complex spectral contours based on particles swarm optimization
International Nuclear Information System (INIS)
The approach based on the stochastic algorithm of particle swarm optimization was used for mathematical treatment of spectral contours. It was shown that the method supposed allows one to separate complex model spectra and to determine parameters of spectral components. In the mathematical experiments a random fractal noise as a model of noise was applied
DEVELOPMENT AND INVESTIGATION OF THE EFFECTIVENESS OF THE PARTICLE SWARM OPTIMIZATION ALGORITHM
Akhmedova, Sh
2012-01-01
This article deals with investigation of the effectiveness of the Particle Swarm Optimization (PSO) [1] algorithm for solving constrained and unconstrained oneand multi-criteria optimization problems. Besides the investigations were conducted both the standard and the binary PSO. Also parallelized modifications of these algorithms were developed for multi-processor operations and two real-world problems were solved.
S. F. El-Zoghdy; M. A. Shohla; El-Sawy, A. A.; M. Nofal
2012-01-01
The starting time in the machine time scheduling problem will be assumed stochastic follows certain distribution. A hybrid algorithm combines the mutation operation with particle swarm optimization algorithm with constriction factor has been developed to find best starting time for each machine in each cycle when starting time follows normal distribution.
The Study on Food Sensory Evaluation based on Particle Swarm Optimization Algorithm
Hairong Wang; Huijuan Xu
2015-01-01
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.
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.
Particle Swarm Optimization of Speed in Unplanned Lane Traffic
Directory of Open Access Journals (Sweden)
Prasun Ghosal
2012-08-01
Full Text Available Analysis of Speed Optimization Technique in Traffic is a very promising research problem. Searching foran efficient optimization method to increase the degree of speed optimization and thereby increasing thetraffic flow in a lane is a widely concerning issue. However, there has been a limited research effort on theoptimization of the lane usage with speed optimization. This paper presents a novel technique to solve theproblem optimally using the knowledge base analysis of speeds of vehicles, population of lanes , usingpartial modification of Swarm Intelligence which, in turn will act as a guide for design of lanes optimally toprovide better optimized traffic with less number of transitions of vehicles between lanes..
Supplementary kinetic constants of charged particles
Ribaric, Marijan; Sustersic, Luka
2006-01-01
We put forward: (A) An improved description of classical, kinetic properties of a charged pointlike physical particle that consists, in addition to its mass and charge, also of the Eliezer and Bhabha kinetic constants; and (B) a proposal to evaluate these kinetic constants by considering the trajectories of charged particles in an acccelerator.
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.
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
A New Variation Particle Swarm Optimization for Multi-objective Reactive Power Optimization
Directory of Open Access Journals (Sweden)
Wenqing Zhao
2013-01-01
Full Text Available In order to overcome the particle swarm algorithm easy to fall into local optimal value and the lack of late slow convergence, this study presents a cloud model based on adaptive particle swarm optimization algorithm. The algorithm according to the fitness value of the particle populations of particles into the near optimal values closer to the optimal value and away from the optimal value of three subgroups and the generation of different populations to adopt a different strategy to generate inertia weight, where the normal cloud generator algorithm uses adaptive dynamic adjustment closer to the optimal particle subgroups of inertia weight, get rid of the shackles of algorithms into local optimum value; in the iteration algorithm uses the normal cloud to the mutation operation of the particle which makes the algorithm can quickly converge to the optimal solution. In summary presented Could Adaptive Variation Particle Swarm Optimization (CAVPSO to solve the multi-objective optimization problem of reactive power. Use standard IEEE30 node system to test simulation results show that the use of CAVPSO algorithms to solve multi-objective optimization of reactive power superiority.
Use of the particle swarm optimization algorithm for second order design of levelling networks
Yetkin, Mevlut; Inal, Cevat; Yigit, Cemal Ozer
2009-08-01
The weight problem in geodetic networks can be dealt with as an optimization procedure. This classic problem of geodetic network optimization is also known as second-order design. The basic principles of geodetic network optimization are reviewed. Then the particle swarm optimization (PSO) algorithm is applied to a geodetic levelling network in order to solve the second-order design problem. PSO, which is an iterative-stochastic search algorithm in swarm intelligence, emulates the collective behaviour of bird flocking, fish schooling or bee swarming, to converge probabilistically to the global optimum. Furthermore, it is a powerful method because it is easy to implement and computationally efficient. Second-order design of a geodetic levelling network using PSO yields a practically realizable solution. It is also suitable for non-linear matrix functions that are very often encountered in geodetic network optimization. The fundamentals of the method and a numeric example are given.
Rahman, Md Ashiqur; Anwar, Sohel; Izadian, Afshin
2016-03-01
In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific parameters of the electrochemical model of a Lithium-Ion battery with LiCoO2 cathode chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these parameter changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified model parameters were used to generate the respective battery models for both healthy and degraded batteries. These models were then validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.
Makino, Yohei; Fujii, Toshinori; Imai, Jun; Funabiki, Shigeyuki
Recently, it is desired to develop energy control technologies for environmental issues such as global warming and exhaustion of fossil fuel. Power fluctuations in large power consumers may cause the instability of electric power systems and increase the cost of the electric power facility and electricity charges. Developing the electric power-leveling systems (EPLS) to compensate the power fluctuations is necessary for future electric power systems. Now, EPLS with an SMES have been proposed as one of the countermeasures for the electric power quality improvement. The SMES is superior to other energy storage devices in response and storage efficiency. The authors proposed the EPLS based on fussy control with the SMES. For this practical implementation, optimizing control gain and SMES capacity is an important issue. This paper proposes a new optimization method of the EPLS. The proposed algorithm is novel particle swarm optimization based on taper-off reflectance (TRPSO). The proposed TRPSO optimizes the design variables of the EPLS efficiently and effectively.
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.
Spinning charged test particles and Cosmic Censorship
International Nuclear Information System (INIS)
The authors consider spinning charged test particles in the gravitational field of a rotating charged black hole, and it is shown that the hole cannot be destroyed, according to the Cosmic Censorship hypothesis. (Auth.)
Li Mao; Yu Mao; Changxi Zhou; Chaofeng Li; Xiao Wei; Hong Yang
2016-01-01
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 a...
Comparative learning global particle swarm optimization for optimal distributed generations' output
JAMIAN, Jasrul Jamani; MOKHLIS, Hazlie; Mustafa, Mohd Wazir
2014-01-01
The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new ...
Charge of dust particles in a particle chain
Yousefi, Razieh; Matthews, Lorin Swint; Hyde, Truell W
2016-01-01
Charged dust particles form structures which are extended in the vertical direction in the electrode sheath of a rf discharge when confined within a glass box. The charge on each particle as a function of height varies due to the changing plasma conditions and the wakefield of upstream particles. Here an analysis of the equilibrium state of chains of varying number of particles is analyzed to determine the charge on each particle within a vertically extended chain as well as the magnitude of the positive wakefield charge.
Particle Swarm Optimization of Speed in Unplanned Lane Traffic
Directory of Open Access Journals (Sweden)
Prasun Ghosal
2012-07-01
Full Text Available Analysis of Speed Optimization Technique in Traffic is a very promising research problem. Searching for an efficient optimization method to increase the degree of speed optimization and thereby increasing the traffic flow in a lane is a widely concerning issue. However, there has been a limited research effort on the optimization of the lane usage with speed optimization. This paper presents a novel technique to solve the problem optimally using the knowledge base analysis of speeds of vehicles, population of lanes , using partial modification of Swarm Intelligence which, in turn will act as a guide for design of lanes optimally to provide better optimized traffic with less number of transitions of vehicles between lanes..
Mingming Gao; Jingchang Nan; Surina Wang
2014-01-01
In order to design and optimize high-linearity power amplifier (PA), which with nonlinear and memory effect, it is very important to build power amplifier behavior modeling accurately. This paper proposes a power amplifier behavior modeling based on RBF neural network with improved chaos particle swarm optimization algorithm. To make the particles evenly distribute in the problem search space, a novel Chaos Particle Swarm Optimization (CPSO) is proposed based on the analysis of the ergodicity...
Worldline deviations of charged spinning particles
Energy Technology Data Exchange (ETDEWEB)
Heydari-Fard, M. [Department of Physics, Shahid Beheshti University, Evin, 19839 Tehran (Iran, Islamic Republic of); Mohseni, M. [Physics Department, Payame Noor University, 19395-4697 Tehran (Iran, Islamic Republic of)]. E-mail: m-mohseni@pnu.ac.ir; Sepangi, H.R. [Department of Physics, Shahid Beheshti University, Evin, 19839 Tehran (Iran, Islamic Republic of); Institute for Studies in Theoretical Physics and Mathematics, Tehran (Iran, Islamic Republic of)
2005-10-20
The geodesic deviation equation is generalized to worldline deviation equations describing the relative accelerations of charged spinning particles in the framework of Dixon-Souriau equations of motion.
Worldline deviations of charged spinning particles
Heydari-Fard, M; Sepangi, H R
2005-01-01
The geodesic deviation equation is generalized to worldline deviation equations describing the relative accelerations of charged spinning particles in the framework of Dixon-Souriau equations of motion.
Improvement of Interior Ballistic Performance Utilizing Particle Swarm Optimization
El Sadek, Hazem; Zhang, Xiaobing; Rashad, Mahmoud; Cheng, Cheng
2014-01-01
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 ...
Discrete ternary particle swarm optimization for area optimization of MPRM circuits
International Nuclear Information System (INIS)
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 Reed-Muller 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. (semiconductor integrated circuits)
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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.
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Vinay K. Jadoun
2014-10-01
Full Text Available In this study a Dynamically Controlled Particle Swarm Optimization (DCPSO method has been developed to solve Economic Emission Short-Term Hydrothermal Scheduling (EESTHS problem of power system with a variety of operational and network constraints. The inertial, cognitive and social behavior of the swarm is modified by introducing exponential functions for better exploration and exploitation of the search space. A new concept of preceding and aggregate experience of particle is proposed which makes PSO highly efficient. A correction algorithm is suggested to handle various constraints related to hydrothermal plants. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement. The effectiveness of the proposed method is investigated on two standard hydrothermal test systems considering various operational constraints. The application results show that the proposed DCPSO method is very promising.
International Nuclear Information System (INIS)
Highlights: ► Optimized fuzzy logic controller for a hybrid green power system is developed. ► PSO algorithm is used to optimize membership functions of controller. ► Optimized fuzzy logic controller results in lower O and M costs and LPSP. ► Optimization results in less variation of battery state of charge. - Abstract: The objective of this study is to develop an optimized fuzzy logic controller (FLC) for operating an autonomous hybrid green power system (HGPS) based on the particle swarm optimization (PSO) algorithm. An electrolyzer produces hydrogen from surplus energy generated by the wind turbine and photovoltaic array of HGPS for later use by a fuel cell. The PSO algorithm is used to optimize membership functions of the FLC. The FLC inputs are (a) net power flow and (b) batteries state of charge (SOC) and FLC output determines the time for hydrogen production or consumption. Actual data for weekly residential load, wind speed, ambient temperature, and solar irradiation are used for performance simulation and analysis of the HGPS examined. The weekly operation and maintenance (O and M) costs and the loss of power supply probability (LPSP) are considered in the optimization procedure. It is determined that FLC optimization results in (a) reduced fluctuations in batteries SOC which translates into longer life for batteries and the average SOC is increased by 6.18% and (b) less working hours for fuel cell, when the load is met by wind and PV. It is found that the optimized FLC results in lower O and M costs and LPSP by 57% and 33%, respectively, as compared to its un-optimized counterpart. In addition, a reduction of 18% in investment cost is achievable by optimal sizing and reducing the capacity of HGPS equipment.
Xu Bao
2013-01-01
One of the most important targets of routing algorithm for Wireless Sensor Network (WSN) is to prolong the network lifetime. Aimed at the features of WSN, a new routing optimization approach based on cloud adaptive particle swarm optimization algorithm is put forward in this paper. All paths appear at the same time in one round are fused in one particle, and the coding rule of particle is set down. The particle itself is defined as its position, the number of replaceable relay nodes in paths ...
