Improved Chicken Swarm Optimization Method for Reentry Trajectory Optimization
Directory of Open Access Journals (Sweden)
Yu Wu
2018-01-01
Full Text Available Reentry trajectory optimization has been researched as a popular topic because of its wide applications in both military and civilian use. It is a challenging problem owing to its strong nonlinearity in motion equations and constraints. Besides, it is a high-dimensional optimization problem. In this paper, an improved chicken swarm optimization (ICSO method is proposed considering that the chicken swarm optimization (CSO method is easy to fall into local optimum when solving high-dimensional optimization problem. Firstly, the model used in this study is described, including its characteristic, the nonlinear constraints, and cost function. Then, by introducing the crossover operator, the principles and the advantages of the ICSO algorithm are explained. Finally, the ICSO method solving the reentry trajectory optimization problem is proposed. The control variables are discretized at a set of Chebyshev collocation points, and the angle of attack is set to fit with the flight velocity to make the optimization efficient. Based on those operations, the process of ICSO method is depicted. Experiments are conducted using five algorithms under different indexes, and the superiority of the proposed ICSO algorithm is demonstrated. Another group of experiments are carried out to investigate the influence of hen percentage on the algorithm’s performance.
Directory of Open Access Journals (Sweden)
Mehdi Neshat
2015-11-01
Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.
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.
SOLVING ENGINEERING OPTIMIZATION PROBLEMS WITH THE SWARM INTELLIGENCE METHODS
Directory of Open Access Journals (Sweden)
V. Panteleev Andrei
2017-01-01
Full Text Available An important stage in problem solving process for aerospace and aerostructures designing is calculating their main charac- teristics optimization. The results of the four constrained optimization problems related to the design of various technical systems: such as determining the best parameters of welded beams, pressure vessel, gear, spring are presented. The purpose of each task is to minimize the cost and weight of the construction. The object functions in optimization practical problem are nonlinear functions with a lot of variables and a complex layer surface indentations. That is why using classical approach for extremum seeking is not efficient. Here comes the necessity of using such methods of optimization that allow to find a near optimal solution in acceptable amount of time with the minimum waste of computer power. Such methods include the methods of Swarm Intelligence: spiral dy- namics algorithm, stochastic diffusion search, hybrid seeker optimization algorithm. The Swarm Intelligence methods are designed in such a way that a swarm consisting of agents carries out the search for extremum. In search for the point of extremum, the parti- cles exchange information and consider their experience as well as the experience of population leader and the neighbors in some area. To solve the listed problems there has been designed a program complex, which efficiency is illustrated by the solutions of four applied problems. Each of the considered applied optimization problems is solved with all the three chosen methods. The ob- tained numerical results can be compared with the ones found in a swarm with a particle method. The author gives recommenda- tions on how to choose methods parameters and penalty function value, which consider inequality constraints.
Lifecycle-Based Swarm Optimization Method for Numerical Optimization
Directory of Open Access Journals (Sweden)
Hai Shen
2014-01-01
Full Text Available Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO. Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.
A solution quality assessment method for swarm intelligence optimization algorithms.
Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
2014-01-01
Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.
A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms
Directory of Open Access Journals (Sweden)
Zhaojun Zhang
2014-01-01
Full Text Available Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of “value performance,” the “ordinal performance” is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and “good enough” set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO, particle swarm optimization (PSO, and artificial fish swarm algorithm (AFS were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.
Panorama parking assistant system with improved particle swarm optimization method
Cheng, Ruzhong; Zhao, Yong; Li, Zhichao; Jiang, Weigang; Wang, Xin'an; Xu, Yong
2013-10-01
A panorama parking assistant system (PPAS) for the automotive aftermarket together with a practical improved particle swarm optimization method (IPSO) are proposed in this paper. In the PPAS system, four fisheye cameras are installed in the vehicle with different views, and four channels of video frames captured by the cameras are processed as a 360-deg top-view image around the vehicle. Besides the embedded design of PPAS, the key problem for image distortion correction and mosaicking is the efficiency of parameter optimization in the process of camera calibration. In order to address this problem, an IPSO method is proposed. Compared with other parameter optimization methods, the proposed method allows a certain range of dynamic change for the intrinsic and extrinsic parameters, and can exploit only one reference image to complete all of the optimization; therefore, the efficiency of the whole camera calibration is increased. The PPAS is commercially available, and the IPSO method is a highly practical way to increase the efficiency of the installation and the calibration of PPAS in automobile 4S shops.
