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
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
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
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
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
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
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
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
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
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
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
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
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
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...
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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.
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)
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
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
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
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
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
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...
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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.
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).
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.
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
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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.
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
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...
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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.
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.
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...