Kinetic phenomena in charged particle transport in gases and plasmas
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Petrovic, Zoran Lj.; Dujko, Sasa; Sasic, Olivera; Stojanovic, Vladimir; Malovic, Gordana [Institute of Physics, University of Belgrade, POB 68 11080 Zemun (Serbia); Faculty of Traffic Engineering, University of Belgrade Belgrade (Serbia); Institute of Physics, University of Belgrade, POB 68 11080 Zemun (Serbia)
2012-05-25
The key difference between equilibrium (thermal) and non-equilibrium (low temperature - a.k.a. cold) plasmas is in the degree in which the shape of the cross sections influences the electron energy distribution function (EEDF). In this paper we will discuss the issue of kinetic phenomena from two different angles. The first will be how to take advantage of the strong influence and use low current data to obtain the cross sections. This is also known as the swarm technique and the product of a ''swarm analysis'' is a set of cross sections giving good number, momentum and energy balances of electrons or other charged particles. At the same time understanding the EEDF is based on the cross section data. Nevertheless sometimes the knowledge of the cross sections and even the behaviour of individual particles are insufficient to explain collective behaviour of the ensemble. The resulting ''kinetic'' effects may be used to favour certain properties of non-equilibrium plasmas and even may be used as the basis of some new plasma applications.
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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.
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.
Delay Equation for Charged Brown Particle
Vlasov, Alexander A.
2001-01-01
In previous work (physics/0004026) was shown, with the help of numerical calculations, that the effective Brown temperature for charged particle is lower than that for particle without charge. Here we derive this result without numerical calculations, integrating the delay equation analytically, as for zero, so for nonzero viscosity.
A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers
International Nuclear Information System (INIS)
Particle swarm optimization (PSO) method is a population-based optimization technique of swarm intelligence field in which each solution called “particle” flies around in a multidimensional problem search space. During the flight, every particle adjusts its position according to its own experience, as well as the experience of neighboring particles, using the best position encountered by itself and its neighbors. In this paper, a new quantum particle swarm optimization (QPSO) approach combined with Zaslavskii chaotic map sequences (QPSOZ) to shell and tube heat exchanger optimization is presented based on the minimization from economic view point. The results obtained in this paper for two case studies using the proposed QPSOZ approach, are compared with those obtained by using genetic algorithm, PSO and classical QPSO showing the best performance of QPSOZ. In order to verify the capability of the proposed method, two case studies are also presented showing that significant cost reductions are feasible with respect to traditionally designed exchangers. Referring to the literature test cases, reduction of capital investment up to 20% and 6% for the first and second cases, respectively, were obtained. Therefore, the annual pumping cost decreased markedly 72% and 75%, with an overall decrease of total cost up to 30% and 27%, respectively, for the cases 1 and 2, respectively, showing the improvement potential of the proposed method, QPSOZ. - Highlights: ► Shell and tube heat exchanger is minimized from economic view point. ► A new quantum particle swarm optimization (QPSO) combined with Zaslavskii chaotic map sequences (QPSOZ) is proposed. ► Reduction of capital investment up to 20% and 6% for the first and second cases was obtained. ► Annual pumping cost decreased 72% and 75%, with an overall decrease of total cost up to 30% and 27% using QPSOZ.
Chaos Cooperative Particle Swarm Optimization Based Water Level Control for Nuclear Steam Generator
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Sheng Guimin
2016-01-01
Full Text Available The Stability of SG (Steam Generator water level plays an important role in the safety of nuclear power plants, but it is difficult to tune the parameters of water level PID controller. A proposed novel algorithm, CCPSO (chaos cooperative particle swarm optimization, is used for tuning PID controller parameters. The (chaos particle swarm optimizationCPSO algorithm has the ability to avoid falling into local minimum and the (cooperative particle swarm optimizationCPSO-Sk has fast convergence in certain functions, so CCPSO algorithm is proposed to utilize the advantages of CPSO and CPSO-Sk. Therefore, half of the particles are updated in the CPSO-Sk, and the other half are updated in the CPSO. The information exchange of the optimal solutions obtained after the end of each iteration is the performance of CPSO-Sk and CPSO collaboration.The simulation results: compared with the PID controller whose parameters are tuned by ZN method, CCPSO show smaller overshoot, better stability, and shorter adjustment time. The simulation results show that the proposed method is effective for tuning PID parameters.
CLUSTERING BASED ADAPTIVE IMAGE COMPRESSION SCHEME USING PARTICLE SWARM OPTIMIZATION TECHNIQUE
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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.
Shao, Gui-Fang; Wang, Ting-Na; Liu, Tun-Dong; Chen, Jun-Ren; Zheng, Ji-Wen; Wen, Yu-Hua
2015-01-01
Pt-Pd alloy nanoparticles, as potential catalyst candidates for new-energy resources such as fuel cells and lithium ion batteries owing to their excellent reactivity and selectivity, have aroused growing attention in the past years. Since structure determines physical and chemical properties of nanoparticles, the development of a reliable method for searching the stable structures of Pt-Pd alloy nanoparticles has become of increasing importance to exploring the origination of their properties. In this article, we have employed the particle swarm optimization algorithm to investigate the stable structures of alloy nanoparticles with fixed shape and atomic proportion. An improved discrete particle swarm optimization algorithm has been proposed and the corresponding scheme has been presented. Subsequently, the swap operator and swap sequence have been applied to reduce the probability of premature convergence to the local optima. Furthermore, the parameters of the exchange probability and the 'particle' size have also been considered in this article. Finally, tetrahexahedral Pt-Pd alloy nanoparticles has been used to test the effectiveness of the proposed method. The calculated results verify that the improved particle swarm optimization algorithm has superior convergence and stability compared with the traditional one.
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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.
Scintillation Detectors for Charged Particles and Photons
Lecoq, P
2011-01-01
Scintillation Detectors for Charged Particles and Photons in 'Charged Particle Detectors - Particle Detectors and Detector Systems', part of 'Landolt-Börnstein - Group I Elementary Particles, Nuclei and Atoms: Numerical Data and Functional Relationships in Science and Technology, Volume 21B1: Detectors for Particles and Radiation. Part 1: Principles and Methods'. This document is part of Part 1 'Principles and Methods' of Subvolume B 'Detectors for Particles and Radiation' of Volume 21 'Elementary Particles' of Landolt-Börnstein - Group I 'Elementary Particles, Nuclei and Atoms'. It contains the Subsection '3.1.1 Scintillation Detectors for Charged Particles and Photons' of Section '3.1 Charged Particle Detectors' of Chapter '3 Particle Detectors and Detector Systems' with the content: 3.1.1 Scintillation Detectors for Charged Particles and Photons 3.1.1.1 Basic detector principles and scintillator requirements 3.1.1.1.1 Interaction of ionizing radiation with scintillator material 3.1.1.1.2 Important scint...
International Nuclear Information System (INIS)
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.
DAILY SCHEDULING OF SMALL HYDRO POWER PLANTS DISPATCH WITH MODIFIED PARTICLES SWARM OPTIMIZATION
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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.
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M. Nikzad
2011-08-01
Full Text Available In this study, the performance of supplementary stabilizer for static synchronous compensator (STATCOM tuned based on particle swarm optimization method in order to improvement of dynamic stability is studied. Since STATCOM is usually used for damping Low Frequency Oscillations (LFO, a supplementary stabilizer is incorporated with STATCOM to reach the mentioned purpose. As a heuristic optimization technique, particle swarm optimization (PSO is used for tuning the parameters of the STATCOM supplementary stabilizer. The IEEE 14 bus test system is considered for achieving simulation results .In order to show the ability of GA-based STATCOM to damp LFO, the system responses in case with and without STATCOM are compared. Also two different operation conditions i.e. normal and heavy have been considered for better examination of PSO-based STATCOM performance. Several nonlinear time-domain simulation tests visibly show the ability of STATCOM in damping of power system oscillations and consequently stability enhancement.
Reliability Allocation of Underwater Experiment System Based on Particle Swarm Optimization
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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.
A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch
Energy Technology Data Exchange (ETDEWEB)
Kumar, Rajesh; Sharma, Devendra; Sadu, Abhinav [Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur 302 017 (India)
2011-01-15
This paper presents a new multi-agent based hybrid particle swarm optimization technique (HMAPSO) applied to the economic power dispatch. The earlier PSO suffers from tuning of variables, randomness and uniqueness of solution. The algorithm integrates the deterministic search, the Multi-agent system (MAS), the particle swarm optimization (PSO) algorithm and the bee decision-making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realizes the purpose of optimization. The economic power dispatch problem is a non-linear constrained optimization problem. Classical optimization techniques like direct search and gradient methods fails to give the global optimum solution. Other Evolutionary algorithms provide only a good enough solution. To show the capability, the proposed algorithm is applied to two cases 13 and 40 generators, respectively. The results show that this algorithm is more accurate and robust in finding the global optimum than its counterparts. (author)
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.
Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization
Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard
2002-01-01
The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.
The application of particle swarm optimization to identify gamma spectrum with neural network
International Nuclear Information System (INIS)
Aiming at the shortcomings that BP algorithm is usually trapped to a local optimum and it has a low speed of convergence in the application of neural network to identify gamma spectrum, according to the advantage of the globe optimal searching of particle swarm optimization, this paper put forward a new algorithm for neural network training by combining BP algorithm and Particle Swarm Optimization-mixed PSO-BP algorithm. In the application to identify gamma spectrum, the new algorithm overcomes the shortcoming that BP algorithm is usually trapped to a local optimum and the neural network trained by it has a high ability of generalization with identification result of one hundred percent correct. Practical example shows that the mixed PSO-BP algorithm can effectively and reliably be used to identify gamma spectrum. (authors)
International Nuclear Information System (INIS)
The detection and estimation of gravitational wave signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Because of noise in the data, the function to be maximized is often highly multimodal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the particle swarm optimization method in this context. The method is applied to a test bed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that particle swarm optimization works well in the presence of high multimodality, making it a viable candidate method for further applications in gravitational wave data analysis.
Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm
International Nuclear Information System (INIS)
In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
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. PMID:26880881
Minimal K-Covering Set Algorithm based on Particle Swarm Optimizer
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Yong Hu
2013-12-01
Full Text Available For random high density distribution in wireless sensor networks in this article have serious redundancy problems. 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. Firstly, the network monitoring area is divided into a number of grid points. Utilization rate and the node minimum are used as optional objective, and a combinatorial optimization mathematical model is established. Then using Particle Swarm Optimizer to solve optimization model, thus the optimal network coverage and the utilization od sensor nodes are obtained. Simulation results that algorithm has reduced node redundancy and the energy consumption, and improved the network coverage effectively
Hybrid Chaotic Particle Swarm Optimization Based Gains For Deregulated Automatic Generation Control
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Cheshta Jain Dr. H. K. Verma
2011-12-01
Full Text Available Generation control is an important objective of power system operation. In modern power system, the traditional automatic generation control (AGC is modified by incorporating the effect of bilateral contracts. This paper investigates application of chaotic particle swarm optimization (CPSO for optimized operation of restructured AGC system. To obtain optimum gains of controllers, application of adaptive inertia weight factor and constriction factors is proposed to improve performance of particle swarm optimization (PSO algorithm. It is also observed that chaos mapping using logistic map sequence increases convergence rate of traditional PSO algorithm. The hybrid method presented in this paper gives global optimum gains of controller with significant improvement in convergence rate over basic PSO algorithm. The effectiveness and efficiency of the proposed algorithm have been tested on two area restructure system.