Luo, Yaqi; Zeng, Bi
2017-08-01
This paper researches the drainage routing problem in drainage pipe network, and propose an intelligent scheduling method. The method relates to the design of improved particle swarm optimization algorithm, the establishment of the corresponding model from the pipe network, and the process by using the algorithm based on improved particle swarm optimization to find the optimum drainage route in the current environment.
Directory of Open Access Journals (Sweden)
Bin He
2014-01-01
Full Text Available In city traffic, it is important to improve transportation efficiency and the spacing of platoon should be shortened when crossing the street. The best method to deal with this problem is automatic control of vehicles. In this paper, a mathematical model is established for the platoon’s longitudinal movement. A systematic analysis of longitudinal control law is presented for the platoon of vehicles. However, the parameter calibration for the platoon model is relatively difficult because the platoon model is complex and the parameters are coupled with each other. In this paper, the particle swarm optimization method is introduced to effectively optimize the parameters of platoon. The proposed method effectively finds the optimal parameters based on simulations and makes the spacing of platoon shorter.
Yang, Guo Sheng; Wang, Xiao Yang; Li, Xue Dong
2018-03-01
With the establishment of the integrated model of relay protection and the scale of the power system expanding, the global setting and optimization of relay protection is an extremely difficult task. This paper presents a kind of application in relay protection of global optimization improved particle swarm optimization algorithm and the inverse time current protection as an example, selecting reliability of the relay protection, selectivity, quick action and flexibility as the four requires to establish the optimization targets, and optimizing protection setting values of the whole system. Finally, in the case of actual power system, the optimized setting value results of the proposed method in this paper are compared with the particle swarm algorithm. The results show that the improved quantum particle swarm optimization algorithm has strong search ability, good robustness, and it is suitable for optimizing setting value in the relay protection of the whole power system.
Exergetic optimization of a thermoacoustic engine using the particle swarm optimization method
International Nuclear Information System (INIS)
Chaitou, Hussein; Nika, Philippe
2012-01-01
Highlights: ► Optimization of a thermoacoustic engine using the particle swarm optimization method. ► Exergetic efficiency, acoustic power and their product are the optimized functions. ► PSO method is used successfully for the first time in the TA research. ► The powerful PSO tool is advised to be more involved in the TA research and design. ► EE times AP optimized function is highly recommended to design any new TA devices. - Abstract: Thermoacoustic engines convert heat energy into acoustic energy. Then, the acoustic energy can be used to pump heat or to generate electricity. It is well-known that the acoustic energy and therefore the exergetic efficiency depend on parameters such as the stack’s hydraulic radius, the stack’s position in the resonator and the traveling–standing-wave ratio. In this paper, these three parameters are investigated in order to study and analyze the best value of the produced acoustic energy, the exergetic efficiency and the product of the acoustic energy by the exergetic efficiency of a thermoacoustic engine with a parallel-plate stack. The dimensionless expressions of the thermoacoustic equations are derived and calculated. Then, the Particle Swarm Optimization method (PSO) is introduced and used for the first time in the thermoacoustic research. The use of the PSO method and the optimization of the acoustic energy multiplied by the exergetic efficiency are novel contributions to this domain of research. This paper discusses some significant conclusions which are useful for the design of new thermoacoustic engines.
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
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...
Directory of Open Access Journals (Sweden)
Shaolong Chen
2016-01-01
Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.
Directory of Open Access Journals (Sweden)
Ruisheng Sun
2016-01-01
Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.
Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd
2018-03-01
Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.
Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
Wen-Yeau Chang
2013-01-01
High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...
Locating multiple optima using particle swarm optimization
CSIR Research Space (South Africa)
Brits, R
2007-01-01
Full Text Available Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems. This paper presents a new particle swarm optimization (PSO) technique to locate and refine multiple...