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A.I.Awad
2015-09-01
Full Text Available Task scheduling is one of the most important research topics in Cloud Computing environment. Dynamic Multi-objective task scheduling in Cloud Computing are proposed by using modified particle swarm optimization. This paper presents efficient allocation of tasks to available virtual machine in user level base on different parameters such as reliability, time, cost and load balancing of virtual machine. Agent used to create dynamic system. We propose mathematical model multi-objective Load Balancing Mutation particle swarm optimization (MLBMPSO to schedule and allocate tasks to resource. MLBMPSO considers two objective functions to minimize round trip time and total cost. Reliability can be achieved in system by getting task failure to allocate and reschedule with available resource based on load of virtual machine. Experimental results demonstrated that MLBMPSO outperformed the other algorithms in time and cost.
Speed Control of Switched Reluctance Motor Using New Hybrid Particle Swarm Optimization
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K. Thanushkodi
2012-01-01
Full Text Available Problem statement: The main objective of this research is to obtain the speed control of switched reluctance motor with minimum settling time and without overshoot. Approach: A new algorithm has been developed with the combination of differential evolution and particle swarm optimization and applied for speed control of switched reluctance motor under sudden change in speed. Also speed control of switched reluctance motor was obtained by other artificial intelligence methods such as fuzzy logic controller, fuzzy PI controller and particle swarm optimization based tuning of fuzzy PI controller. Matlab/Simulink environment was used for the simulation. Results: Results are discussed and tabulated based on the performance of the controllers. Conclusion: From the comparison of all above methods, the algorithm has given better results in speed response than other controllers.
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I. Cruz-Aceves
2013-01-01
Full Text Available This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO. The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.
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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.
Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization
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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.
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.
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.
Least Squares Fitting of Chacón-Gielis Curves by the Particle Swarm Method of Optimization
Mishra, SK
2006-01-01
Ricardo Chacón generalized Johan Gielis's superformula by introducing elliptic functions in place of trigonometric functions. In this paper an attempt has been made to fit the Chacón-Gielis curves (modified by various functions) to simulated data by the least squares principle. Estimation has been done by the Particle Swarm (PS) methods of global optimization. The Repulsive Particle Swarm optimization algorithm has been used. It has been found that although the curve-fitting exercise may be s...
Pengfei Jia; Shukai Duan; Jia Yan
2015-01-01
Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the number of iterations is limited. A novel way of computing the local attractor for QPSO is proposed to i...
Xue-cun Yang; Xiao-ru Yan; Chun-feng Song
2015-01-01
For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The a...
Hamid Reza Mohammadi; Ali Akhavan
2014-01-01
A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the namepl...
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.
Charged-particle activation analysis
International Nuclear Information System (INIS)
The paper discusses the methodology and application of nuclear activation with ion beams (19 via 16O(3He,p)18F, 12C(3He,α)11C and 14N(p,α)11C respectively. Recently, triton activation has been shown to be inherently still superior to 3He activation for the determination of oxygen [16O(3H,n)18F]. Lithium, boron, carbon and sulphur can be detected rapidly, nondestructively and with high sensitivity (approximately 0.25ppm for Li and B) via ''quasi-prompt'' activation based on the detection of short-lived, high-energy beta emitters (10ms1H(7Li,n)7Be for example. Nondestructive multielement analysis: Proton activation has the inherent potential for meeting requirements of broad elemental coverage, sensitivity (ppm and sub-ppm range) and selectivity. Up to 30 elements have been determined in Al, Co, Ag, Nb, Rh, Ta and biological samples, using 12-MeV proton activation followed by gamma-ray spectrometry. These capabilities are further enhanced with the counting of X-ray emitters, 28 elements (269) and accuracy using proton activation. 204Pb/206Pb ratios can also be determined with a relative precision of a few per cent. Although charged-particle activation analysis is a well-established trace analysis technique, broad potential capabilities remain to be explored, e.g. those arising from ultrashort-lived nuclides, heavy ion interactions and the combination of delayed and prompt methods. (author)
Democratic Inspired Particle Swarm Optimization for Multi-Robot Exploration Task
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...
International Nuclear Information System (INIS)
A new hybrid inversion method for depth profiling reconstruction of thermal conductivities of inhomogeneous solids is proposed based on multilayer quadrupole formalism of thermal waves, particle swarm optimization and sequential quadratic programming. The reconstruction simulations for several thermal conductivity profiles are performed to evaluate the applicability of the method. The numerical simulations demonstrate that the precision and insensitivity to noise of the inversion method are very satisfactory. (condensed matter: structure, mechanical and thermal properties)
Chen, Zhao-Jiang; Zhang, Shu-Yi
2010-02-01
A new hybrid inversion method for depth profiling reconstruction of thermal conductivities of inhomogeneous solids is proposed based on multilayer quadrupole formalism of thermal waves, particle swarm optimization and sequential quadratic programming. The reconstruction simulations for several thermal conductivity profiles are performed to evaluate the applicability of the method. The numerical simulations demonstrate that the precision and insensitivity to noise of the inversion method are very satisfactory.
Chia-Hung Lin; Jian-Liung Chen; Zwe-Lee Gaing
2010-01-01
This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate th...
EXTRUSION DIE PROFILE DESIGN USING SIMULATED ANNEALING ALGORITHM AND PARTICLE SWARM OPTIMIZATION
Directory of Open Access Journals (Sweden)
R.VENKETESAN
2010-08-01
Full Text Available In this paper a new method has been proposed for optimum shape design of extrusion die. The Design problem is formulated as an unconstrained optimization problem. Here nontraditional optimization techniques likeSimulated Annealing Algorithm and Particle Swarm Optimization are used to minimize the extrusion force by optimizing the extrusion ratio and die cone angle. Internal power of deformation is also calculated and results are compared.
Liang, J J; Song, H; B. Y. Qu; Liu, Z. F.
2014-01-01
In path planning problems, the most important task is to find a suitable collision-free path which satisfies some certain criteria (the shortest path length, security, feasibility, smoothness, and so on), so defining a suitable curve to describe path is essential. Three different commonly used curves are compared and discussed based on their performance on solving a set of path planning problems. Dynamic multiswarm particle swarm optimizer is employed to optimize the necessary parameters for ...
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Yudong Zhang; Shuihua Wang; Genlin Ji
2015-01-01
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridizatio...
UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization
Yudong Zhang; Lenan Wu; Shuihua Wang
2013-01-01
Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust ...
Image Edge Detection Based on Cellular Neural Network and Particle Swarm Optimization
Lili Li; Zhengxia Wang
2014-01-01
Edge detection is one of the basic pre-processing methods in digital image processing. In order to extract the edge of image effectively, this paper employs linear matrix inequality and particle swarm optimization (PSO) based on cellular neural networks (CNN). Among these templates obtained by using linear matrix inequality (LMI), we utilize the PSO to carry out the optimization parameters. The performance of the proposed edge detection method is evaluated on different test images and compare...
An Efficient Process Mining Method Based on Discrete Particle Swarm Optimization
Zhixiang Yin; Xin Gao; Xianwen Fang; Qianjin Zhao
2011-01-01
Process mining is to extract business process models from event logs, the mining process is an important learning task. However, the discovery of these processes poses many challenges, including noise, non-local, non-free choice constructs and so on. In the study, we give out the definition of the behavior redundancy degree which is benefit to analyze the behavior conformance. Then, in order to build the optimal the process model, a process mining method based on Discrete Particle Swarm Optim...
Multiagent and Particle Swarm Optimization for Ship Integrated Power System Network Reconfiguration
Zheng Wang; Li Xia; Yongji Wang; Lei Liu
2014-01-01
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 r...
Mavaddaty, Samira; Ebrahimzadeh, Ata
2011-01-01
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evaluate and compare the performance of these methods, we have focused on separation of noisy and noiseless sourc...
Optimasi Penyusunan Barang dalam Peti Kemas Menggunakan Algoritma Particle Swarm Optimization
Erny, Erny
2014-01-01
ABSTRAK Penyusunan barang dalam peti kemas adalah salah satu masalah penting untuk beberapa perusahaan. Karena salah satu cara untuk mendapatkan keuntungan yang sebesar-besarnya yaitu dengan mengoptimalkan penyusunan barang. Oleh sebab itu dibutuhkan sebuah algoritma yang dapat menghasilkan penyusunan barang agar peyimpanan barang menjadi optimal. Pada skripsi ini membahas solusi menggunakan algoritma Particle Swarm Optimization (PSO). Dalam algoritma PSO ini, struktur partikel yang di...
Jun-qing Li; Quan-ke Pan; Kun Mao
2014-01-01
A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedd...
Long Cheng; Yan Wang; Shuai Li
2015-01-01
With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective funct...
Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization
Na Tian; Zhicheng Ji
2015-01-01
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 t...
Xiaobing Yu; Jie Cao; Haiyan Shan; Li Zhu; Jun Guo
2014-01-01
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and D...
A two-layer surrogate-assisted particle swarm optimization algorithm
Sun, C.; Jin, Y.; Zeng, J; Yu, Y
2014-01-01
Like most evolutionary algorithms, particle swarm optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This poses an obstacle for applying PSO to computationally expensive problems. This paper proposes a two-layer surrogate-assisted PSO (TLSAPSO) algorithm, in which a global and a number of local surrogate models are employed for fitness approximation. The global surrogate model aims to smooth out the local optima of the original mu...
Yang Lu; Nianyin Zeng; Xiaohui Liu; Shujuan Yi
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. Th...
Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization
Supriya Dhabal; Palaniandavar Venkateswaran
2014-01-01
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 exper...
The Design of Frequency Filters of Iterative Feedback Tuning Using Particle Swarm Optimization
Arman Sharifi
2014-01-01
Iterative feedback tuning (IFT) is a data-based tuning approach that minimizes a quadratic performance index using some closed-loop experimental data. A control weighting coefficient, known as lambda, and two frequency filters are the most important parameters which can significantly improve the performance of the method. One of the major problems in IFT is tuning these parameters. This paper presents a new approach to tune frequency filters using particle swarm optimization (PSO). At the end...
Designing of Elastoplastic Adaptive Truss Structures with the Use of Particle Swarm Optimization
Jacek Szklarski; Marcin Wikło
2015-01-01
In the paper we demonstrate how Particle Swarm Optimization (PSO) can be employed to solve the Adaptive Impact Absorption (AIA) problem. We consider a truss structure which is subjected to impact loads. Stiff bars can be replaced by elastoplastic fuses which control theirs dynamical response. The point of optimization is to maximize or minimize a given objective function by redesigning the structure. This is realized by redistributing the initial mass, finding proper fuse localizations and ad...
Consensus Achievement of Decentralized Sensors Using Adapted Particle Swarm Optimization Algorithm
Hyunseok Kim; Seongju Chang; Jinsul Kim
2014-01-01
This paper explores the possibility of enhancing consensus achievement of decentralized sensors by establishing cooperative behavior between sensor agents. To these ends, a novel particle swarm optimization framework to achieve robust consensus of decentralized sensors is devised to distribute sensing information via local fusing with neighbors rather than through centralized control; the new framework showed a 16.5 percent improvement in consensus achievement as compared to the classic major...
Production Scheduling of Open Pit Mines Using Particle Swarm Optimization Algorithm
Asif Khan; Christian Niemann-Delius
2014-01-01
Determining an optimum long term production schedule is an important part of the planning process of any open pit mine; however, the associated optimization problem is demanding and hard to deal with, as it involves large datasets and multiple hard and soft constraints which makes it a large combinatorial optimization problem. In this paper a procedure has been proposed to apply a relatively new and computationally less expensive metaheuristic technique known as particle swarm optimization (P...
Ms. Prerana Shrivastava*
2016-01-01
In today’s wireless sensor networks mobile sinks plays an important role in data transmission and reception. Therefore it becomes very important to estimate the optimized position of the mobile sinks in order to improve the overall efficiency of the wireless sensor networks. In this paper, the particle swarm optimization technique has been used for the estimation of the position of the mobile sinks and its impact on the various performance factors of the wireless sensor network has been...
Minimal K-Covering Set Algorithm based on Particle Swarm Optimizer
Yong Hu
2013-01-01
For random high density distribution in wireless sensor networks in this article have serious redundancy problems. 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. Firstly, the network monitoring area is divided into a number of grid points. Utilization rate and the node minimum are used as optional objective, and a combinatorial optim...
Akanksha Mohan Gupte; Suryalok Sarkar; A. Karthikeyan
2013-01-01
Designing a WSN involves taking into account two most important design criterions. One is achieving the energy optimization and other is enhancing the network longevity. Particle Swarm Optimization (PSO) technique is an efficient protocol which is capable of achieving these deign goals. Now PSO algorithm can be designed either having a Single Cluster Head or Double Cluster Heads. This paper deals with the choice to be made out of these algorithms depending upon the Wireless Sensor Network (WS...
Dao-Wei Bi; Sheng Wang; Jun-Jie Ma; Xue Wang
2007-01-01
The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage...
Hanbing Liu; Gang Song; Yubo Jiao; Peng Zhang; Xianqiang Wang
2014-01-01
An approach to identify damage of bridge utilizing modal flexibility and neural network optimized by particle swarm optimization (PSO) is presented. The method consists of two stages; modal flexibility indices are applied to damage localizing and neural network optimized by PSO is used to identify the damage severity. Numerical simulation of simply supported bridge is presented to demonstrate feasibility of the proposed method, while comparative analysis with traditional BP network is for its...
GÖKDAĞ, Hakan
2013-01-01
In this work a crack identification method for beam type structures under moving vehicle is proposed. The basic of the method is to formulate damage detection as an inverse problem, and solve for damage locations and extents. To this end, an objective function is defined based on the difference of damaged beam dynamic response and the response calculated by the mathematical model of the beam. The optimization problem is solved through a popular evolutionary algorithm, i.e. the particle swarm ...
Xiangdong Qian; Maosen Cao; Zhongqing Su; Jiangang Chen
2012-01-01
Delamination is a type of representative damage in composite structures, severely degrading structural integrity and reliability. The identification of delamination is commonly treated as an issue of nondestructive testing. Differing from existing studies, a hybrid optimization algorithm (HOA), combining particle swarm optimization (PSO) with simplex method (SM), is proposed to identify delamination in laminated beams. The objective function of the optimization problem is created using delami...
DEFF Research Database (Denmark)
Nica, Florin Valentin Traian; Ritchie, Ewen; Leban, Krisztina Monika
2013-01-01
, genetic algorithm and particle swarm are shortly presented in this paper. These two algorithms are tested to determine their performance on five different benchmark test functions. The algorithms are tested based on three requirements: precision of the result, number of iterations and calculation time....... Both algorithms are also tested on an analytical design process of a Transverse Flux Permanent Magnet Generator to observe their performances in an electrical machine design application....
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models
Weng Kee Wong; Ray-Bing Chen; Chien-Chih Huang; Weichung Wang
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also...
Soleimani-Pouri, Mohammad; Rezvanian, Alireza; Meybodi, Mohammad Reza
2013-01-01
Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pherom...
Yusran
2013-01-01
The optimal aloccation of Distributed Generation (DG) was most important aspect of DG connected to electrical network scheme development. The methods to determine optimal allocation of DG like SGA dan PSO had weakness. The weakness was a large possibility to be trapped in local optimum solutions. Inertia weight (w) adding to PSO algorithm was a way to overcome the weakness. The developing method knew as Improved Particle Swarm Optimization (IPSO). This research used IPSO method fo...
S. Radhimeenakshi; G. M. Nasira
2014-01-01
Heart failure is one of the real cardio-vascular ailments influencing the center matured and the matured. It happens because of diminished cardiovascular yield. It can be both right-sided and left-sided failure of heart. This research study proposes a bio-inspired computing paradigm called particle swarm optimization shortly termed as PSO towards the prediction of heart failure. The implementation is carried out using java. The metrics such as time complexity and prediction accuracy are taken...
EXTRUSION DIE PROFILE DESIGN USING SIMULATED ANNEALING ALGORITHM AND PARTICLE SWARM OPTIMIZATION
R.VENKETESAN
2010-01-01
In this paper a new method has been proposed for optimum shape design of extrusion die. The Design problem is formulated as an unconstrained optimization problem. Here nontraditional optimization techniques likeSimulated Annealing Algorithm and Particle Swarm Optimization are used to minimize the extrusion force by optimizing the extrusion ratio and die cone angle. Internal power of deformation is also calculated and results are compared.
Y. Labbi; D. Ben Attous; H. Sarhoud
2015-01-01
Photovoltaic electricity is seen as an important source of renewable energy. The photovoltaic array is an unstable source of power since the peak power point depends on the temperature and the irradiation level. A maximum peak power point tracking is then necessary for maximum efficiency.In this work, a Particle Swarm Optimization (PSO) is proposed for maximum power point tracker for photovoltaic panel, are used to generate the optimal MPP, such that solar panel maximum power is generated und...
PARTICLE SWARM OPTIMIZATION OF SOLAR CENTRAL RECEIVER SYSTEMS FROM A MONTE CARLO DIRECT MODEL
Farges, Olivier; Bézian, Jean-Jacques; El Hafi, Mouna; Fudym, Olivier; Bru, Hélène
2013-01-01
Considering the investment needed to build a solar concentrating facility, the performance of such an installation has to be maximized. This is the reason why the preliminary design step is one of the most important stage of the project process. This paper presents an optimization approach coupling a Particle Swarm Optimization algorithm with a Monte Carlo algorithm applied to the design of Central Receiver Solar systems. After the validation of the direct model from experimental data, severa...
Tang, Y.; Wang, Z; J. Fang
2011-01-01
The official published version can be found at the link below. This paper presents a novel particle swarm optimization (PSO) algorithm based on Markov chains and competitive penalized method. Such an algorithm is developed to solve global optimization problems with applications in identifying unknown parameters of a class of genetic regulatory networks (GRNs). By using an evolutionary factor, a new switching PSO (SPSO) algorithm is first proposed and analyzed, where the velocity updating e...
Palma, Giuseppe; Bia, Pietro; Mescia, Luciano; Yano, Tetsuji; Nazabal, Virginie; Taguchi, Jun; Moréac, Alain; Prudenzano, Francesco
2013-01-01
A mid-IR amplifier consisting of a tapered chalcogenide fiber coupled to an Er3+-doped chalcogenide microsphere has been optimized via a particle swarm optimization (PSO) approach. More precisely, a dedicated three-dimensional numerical model, based on the coupled mode theory and solving the rate equations, has been integrated with the PSO procedure. The rate equations have included the main transitions among the erbium energy levels, the amplified spontaneous emission, and the most important...
Design of a Novel UWB Omnidirectional Antenna Using Particle Swarm Optimization
2015-01-01
A UWB E-plane omnidirectional microwave antenna is designed and fabricated for IEEE 802.11a communication system and microwave magnetron source system as a radiation monitor. A cooptimization method based on particle swarm optimization (PSO) algorithm and FDTD software is presented. The presented PSO algorithm is useful in many industrial microwave applications, such as microwave magnetron design and other techniques with a high power level. The maximum measured relative bandwidth of 65% is a...
Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm
Peilin Liu; Wenhao Leng; Wei Fang
2013-01-01
This paper proposes a novel method of training the parameters of adaptive-network-based fuzzy inference system (ANFIS). Different from the previous works which emphasized on gradient descent (GD) method, we present an approach to train the parameters of ANFIS by using an improved version of quantum-behaved particle swarm optimization (QPSO). This novel variant of QPSO employs an adaptive dynamical controlling method for the contraction-expansion (CE) coefficient which is the most influential ...
Viewpoint Selection Using Hybrid Simplex Search and Particle Swarm Optimization for Volume Rendering
Zhang You-sai,,,; Dai Chang-jiang; Wang Bin; Zhu Zhi-yu
2012-01-01
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 t...
Huawang Shi; Wanqing Li
2010-01-01
The current paper presents an approach that integrates soft-computing techniques in order to facilitate the computer-aided quality assessment of construction project. We confirmed the weight of each index quantitatively by mean s of Group-decision AHP according to an established index system. Then, we defined the elements of an assessment matrix using fuzzy and a quality assessment model for construction project is set up. The adoption of a particle swarm optimization (PSO) model to train per...
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....
Jing Zhao; Ming Li; Zhihong Wang
2013-01-01
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 ...
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Sree Bash Chandra Debnath; Pintu Chandra Shill; Kazuyuki Murase
2013-01-01
This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO). The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optim...
Zhiwei Ye; Mingwei Wang; Zhengbing Hu; Wei Liu
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three fa...
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...
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, ...
Kar, Subhajit; Sharma, Kaushik Das
2010-10-01
System identification is a ubiquitous necessity for successful applications in various fields. The area of system identification can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. Particle Swarm Optimization (PSO) is a stochastic, population-based optimization algorithm and many variants of PSO have been developed since, including constrained, multi objective, and discrete or combinatorial versions and applications have been developed using PSO in many fields. The basic PSO algorithm implicitly utilizes a fully connected neighborhood topology. However, local neighborhood models have also been proposed for PSO long ago, where each particle has access to the information corresponding to its immediate neighbors, according to a certain swarm topology. In this local neighborhood model of PSO, particles have information only of their own and their nearest neighbors' bests, rather than that of the entire population of the swarm. In the present work basic PSO method and two of its local neighborhood variants are utilized for determining the optimal parameters of a dc motor. The result obtain from the simulation study demonstrate the usefulness of the proposed methodology.
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.
Particle Swarm Optimization to the U-tube steam generator in the nuclear power plant
Energy Technology Data Exchange (ETDEWEB)
Ibrahim, Wesam Zakaria, E-mail: mimi9_m@yahoo.com
2014-12-15
Highlights: • We establish stability mathematical model of steam generator and reactor core. • We propose a new Particle Swarm Optimization algorithm. • The algorithm can overcome premature phenomenon and has a high search precision. • Optimal weight of steam generator is 15.1% less than the original. • Sensitivity analysis and optimal design provide reference for steam generator design. - Abstract: This paper, proposed an improved Particle Swarm Optimization approach for optimize a U-tube steam generator mathematical model. The UTSG is one of the most important component related to safety of most of the pressurized water reactor. The purpose of this article is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting Particle Swarm Optimization. PSO was applied in regarding the choice of the time intervals for the periodic testing of the model of the steam generator.
International Nuclear Information System (INIS)
Highlights: ► A method of synchronous optimization design of superconducting magnets is proposed. ► We get a refining design of a main magnet on Lanzhou Penning Trap by the method. ► We expounds the necessity of tracking optimizing of coils for magnets. ► Particle swarm optimization shows effectiveness in magnet optimization. ► The expected homogeneity of the magnet improves considerably. -- Abstract: A methodology of synchronous optimization design of magnets under construction according to original design scheme is put forward in this paper, and it has been successfully used for refining design of a superconducting magnet on Lanzhou Penning Trap (LPT). This paper expounds the necessity of tracking optimization of magnet coil in the process of traditional manufacturing, and optimization design of magnet coils by particle swarm optimization is proposed. Particle swarm optimization is turned out to be an effective design method for magnet optimization. The expected homogeneity of the magnet is improved to 200 ppm from 1150 ppm through the refining optimizing, which provides important guarantee for required homogeneity of the whole magnet
International Nuclear Information System (INIS)
Research highlights: → A closed-loop fuzzy logic controller based on the particle swarm optimization algorithm was proposed for controlling the power level of nuclear research reactors. → The proposed control system was tested for various initial and desired power levels, and it could control the reactor successfully for most situations. → The proposed controller is robust against the disturbances. - Abstract: In this paper, a closed-loop fuzzy logic controller based on the particle swarm optimization algorithm is proposed for controlling the power level of nuclear research reactors. The principle of the fuzzy logic controller is based on the rules constructed from numerical experiments made by means of a computer code for the core dynamics calculation and from human operator's experience and knowledge. In addition to these intuitive and experimental design efforts, consequent parts of the fuzzy rules are optimally (or near optimally) determined using the particle swarm optimization algorithm. The contribution of the proposed algorithm to a reactor control system is investigated in details. The performance of the controller is also tested with numerical simulations in numerous operating conditions from various initial power levels to desired power levels, as well as under disturbance. It is shown that the proposed control system performs satisfactorily under almost all operating conditions, even in the case of very small initial power levels.
Tao, Shanshan; Dong, Sheng; Wang, Zhifeng; Jiang, Wensheng
2016-06-01
The maximum entropy distribution, which consists of various recognized theoretical distributions, is a better curve to estimate the design thickness of sea ice. Method of moment and empirical curve fitting method are common-used parameter estimation methods for maximum entropy distribution. In this study, we propose to use the particle swarm optimization method as a new parameter estimation method for the maximum entropy distribution, which has the advantage to avoid deviation introduced by simplifications made in other methods. We conducted a case study to fit the hindcasted thickness of the sea ice in the Liaodong Bay of Bohai Sea using these three parameter-estimation methods for the maximum entropy distribution. All methods implemented in this study pass the K-S tests at 0.05 significant level. In terms of the average sum of deviation squares, the empirical curve fitting method provides the best fit for the original data, while the method of moment provides the worst. Among all three methods, the particle swarm optimization method predicts the largest thickness of the sea ice for a same return period. As a result, we recommend using the particle swarm optimization method for the maximum entropy distribution for offshore structures mainly influenced by the sea ice in winter, but using the empirical curve fitting method to reduce the cost in the design of temporary and economic buildings.
Particle Swarm Optimization to the U-tube steam generator in the nuclear power plant
International Nuclear Information System (INIS)
Highlights: • We establish stability mathematical model of steam generator and reactor core. • We propose a new Particle Swarm Optimization algorithm. • The algorithm can overcome premature phenomenon and has a high search precision. • Optimal weight of steam generator is 15.1% less than the original. • Sensitivity analysis and optimal design provide reference for steam generator design. - Abstract: This paper, proposed an improved Particle Swarm Optimization approach for optimize a U-tube steam generator mathematical model. The UTSG is one of the most important component related to safety of most of the pressurized water reactor. The purpose of this article is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting Particle Swarm Optimization. PSO was applied in regarding the choice of the time intervals for the periodic testing of the model of the steam generator
Directory of Open Access Journals (Sweden)
Dao-Wei Bi
2007-05-01
Full Text Available The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and DijkstraÃ¢Â€Â™s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications.
Directory of Open Access Journals (Sweden)
C. Srinivasa Rao
2008-06-01
Full Text Available This paper demonstrates the analysis of Automatic generation control (AGC of a two-area interconnected thyristor controlled phase shifter (TCPS based hydrothermal system in the continuous mode under open market scenario. Further the use of particle swarm optimization in optimizing the value of integral controller has also been reported. The effects of nonlinearities like deadband and generation rate constraint on the system have also been investigated. Open transmission access and the evolving of more socialized companies for generation, transmission and distribution affects the formulation of AGC problem. So the traditional AGC two-area system is modified to take into account the effect of bilateral contracts on the dynamics. A control strategy using TCPS is proposed to provide active control of system frequency. Gain settings of the integral controllers without and with TCPS are optimized using the particle swarm optimization following a step load disturbance in either of the areas. The results reported in this paper demonstrate the effectiveness of the particle swarm optimizer (PSO in the tuning of value of integral controller. The enhancement in the dynamic response of the power system is verified through simulation results.
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.
Directory of Open Access Journals (Sweden)
K.Prajna
2014-03-01
Full Text Available This research paper proposes a recently developed new variant of Particle Swarm Optimization (PSO called Accelerated Particle Swarm Optimization (APSO in speech enhancement application. Accelerated Particle Swarm Optimization technique is developed by Xin she Yang in 2010. APSO is simpler to implement and it has faster convergence when compared to the standard PSO (SPSO algorithm. Hence as an alternative to SPSO based speech enhancement algorithm, APSO is introduced to speech enhancement in the present paper. The present study aims to analyze the performance of APSO and to compare it with existing standard PSO algorithm, in the context of dual channel speech enhancement. Objective evaluation of the proposed method is carried out by using three objective measures of speech quality SNR, Improved SNR, PESQ and one objective measure of speech intelligibility FAI. The performance of the algorithm is studied under babble and factory noise environments. Simulation result proves that APSO based speech enhancement algorithm is superior to the standard PSO based algorithm with an improved speech quality and intelligibility measures.
Coagulation of charged particles in dust plasma
International Nuclear Information System (INIS)
One studied peculiarities of behaviour of small particles in dust plasma resulted on the one hand, from suppression of coagulation due to monopolar charging within the range of particle dimensions under the Debye radius of shielding and, on the other hand, from leveling of this case for particles of large dimensions. On the basis of similarity ratios one determined the range of parameters making linear approximation of particle charge dependence on their dimension true. In terms of the modified classical theory of coagulation in diffusion approximation one studied certain anomalies of behavior of dimension distribution of particles. It is determined that in contrast to the ordinary aerosol in dust plasma as time passes one may reduce dispersion of distribution and average dimensions of particles. For the first time one demonstrates the possibility to realize long-lived quasiliquid state of dust plasma associated with the anomalous behaviour of distribution function of coagulating charged particles according to dimensions
Mahapatra, Prasant Kumar; Sethi, Spardha; Kumar, Amod
2015-10-01
In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.
International Nuclear Information System (INIS)
In this paper, a new dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO) method is proposed to solve binary-state multi-objective reliability redundancy allocation problems (MORAPs). A combination of penalty function and modification strategies is used to handle the constraints in the MORAPs. A dynamic self-adaptive penalty function strategy is utilized to handle the constraints. A heuristic cost-benefit ratio is also supplied to modify the structure of violated swarms. An adaptive survey is conducted using several test problems to illustrate the performance of the proposed DSAMOPSO method. An efficient version of the epsilon-constraint (AUGMECON) method, a modified non-dominated sorting genetic algorithm (NSGA-II) method, and a customized time-variant multi-objective particle swarm optimization (cTV-MOPSO) method are used to generate non-dominated solutions for the test problems. Several properties of the DSAMOPSO method, such as fast-ranking, evolutionary-based operators, elitism, crowding distance, dynamic parameter tuning, and tournament global best selection, improved the best known solutions of the benchmark cases of the MORAP. Moreover, different accuracy and diversity metrics illustrated the relative preference of the DSAMOPSO method over the competing approaches in the literature. - Highlights: ► A meta-heuristic method is proposed to solve the redundancy allocation problems. ► The proposed method is statistically evaluated using multi-objective metrics. ► The proposed method outperforms the selected competing methods in the literature.
International Nuclear Information System (INIS)
This paper proposes a new swarm intelligence method known as the Particle-based Simplified Swarm Optimization (PSSO) algorithm while undertaking a modification of the Updating Mechanism (UM), called N-UM and R-UM, and simultaneously applying an Orthogonal Array Test (OA) to solve reliability–redundancy allocation problems (RRAPs) successfully. One difficulty of RRAP is the need to maximize system reliability in cases where the number of redundant components and the reliability of corresponding components in each subsystem are simultaneously decided with nonlinear constraints. In this paper, four RRAP benchmarks are used to display the applicability of the proposed PSSO that advances the strengths of both PSO and SSO to enable optimizing the RRAP that belongs to mixed-integer nonlinear programming. When the computational results are compared with those of previously developed algorithms in existing literature, the findings indicate that the proposed PSSO is highly competitive and performs well. - Highlights: • This paper proposes a particle-based simplified swarm optimization algorithm (PSSO) to optimize RRAP. • Furthermore, the UM and an OA are adapted to advance in optimizing RRAP. • Four systems are introduced and the results demonstrate the PSSO performs particularly well
Particle swarm optimization with random keys applied to the nuclear reactor reload problem
International Nuclear Information System (INIS)
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)
Weak charges of charmed particles
International Nuclear Information System (INIS)
The matrix elements between the lowest states of the ΔC=1 weak charges are evaluated including the effect of SU4 breaking. The charges are obtained from the corresponding generators of the classification group by a unitary transformation U, which is factorized as the product of operators acting on a single quark: the breaking is naturally introduced by having different mixing parameters for the different quarks
Parameter estimation for time-delay chaotic system by particle swarm optimization
International Nuclear Information System (INIS)
The knowledge about time delays and parameters is very important for control and synchronization of time-delay chaotic system. In this paper, parameter estimation for time-delay chaotic system is given by treating the time delay as an additional parameter. The parameter estimation is converted to an optimization problem, which finds a best parameter combination such that an objective function is minimized. Particle swarm optimization (PSO) is used to optimize the objective function through particles' cooperation and evolution. Two illustrative examples are given to show the validity of the proposed method.
Image Stitching based on Particle Swarm and Maximum Mutual Information Algorithm
Directory of Open Access Journals (Sweden)
Yu Zhang
2013-10-01
Full Text Available As a key link in image stitching, image registration based on maximum mutual information does not need any preprocess and has a high degree of automation and high registration accuracy, which thus attracted widespread attention. Optimization search in image registration process is easy to fall into local minima leading to the wrong registration parameters. This paper presents a method to build image pyramid based on wavelet transformation and uses swarm intelligence classical particle swarm optimization method to adjust its parameters with the mutual information and multi-resolution series. Experiments show that this method can effectively avoid local minima and can find the optimal registration transformation by finite optimization iterations. Performance of this proposed algorithm is better than the traditional multi-resolution method and its computational efficiency is higher than that of simulated annealing optimization method.
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.
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.
Binary particle swarm optimization algorithm assisted to design of plasmonic nanospheres sensor
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.
Application of multi-phase particle swarm optimization technique to inverse radiation problem
International Nuclear Information System (INIS)
The multi-phase particle swarm optimization (MPPSO) technique is applied to the inverse radiation problem in the present paper. The directional radiative intensities are served as the measurement data to estimate the radiative source term, optical thickness, scattering albedo, and phase function in one-dimensional semitransparent plane-parallel media by the inverse simulation. To check the performance and accuracy in retrieval, a comparison is presented between three PSO methods, i.e. the MPPSO, the standard PSO, and the Stochastic PSO. The results confirm the potential of the proposed approach MPPSO and show its effectiveness and superiority over the other two PSO algorithms. Furthermore, the effects of swarm size, searching space, phase change frequency, and velocity-reinitializing frequency on the convergence velocity and computational accuracy of MPPSO are also investigated
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.
Robust statistical reconstruction for charged particle tomography
Schultz, Larry Joe; Klimenko, Alexei Vasilievich; Fraser, Andrew Mcleod; Morris, Christopher; Orum, John Christopher; Borozdin, Konstantin N; Sossong, Michael James; Hengartner, Nicolas W
2013-10-08
Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.
New Charged Particles from Higgs Couplings
Cohen, Andrew G
2012-01-01
The recently reported observation of a new particle with mass about 125 GeV and couplings generally resembling those of the Standard Model Higgs boson provides a potential probe of the physics of electroweak symmetry breaking. Although the current data only provides hints, we suggest a particular combination of Higgs couplings as an assay for new charged particles connected with electroweak symmetry breaking, and construct a simple model with charge 5/3 quarks as a demonstration of its use.
Directory of Open Access Journals (Sweden)
Wenhua Han
2014-06-01
Full Text Available In this paper, efficient managing particle swarm optimization (EMPSO for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL signal. In the proposed EMPSO, in order to strengthen exchange of information among particles, particle pair model was built. For more efficient searching when facing different landscapes of problems, velocity updating scheme including three velocity updating models was also proposed. In addition, for more chances to search optimum solution out, automatic particle selection for re-initialization was implemented. The optimization results of six benchmark functions show EMPSO performs well when optimizing 100-D problems. The defect simulation results demonstrate that the inversing technique based on EMPSO outperforms the one based on self-learning particle swarm optimizer (SLPSO, and the estimated profiles are still close to the desired profiles with the presence of low noise in MFL signal. The results estimated from real MFL signal by EMPSO-based inversing technique also indicate that the algorithm is capable of providing an accurate solution of the defect profile with real signal. Both the simulation results and experiment results show the computing time of the EMPSO-based inversing technique is reduced by 20%–30% than that of the SLPSO-based inversing technique.
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
2016-01-01
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. PMID:27428971
NEURAL NETWORK TRAINING WITH PARALLEL PARTICLE SWARM OPTIMIZER
Institute of Scientific and Technical Information of China (English)
无
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...
Thickness of the particle swarm in cosmic ray air showers
Linsley, J.
1985-01-01
The average dispersion in arrival time of air shower particles detected with a scintillator at an impact parameter r is described with accuracy 5-10% by the empirical formula sigma = Sigma sub to (1+r/r sub t) sup b, where Sigma sub to = 2.6 ns, r sub t = 30m and b = (1.94 + or - .08) (0.39 + or - .06) sec Theta, for r 2 km, 10 to the 8th power E 10 to the 11th power GeV, and Theta 60 deg. (E is the primary energy and theta is the zenith angle). The amount of fluctuation in sigma sub t due to fluctuations in the level of origin and shower development is less than 20%. These results provide a basis for estimating the impact parameters of very larger showers with data from very small detector arrays (mini-arrays). The energy of such showers can then be estimated from the local particle density. The formula also provides a basis for estimating the angular resolution of air shower array-telescopes.
Directory of Open Access Journals (Sweden)
M. V. Rodrigues
2006-03-01
Full Text Available This work gives sequence to the study on the measurement of the electrostatic charges in aerosols. The particle charge classifier developed for this purpose and presented in the previous paper (Marra and Coury, 2000 has been used here to measure the particle charge distribution of a number of different aerosols. The charges acquired by the particles were naturally derived from the aerosol generation procedure itself. Two types of aerosol generators were used: the vibrating orifice generator and turntable Venturi plate generator. In the vibrating orifice generator, mono-dispersed particles were generated by a solution of water/ethanol/methylene blue, while in the rotating plate generator, six different materials were utilized. The results showed no clear dependence between electric charge and particle diameter for the mono-dispersed aerosol. However, for the poly-dispersed aerosols, a linear dependence between particle size and charge could be noticed.
Dynamic topology multi force particle swarm optimization algorithm and its application
Chen, Dongning; Zhang, Ruixing; Yao, Chengyu; Zhao, Zheyu
2016-01-01
Particle swarm optimization (PSO) algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization (DTMFPSO) algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization (MFPSO) algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing (FE) topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as µPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.
Institute of Scientific and Technical Information of China (English)
WEI Xiu-ye; PAN Hong-xia; HUANG Jin-ying; WANG Fu-jie
2009-01-01
Panicle swarm optimization (PSO) is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis (KPCA) for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration (CPSO) is applied to optimizing it. It is used for gearbox condition recognition, and the result is compared with the recognized results based on principal component analysis (PCA).The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA bused on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
2016-01-01
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. PMID:27428971
Particles with non abelian charges
Bastianelli, Fiorenzo; Corradini, Olindo; Latini, Emanuele
2013-01-01
Efficient methods for describing non abelian charges in worldline approaches to QFT are useful to simplify calculations and address structural properties, as for example color/kinematics relations. Here we analyze in detail a method for treating arbitrary non abelian charges. We use Grassmann variables to take into account color degrees of freedom, which however are known to produce reducible representations of the color group. Then we couple them to a U(1) gauge field defined on the worldline, together with a Chern-Simons term, to achieve projection on an irreducible representation. Upon gauge fixing there remains a modulus, an angle parametrizing the U(1) Wilson loop, whose dependence is taken into account exactly in the propagator of the Grassmann variables. We test the method in simple examples, the scalar and spin 1/2 contribution to the gluon self energy, and suggest that it might simplify the analysis of more involved amplitudes.
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.
Discrete Element Modeling of Triboelectrically Charged Particles
Hogue, Michael D.; Calle, Carlos I.; Weitzman, Peter S.; Curry, David R.
2008-01-01
Tribocharging of particles is common in many processes including fine powder handling and mixing, printer toner transport and dust extraction. In a lunar environment with its high vacuum and lack of water, electrostatic forces are an important factor to consider when designing and operating equipment. Dust mitigation and management is critical to safe and predictable performance of people and equipment. The extreme nature of lunar conditions makes it difficult and costly to carry out experiments on earth which are necessary to better understand how particles gather and transfer charge between each other and with equipment surfaces. DEM (Discrete Element Modeling) provides an excellent virtual laboratory for studying tribocharging of particles as well as for design of devices for dust mitigation and for other purposes related to handling and processing of lunar regolith. Theoretical and experimental work has been performed pursuant to incorporating screened Coulombic electrostatic forces into EDEM, a commercial DEM software package. The DEM software is used to model the trajectories of large numbers of particles for industrial particulate handling and processing applications and can be coupled with other solvers and numerical models to calculate particle interaction with surrounding media and force fields. While simple Coulombic force between two particles is well understood, its operation in an ensemble of particles is more complex. When the tribocharging of particles and surfaces due to frictional contact is also considered, it is necessary to consider longer range of interaction of particles in response to electrostatic charging. The standard DEM algorithm accounts for particle mechanical properties and inertia as a function of particle shape and mass. If fluid drag is neglected, then particle dynamics are governed by contact between particles, between particles and equipment surfaces and gravity forces. Consideration of particle charge and any tribocharging and
Charged particle concepts for fog dispersion
Frost, W.; Collins, F. G.; Koepf, D.
1981-01-01
Charged particle techniques hold promise for dispersing warm fog in the terminal area of commercial airports. This report focuses on features of the charged particle technique which require further study. The basic physical principles of the technique and the major verification experiments carried out in the past are described. The fundamentals of the nozzle operation are given. The nozzle characteristics and the theory of particle charging in the nozzle are discussed, including information from extensive literature on electrostatic precipitation relative to environmental pollution control and a description of some preliminary reported analyses on the jet characteristics and interaction with neighboring jets. The equation governing the transfer of water substances and of electrical charge is given together with a brief description of several semi-empirical, mathematical expressions necessary for the governing equations. The necessary ingredients of a field experiment to verify the system once a prototype is built are described.
Towards a microscopic theory of particle charging
Bronold, Franz X; Kersten, H; Deutsch, H
2009-01-01
We recently questioned the treatment of a dust particle as a perfect absorber for electrons and ions and proposed a surface model for the charge of a dust particle in a quiescent plasma which combines the microscopic physics at the grain boundary (sticking into and desorption from external surface states) with the macrophysics of the discharge (plasma collection fluxes). Within this model the charge and partial screening of the particle can be calculated without relying on the condition that the total electron collection flux balances on the grain surface the total ion collection flux. Grain charges obtained from our approach compared favorably with experimental data. The purpose of this paper is to describe our model in more detail, in particular, the hypotheses on which it is built, contrast it with the standard charging models based on flux balancing on the grain surface, and to analyze additional experimental data.
Fractionally charged particles in cosmic rays
Bashindzhagyan, George
2016-01-01
The results of many experiments on a search of fractionally charged particles in cosmic rays have been reviewed. The registered by ATIC and PAMELA experiments change of the proton energy spectrum at about 250 GeV can be explained if fractionally charged particles with another energy spectrum slope actually mixed with protons but cannot be separated because of a strong dE/dx fluctuations. The performed simulations show that multilayer detectors can seriously help in such separation. In the Aragats experiment performed using multilayer proportional counter combined with hadron calorimeter a group of 4e/3 like events with unexpectedly high average energy has been registered. It could be explained by their different from regular hadrons energy spectrum. The ATIC experiment ionization spectrum in single charged particle area has been examined. An interesting bump in 2e/3 charge region was observed. The events in the bump have very different from regular protons angular distribution.
Electro-optical detection of charged particles
Semertzidis, Y K; Kowalski, L A; Kraus, D E; Larsen, R; Lazarus, D M; Magurno, B; Nikas, D; Ozben, C; Srinivasan-Rao, T; Tsang, Thomas
2000-01-01
We have made the first observation of a charged particle beam by means of its electro-optical effect on the polarization of laser light in a LiNbO sub 3 crystal. The modulation of the laser light during the passage of a pulsed electron beam was observed using a fast photodiode and a digital oscilloscope. The fastest rise time measured, 120 ps, was obtained in the single shot mode and was limited by the bandwidth of the oscilloscope and the associated electronics. This technology holds good for detectors of greatly improved spatial and temporal resolution for single relativistic charged particles as well as particle beams.
Hybrid particle swarm optimization algorithm and its application in nuclear engineering
International Nuclear Information System (INIS)
Highlights: • We propose a hybrid particle swarm optimization algorithm (HPSO). • Modified Nelder–Mead simplex search method is applied in HPSO. • The algorithm has a high search precision and rapidly calculation speed. • HPSO can be used in the nuclear engineering optimization design problems. - Abstract: A hybrid particle swarm optimization algorithm with a feasibility-based rule for solving constrained optimization problems has been developed in this research. Firstly, the global optimal solution zone can be obtained through particle swarm optimization process, and then the refined search of the global optimal solution will be achieved through the modified Nelder–Mead simplex algorithm. Simulations based on two well-studied benchmark problems demonstrate the proposed algorithm will be an efficient alternative to solving constrained optimization problems. The vertical electrical heating pressurizer is one of the key components in reactor coolant system. The mathematical model of pressurizer has been established in steady state. The optimization design of pressurizer weight has been carried out through HPSO algorithm. The results show the pressurizer weight can be reduced by 16.92%. The thermal efficiencies of conventional PWR nuclear power plants are about 31–35% so far, which are much lower than fossil fueled plants based in a steam cycle as PWR. The thermal equilibrium mathematic model for nuclear power plant secondary loop has been established. An optimization case study has been conducted to improve the efficiency of the nuclear power plant with the proposed algorithm. The results show the thermal efficiency is improved by 0.5%
Hepatitis Disease Diagnosis Using Hybrid Case Based Reasoning and Particle Swarm Optimization
Mehdi Neshat; Mehdi Sargolzaei; Adel Nadjaran Toosi; Azra Masoumi
2012-01-01
Correct diagnosis of a disease is one of the most important problems in medicine. Hepatitis disease is one of the most dangerous diseases that affect millions of people every year and take man’s life. In this paper, the combination of two methods of PSO and CBR (case-based reasoning) has been used to diagnose hepatitis disease. First, a case-based reasoning method is workable to preprocess the data set therefore a weight vector for every one feature is extracted. A particle swarm optimization...
Production Scheduling of Open Pit Mines Using Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Asif Khan
2014-01-01
Full Text Available Determining an optimum long term production schedule is an important part of the planning process of any open pit mine; however, the associated optimization problem is demanding and hard to deal with, as it involves large datasets and multiple hard and soft constraints which makes it a large combinatorial optimization problem. In this paper a procedure has been proposed to apply a relatively new and computationally less expensive metaheuristic technique known as particle swarm optimization (PSO algorithm to this computationally challenging problem of the open pit mines. The performance of different variants of the PSO algorithm has been studied and the results are presented.
Application of Advanced Particle Swarm Optimization Techniques to Wind-thermal Coordination
DEFF Research Database (Denmark)
Singh, Sri Niwas; Østergaard, Jacob; Yadagiri, J.
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....
Directory of Open Access Journals (Sweden)
Boumediène ALLAOUA
2009-07-01
Full Text Available In this paper, an intelligent controller of DC Motor drive is designed using particle swarm optimization (PSO method for formative the optimal proportional-integral-derivative (PID controller Tuning parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Comparing with fuzzy logic controller using PSO intelligent algorithms, the planned method is more proficient in improving the speed loop response stability, the steady state error is reduced, the rising time is perfected and the disturbances do not affect the performances of driving motor with no overtaking.
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. PMID:22969355
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.
Fawad Zaman; I.M. Qureshi; A. Naveed; Khan, Z. U.
2012-01-01
In this study, we propose a method based on Particle Swarm Optimization for estimating Direction of Arrival of sources impinging on uniform linear array in the presence of noise. Mean Square Error is used as a fitness function which is optimum in nature and avoids any ambiguity among the angles that are supplement to each others. Multiple sources have been taken in the far field of the sensors array. In Case-I the sources are assumed to be far away from each other whereas, in case-II they are...
International Nuclear Information System (INIS)
Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was
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.
International Nuclear Information System (INIS)
We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra. (paper)
Early Mission Design of Transfers to Halo Orbits via Particle Swarm Optimization
Abraham, Andrew J.; Spencer, David B.; Hart, Terry J.
2016-06-01
Particle Swarm Optimization (PSO) is used to prune the search space of a low-thrust trajectory transfer from a high-altitude, Earth orbit to a Lagrange point orbit in the Earth-Moon system. Unlike a gradient based approach, this evolutionary PSO algorithm is capable of avoiding undesirable local minima. The PSO method is extended to a "local" version and uses a two dimensional search space that is capable of reducing the computation run-time by an order of magnitude when compared with published work. A technique for choosing appropriate PSO parameters is demonstrated and an example of an optimized trajectory is discussed.
Parameter estimation for chaotic system with initial random noises by particle swarm optimization
International Nuclear Information System (INIS)
This paper is concerned with the unknown parameters and time-delays of nonlinear chaotic systems with random initial noises. A scheme based on particle swarm optimization(PSO) is newly introduced to solve the problem via a nonnegative multi-modal nonlinear optimization, which finds a best combination of parameters and time-delays such that an objective function is minimized. The illustrative examples, in chaos systems with time-delays or free, are given to demonstrate the effectiveness of the present method.
Improved Quantum Evolutionary Computation Based on Particle Swarm Optimization and Two-Crossovers
International Nuclear Information System (INIS)
A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly
Application of particle swarm optimization to identify gamma spectrum with neural network
International Nuclear Information System (INIS)
In applying neural network to identification of gamma spectra back propagation (BP) algorithm is usually trapped to a local optimum and has a low speed of convergence, whereas particle swarm optimization (PSO) is advantageous in terms of globe optimal searching. In this paper, we propose a new algorithm for neural network training, i.e. combined BP and PSO optimization, or PSO-BP algorithm. Practical example shows that the new algorithm can overcome shortcomings of BP algorithm and the neural network trained by it has a high ability of generalization with identification result of 100% correctness. It can be used effectively and reliably to identify gamma spectra. (authors)
Directory of Open Access Journals (Sweden)
Akanksha Mohan Gupte
2013-04-01
Full Text Available Designing a WSN involves taking into account two most important design criterions. One is achieving the energy optimization and other is enhancing the network longevity. Particle Swarm Optimization (PSO technique is an efficient protocol which is capable of achieving these deign goals. Now PSO algorithm can be designed either having a Single Cluster Head or Double Cluster Heads. This paper deals with the choice to be made out of these algorithms depending upon the Wireless Sensor Network (WSN scenarios. PSO being a heuristic technique, it is very important to choose the efficient method in order to achieve an improved network lifetime along with reduction in energy consumption.
Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem
Institute of Scientific and Technical Information of China (English)
无
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.
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.
Directory of Open Access Journals (Sweden)
Weng Kee Wong
Full Text Available Particle Swarm Optimization (PSO is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1].
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.
Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237
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.
DESIGN OPTIMIZATION BY USING PARTICLE SWARM OPTIMIZATION IN MATLAB AND APDL IN ANSYS
M.AMRITA; SAROJINI Jajimoggala
2012-01-01
For optimization of real time problems it is difficult to generate equations which are to be optimized.Such problems can be optimized by using Design Optimization in ANSYS. But in many cases, it gives an optimum solution which is slightly infeasible which means it slightly violates the constraints. In this work our aim is to solve one of such practical problem in ANSYS and optimize it using optimization technique particle swarm optimization (PSO) run from mat-lab. It was found that the result...
Optimized control of multi-terminal DC grids using particle swarm optimization
Rouzbehi, Kumars; Miranian, Arash; Luna Alloza, Álvaro; Rodríguez Cortés, Pedro
2015-01-01
The electric networks of the future will make an extensive use of DC grids. Therefore, the control of Multi-terminal DC (MTDC) grids is a key issue, which is gathering the attention of the industry and the research community. In this regard, this paper proposes a grid control strategy for voltage-source converter (VSC)-based MTDC networks, based on the use of the particle swarm optimization (PSO) technique. In the proposed approach, the controllers of the power converters belonging to the MTD...
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...... to stabilize the frequency and voltage after the system enters into the islanding operation mode. The proposed algorithm is based on the formulation of an optimization problem using agent based PSO. The modified IEEE 9-bus system is employed to illustrate the performance of the proposed controller via RTDS...
Research on a Distribution Center Location Model Based on a Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Fei; HU Xin-bu; JIA Tao
2009-01-01
Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity.Transportation costs,distribution center construction costs,ordering costs,safe inventory costs and inventory holding costs are the important parts of the total logistics costs.In this paper,based on the research results of LMRP( location model of risk pooling) location with fixed construction cost,the LMRPVCC (location model of risk pooling based on variable construction cost) will be introduced.Applying particle swarm optimization to several computational instances,the authors find the suboptimum solution of the model.
Intrusion Detection System using Support Vector Machine (SVM and Particle Swarm Optimization (PSO
Directory of Open Access Journals (Sweden)
Vitthal Manekar
2014-09-01
Full Text Available Security and privacy of a system is vulnerable, when an intrusion happens. Intrusion Detection System (IDS takes an important role in network security as it detects various types of attacks in the network. In this paper, the propose Intrusion Detection System using data mining technique: SVM (Support Vector Machine and PSO (Particle Swarm Optimization. Here, first PSO performed parameter optimization using SVM to get the optimized value of C (cost and g (gamma parameter. Then PSO performed feature optimization to get optimized feature. Then these parameters and features are given to SVM to get higher accuracy. The experiment is performed by using NSL-KDD dataset.
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
Energy Technology Data Exchange (ETDEWEB)
Luo, Xiongbiao, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au [Robarts Research Institute, Western University, London, Ontario N6A 5K8 (Canada); Wan, Ying, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian [School of Computing and Communications, University of Technology, Sydney, New South Wales 2007 (Australia)
2015-04-15
Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was
A particle swarm model for estimating reliability and scheduling system maintenance
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.
Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell (PEMFC)
Institute of Scientific and Technical Information of China (English)
无
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.
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
Particle Swarm Optimization for Mobility Load Balancing SON in LTE Networks
Altman, Zwi; SALLEM, Soumaya; Nasri, Ridha; Sayrac, Berna; Clerc, Maurice
2013-01-01
This paper presents a self-optimizing solution for Mobility Load Balancing (MLB). The MLB-SON is performed in two phases. In the first, a MLB controller is designed using Multi-Objective Particle Swarm Optimization (MO-PSO) which incorporates a priori expert knowledge to considerably reduce the search space and optimization time. The dynamicity of the optimization phase is addressed. In the second phase, the controller is pushed into the base stations to implement the MLB SON. The method is a...
Particle Swarm Optimization for Mobility Load Balancing SON in LTE Networks
Altman, Zwi; SALLEM, Soumaya; Nasri, Ridha; Sayrac, Berna; Clerc, Maurice
2014-01-01
This paper presents a self-optimizing solution for Mobility Load Balancing (MLB). The MLB-SON is performed in two phases. In the first, a MLB controller is designed using Multi-Objective Particle Swarm Optimization (MO-PSO) which incorporates a priori expert knowledge to considerably reduce the search space and optimization time. The dynamicity of the optimization phase is addressed. In the second phase, the controller is pushed into the base stations to implement the MLB SON. The method is a...
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. PMID:22492176
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.
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.
Why do particle clouds generate electric charges?
Pähtz, T.; Herrmann, H. J.; Shinbrot, T.
2010-05-01
Grains in desert sandstorms spontaneously generate strong electrical charges; likewise volcanic dust plumes produce spectacular lightning displays. Charged particle clouds also cause devastating explosions in food, drug and coal processing industries. Despite the wide-ranging importance of granular charging in both nature and industry, even the simplest aspects of its causes remain elusive, because it is difficult to understand how inert grains in contact with little more than other inert grains can generate the large charges observed. Here, we present a simple yet predictive explanation for the charging of granular materials in collisional flows. We argue from very basic considerations that charge transfer can be expected in collisions of identical dielectric grains in the presence of an electric field, and we confirm the model's predictions using discrete-element simulations and a tabletop granular experiment.
Search milli-charged particles at SLAC
Energy Technology Data Exchange (ETDEWEB)
Langeveld, W.G.J. [Stanford Univ., CA (United States)
1997-01-01
Particles with electric charge q {triple_bond} Qe {le} 10{sup -3} e and masses in the range 1-1000 MeV/c{sup 2} are not excluded by present experiments or by astrophysical or cosmological arguments. A beam dump experiment uniquely suited to the detection of such {open_quotes}milli-charged{close_quotes} particles has been carried out at SLAC, utilizing the short-duration pulses of the SLC electron beam to establish a tight coincidence window for the signal. The detector, a large scintillation counter sensitive to very small energy depositions, provided much greater sensitivity than previous searches. Analysis of the data leads to the exclusion of a substantial portion of the charge-mass plane. In this report, a preliminary mass-dependent upper limit is presented for the charge of milli-charged particles, ranging from Q = 1.7 x 10{sup -5} at milli-charged particle mass 0.1 MeV/c{sup 2} to Q = 9.5 x 10{sup -4} at 100 MeV/c{sup 2}.
A New Mutated Quantum-Behaved Particle Swarm Optimizer for Digital IIR Filter Design
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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.
Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm
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.
Improved particle swarm optimization algorithm for multi-reservoir system operation
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Jun ZHANG
2011-03-01
Full Text Available In this paper, a hybrid improved particle swarm optimization (IPSO algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO algorithm is improved in two ways: (1 The linearly decreasing inertia weight coefficient (LDIWC is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC, which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2 The crossover and mutation idea inspired by the genetic algorithm (GA is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA algorithm.
An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem
Yue, Chen; Yan-Duo, Zhang; Jing, Lu; Hui, Tian
Traveling Salesman Problem (TSP) is one of NP-hard combinatorial optimization problems, which will experience “combination explosion” when the problem goes beyond a certain size. Therefore, it has been a hot topic to search an effective solving method. The general mathematical model of TSP is discussed, and its permutation and combination based model is presented. Based on these, Integer-coded Chaotic Particle Swarm Optimization for solving TSP is proposed. Where, particle is encoded with integer; chaotic sequence is used to guide global search; and particle varies its positions via “flying”. With a typical 20-citys TSP as instance, the simulation experiment of comparing ICPSO with GA is carried out. Experimental results demonstrate that ICPSO is simple but effective, and better than GA at performance.
International Nuclear Information System (INIS)
This paper proposes a short term hydroelectric plant dispatch model based on the rule of maximizing the benefit. For the optimal dispatch model, which is a large scale nonlinear planning problem with multi-constraints and multi-variables, this paper proposes a novel self-adaptive chaotic particle swarm optimization algorithm to solve the short term generation scheduling of a hydro-system better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision. The new method has been examined and tested on a practical hydro-system. The results are promising and show the effectiveness and robustness of the proposed approach in comparison with the traditional particle swarm optimization algorithm
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Xue-cun Yang
2015-01-01
Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.
International Nuclear Information System (INIS)
The Particle Swarm Optimization (PSO) algorithm is used to optimize the design of shell-and-tube heat exchangers and determine the optimal feasible solutions so as to eliminate trial-and-error during the design process. The design formulation takes into account the area and the total annual cost of heat exchangers as two objective functions together with operating as well as geometrical constraints. The Nonlinear Constrained Single Objective Particle Swarm Optimization (NCSOPSO) algorithm is used to minimize and find the optimal feasible solution for each of the nonlinear constrained objective functions alone, respectively. Then, a novel Nonlinear Constrained Mult-objective Particle Swarm Optimization (NCMOPSO) algorithm is used to minimize and find the Pareto optimal solutions for both of the nonlinear constrained objective functions together. The experimental results show that the two algorithms are very efficient, fast and can find the accurate optimal feasible solutions of the shell and tube heat exchangers design optimization problem. (orig.)
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Alexandre Carbonelli
2011-01-01
Full Text Available The aim of this work is to present the great performance of the numerical algorithm of Particle Swarm Optimization applied to find the best teeth modifications for multimesh helical gears, which are crucial for the static transmission error (STE. Indeed, STE fluctuation is the main source of vibrations and noise radiated by the geared transmission system. The microgeometrical parameters studied for each toothed wheel are the crowning, tip reliefs and start diameters for these reliefs. Minimization of added up STE amplitudes on the idler gear of a three-gear cascade is then performed using the Particle Swarm Optimization. Finally, robustness of the solutions towards manufacturing errors and applied torque is analyzed by the Particle Swarm algorithm to access to the deterioration capacity of the tested solution.
Bibliography of integral charged particle nuclear data
International Nuclear Information System (INIS)
This publication is the second supplement to the archival edition of the National Nuclear Data Center's charged-particle bibliography. This supplement contains citations to all references scanned since March 15, 1981, and all corrections and additions to previous citations, and indexes all data received in the international exchanged format (EXFOR). The primary goal of the bibliography has been to satisfy the need expressed by the Nuclear Reaction Data Center Network for a concise and comprehensive bibliography of integral charged-particle cross section data and to provide an index of data exchanged among the members. In 1980, coverage was expanded to include differential data relevant to charged-particle-induced neutron-source reactions
Integral charged particle nuclear data bibliography
Energy Technology Data Exchange (ETDEWEB)
Holden, N.E.; Ramavataram, S.
1989-12-01
This publication is the annual supplement to the first edition published in 1984. The primary goal of this publication has been to satisfy the need expressed by the Nuclear Reaction Data Center Network for a concise and comprehensive bibliography of integral charged--particle cross section data. Included in this paper is a partial list of other bibliographies relevant to charged-particle-induced reaction data and a source list of nuclear data bibliographies, complications, and cumulative, earlier versions are also shown in the tables. This publication makes use of a modification to the database of the Nuclear Structure References (NSR) file. This modification allows the retrieval of integral charged particle nuclear data entries from the NSR file. In recent years, the presentation of various sections was changed, as a result of users' suggestions. The authors continue to welcome users' comments.
Integral charged particle nuclear data bibliography
International Nuclear Information System (INIS)
This publication is the annual supplement to the first edition published in 1984. The primary goal of this publication has been to satisfy the need expressed by the Nuclear Reaction Data Center Network for a concise and comprehensive bibliography of integral charged--particle cross section data. Included in this paper is a partial list of other bibliographies relevant to charged-particle-induced reaction data and a source list of nuclear data bibliographies, complications, and cumulative, earlier versions are also shown in the tables. This publication makes use of a modification to the database of the Nuclear Structure References (NSR) file. This modification allows the retrieval of integral charged particle nuclear data entries from the NSR file. In recent years, the presentation of various sections was changed, as a result of users' suggestions. The authors continue to welcome users' comments
Position sensitive detector of magnetically charged particles
International Nuclear Information System (INIS)
Two variants of coordinate detectors of magnetically charged paticles based on the utilization of transducers with Josephson transitions are proposed. The system of data acquisition and processing is built up in the CAMAC standard. The helium cryostat diagram is presented. The detector resolution time and dead time constitute 1 μs. Methods of detection of magnetically charged particles used in the above detectors are expected to be more effective, as compared to ionization method and other indirect methods
Tuning of damping controller for UPFC using quantum particle swarm optimizer
International Nuclear Information System (INIS)
On the basis of the linearized Phillips-Herffron model of a single machine power system, we design optimally the unified power flow controller (UPFC) based damping controller in order to enhance power system low frequency oscillations. The problem of robustly UPFC based damping controller is formulated as an optimization problem according to the time domain-based objective function which is solved using quantum-behaved particle swarm optimization (QPSO) technique that has fewer parameters and stronger search capability than the particle swarm optimization (PSO), as well as is easy to implement. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed controller is demonstrated through non-linear time-domain simulation and some performance indices studies under various disturbance conditions of over a wide range of loading conditions. The results analysis reveals that the designed QPSO based UPFC controller has an excellent capability in damping power system low frequency oscillations in comparison with the designed classical PSO (CPSO) based UPFC controller and enhance greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions show that the δE based damping controller is superior to the mB based damping controller.
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An Liu
2012-01-01
Full Text Available Coordination optimization of directional overcurrent relays (DOCRs is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS and pickup current (Ip values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.
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Baskar, G.; Mohan, M.R. [Department of Electrical and Electronics Engineering, Anna University, Sadar Patel Road, Guindy, Chennai 600 025, Tamil Nadu (India)
2009-04-15
This paper presents a contingency constrained economic load dispatch (CCELD) using proposed improved particle swarm optimization (IPSO), conventional particle swarm optimization (PSO), evolutionary programming (EP) techniques such as classical EP (CEP), fast-EP (FEP) and mean of classical and fast EP (MFEP) to alleviate line overloading. Power system security enhancement deals with the task of taking remedial action against possible network overloads in the system following the occurrences of contingencies. Line overload can be removed by means of generation redispatching. In the proposed improved PSO, a new velocity strategy equation with scaling factor is proposed and the constriction factor approach (CFA) utilizes the eigen value analysis and controls the system behaviour. The CCELD problem is a twin objective function viz. minimization of fuel cost and minimization of severity index. This proposed IPSO-based CCELD approach generates higher quality solution in terms of optimal cost, minimum CPU time and minimum severity index than the other methods. Simulation results on IEEE-118 bus and IEEE-30 bus test systems are presented and compared with the results of other approaches. (author)
Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator
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Amin Jalali
2013-05-01
Full Text Available The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO is a Mamdani’s error based fuzzy logic controller (FLS with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO. Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model.
The Cartesian Path Planning of Free- Floating Space Robot using Particle Swarm Optimization
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Yangsheng Xu
2008-11-01
Full Text Available The Cartesian path planning of free-floating space robot is much more complex than that of fixed-based manipulators, since the end-effector pose (position and orientation is path dependent, and the position-level kinematic equations can not be used to determine the joint angles. In this paper, a method based on particle swarm optimization (PSO is proposed to solve this problem. Firstly, we parameterize the joint trajectory using polynomial functions, and then normalize the parameterized trajectory. Secondly, the Cartesian path planning is transformed to an optimization problem by integrating the differential kinematic equations. The object function is defined according to the accuracy requirement, and it is the function of the parameters to be defined. Finally, we use the Particle Swarm Optimization (PSO algorithm to search the unknown parameters. The approach has the following traits: 1 The limits on joint angles, rates and accelerations are included in the planning algorithm; 2 There exist not any kinematic and dynamic singularities, since only the direct kinematic equations are used; 3 The attitude singularities do not exist, for the orientation is represented by quaternion; 4 The optimization algorithm is not affected by the initial parameters. Simulation results verify the proposed method.
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
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Danping Yan
Full Text Available 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.
A multi-objective chaotic particle swarm optimization for environmental/economic dispatch
International Nuclear Information System (INIS)
A multi-objective chaotic particle swarm optimization (MOCPSO) method has been developed to solve the environmental/economic dipatch (EED) problems considering both economic and environmental issues. The proposed MOCPSO method has been applied in two test power systems. Compared with the conventional multi-objective particle swarm optimization (MOPSO) method, for the compromising minimum fuel cost and emission case, the fuel cost and pollutant emission obtained from MOCPSO method can be reduced about 50.08 $/h and 2.95 kg/h, respectively, in test system 1, about 0.02 $/h and 1.11 kg/h, respectively, in test system 2. The MOCPSO method also results in higher quality solutions for the minimum fuel cost case and the minimum emission case in both of the test power systems. Hence, MOCPSO method can result in great environmental and economic effects. For EED problems, the MOCPSO method is more feasible and more effective alternative approach than the conventional MOPSO method.
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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%.
International Nuclear Information System (INIS)
In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO-DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO-DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm.
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Omprakash Kaiwartya
2015-01-01
Full Text Available A multiobjective dynamic vehicle routing problem (M-DVRP has been identified and a time seed based solution using particle swarm optimization (TS-PSO for M-DVRP has been proposed. M-DVRP considers five objectives, namely, geographical ranking of the request, customer ranking, service time, expected reachability time, and satisfaction level of the customers. The multiobjective function of M-DVRP has four components, namely, number of vehicles, expected reachability time, and profit and satisfaction level. Three constraints of the objective function are vehicle, capacity, and reachability. In TS-PSO, first of all, the problem is partitioned into smaller size DVRPs. Secondly, the time horizon of each smaller size DVRP is divided into time seeds and the problem is solved in each time seed using particle swarm optimization. The proposed solution has been simulated in ns-2 considering real road network of New Delhi, India, and results are compared with those obtained from genetic algorithm (GA simulations. The comparison confirms that TS-PSO optimizes the multiobjective function of the identified problem better than what is offered by GA solution.
Ghamisi, Pedram; Couceiro, Micael S.; Benediktsson, Jon Atli
2012-11-01
Hyperspectral sensors generate detailed information about the earth's surface and climate in numerous contiguous narrow spectral bands, being widely used in resource management, agriculture, environmental monitoring, and others. However, due to the high dimensionality of hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for hyperspectral imagery. In this paper a new multilevel thresholding method for segmentation of hyperspectral images into different homogenous regions is proposed. The new method is based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. The FODPSO is used to solve the so-called Otsu problem for each channel of the hyperspectral data as a grayscale image that indicates the spectral response to a particular frequency in the electromagnetic spectrum. In other words, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results successfully compare the FODPSO with the traditional PSO for multi-level segmentation of hyperspectral images. The FODPSO acts better than the other method in terms of both CPU time and fitness, thus being able to find the optimal set of thresholds with a larger between-class variance in less computational time.
OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD
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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%.
PARTICLE SWARM AND NEURAL NETWORK APPROACH FOR FAULT CLEARING OF MULTILEVEL INVERTERS
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M. Sivakumar
2013-01-01
Full Text Available This study presents a machine learning technique for fault diagnostics in induction motor drives. A normal model and an extensive range of faulted models for the inverter-motor combination were developed and implemented using a generic commercial simulation tool to generate voltages and current signals at a broad range of operating points selected by a Particle Swarm Optimization (PSO based machine learning algorithm. A structured Particle Swarm (PS-neural network system has been designed, developed and trained to detect and isolate the most common types of faults: single switch open circuit faults, post-short circuits, short circuits and the unknown faults. Extensive simulation experiments were conducted to test the system with added noise and the results show that the structured neural network system which was trained by using the proposed machine learning approach gives high accuracy in detecting whether a faulty condition has occurred, thus isolating and pin-pointing to the type of faulty conditions occurring in power electronics inverter based electrical drives. Finally, the authors show that the proposed structured PS-neural network system has the capability of real-time detection of any of the faulty conditions mentioned above within 20 milliseconds or less.
International Nuclear Information System (INIS)
We present structural and thermal model (STM) tests of the BepiColombo laser altimeter (BELA) receiver baffle with emphasis on the correlation of the data with a thermal mathematical model. The test unit is a part of the thermal and optical protection of the BELA instrument being tested under infrared and solar irradiation at University of Bern. An iterative optimization method known as particle swarm optimization has been adapted to adjust the model parameters, mainly the linear conductivity, in such a way that model and test results match. The thermal model reproduces the thermal tests to an accuracy of 4.2 °C ± 3.2 °C in a temperature range of 200 °C after using only 600 iteration steps of the correlation algorithm. The use of this method brings major benefits to the accuracy of the results as well as to the computational time required for the correlation. - Highlights: ► We present model correlations of the BELA receiver baffle to thermal balance tests. ► Adaptive particle swarm optimization has been adapted for the correlation. ► The method improves the accuracy of the correlation and the computational time.
Advanced Adaptive Particle Swarm Optimization based SVC Controller for Power System Stability
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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.