Zhao, Yong; Chen, Genliang; Wang, Hao; Lin, Zhongqin
2013-07-01
The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effective and efficient methods for the optimum selection among different types of mechanism candidates. This paper presents a new strategy for the purpose of optimum mechanism type selection based on the modified particle swarm optimization method. The concept of sub-swarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the sub-swarms to reassign their population size according to the relative performances of the mechanism candidates to implement the optimization. Combining with a modular modeling approach for fast calculation of the performance index of the potential candidates, the proposed method is applied to determine the optimum mechanism type among the potential candidates for the desired manipulator. The effectiveness and efficiency of the proposed method is demonstrated through a case study on the optimum selection of mechanism type of a heavy manipulator where six feasible candidates are considered with force capability as the specific performance index. The optimization result shows that the fitness of the optimum mechanism type for the considered heavy manipulator can be up to 0.578 5. This research provides the instruction in optimum selection of mechanism types for robotic manipulators.
Directory of Open Access Journals (Sweden)
Abdul Wadood
2018-04-01
Full Text Available In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA is implanted into the original particle swarm algorithm (PSO and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO are compared with other evolutionary optimization algorithms (EA and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution.
Directory of Open Access Journals (Sweden)
Jhon E. González-Pérez
2017-01-01
Full Text Available In this paper, a methodology for design of electrical field relaxing electrodes is shown. This design methodology is based in an optimization process carried out by particle swarm optimization technique. The objective function of the optimization process, include the electro statics model of the high voltage equipment that is solved by the finite element method. The proposed methodology was implemented using the computational tools Matlab and Comsol. This methodology was validated by designing the electric fields relaxing electrodes in a high voltage resistive divider, which used in measurement of lightning impulse waves.
Directory of Open Access Journals (Sweden)
A. P. Karpenko
2014-01-01
Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.
Hybrid chaotic ant swarm optimization
International Nuclear Information System (INIS)
Li Yuying; Wen Qiaoyan; Li Lixiang; Peng Haipeng
2009-01-01
Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm that is used to find the global optimum solution in search space. However, the CASO algorithm has some disadvantages, such as lower solution precision and longer computational time, when solving complex optimization problems. To resolve these problems, an improved CASO, called hybrid chaotic swarm optimization (HCASO), is proposed in this paper. The new algorithm introduces preselection operator and discrete recombination operator into the CASO; meanwhile it replaces the best position found by own and its neighbors' ants with the best position found by preselection operator and discrete recombination operator in evolution equation. Through testing five benchmark functions with large dimensionality, the experimental results show the new method enhances the solution accuracy and stability greatly, as well as reduces the computational time and computer memory significantly when compared to the CASO. In addition, we observe the results can become better with swarm size increasing from the sensitivity study to swarm size. And we gain some relations between problem dimensions and swam size according to scalability study.
A Parallel Particle Swarm Optimizer
National Research Council Canada - National Science Library
Schutte, J. F; Fregly, B .J; Haftka, R. T; George, A. D
2003-01-01
.... Motivated by a computationally demanding biomechanical system identification problem, we introduce a parallel implementation of a stochastic population based global optimizer, the Particle Swarm...
International Nuclear Information System (INIS)
Sun Jun; Fang Wei; Wang Daojun; Xu Wenbo
2009-01-01
In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO-DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO-DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm.
Guang, Chen; Qibo, Feng; Keqin, Ding; Zhan, Gao
2017-10-01
A subpixel displacement measurement method based on the combination of particle swarm optimization (PSO) and gradient algorithm (GA) was proposed for accuracy and speed optimization in GA, which is a subpixel displacement measurement method better applied in engineering practice. An initial integer-pixel value was obtained according to the global searching ability of PSO, and then gradient operators were adopted for a subpixel displacement search. A comparison was made between this method and GA by simulated speckle images and rigid-body displacement in metal specimens. The results showed that the computational accuracy of the combination of PSO and GA method reached 0.1 pixel in the simulated speckle images, or even 0.01 pixels in the metal specimen. Also, computational efficiency and the antinoise performance of the improved method were markedly enhanced.
Directory of Open Access Journals (Sweden)
Keivan Borna
2015-12-01
Full Text Available Traveling salesman problem (TSP is a well-established NP-complete problem and many evolutionary techniques like particle swarm optimization (PSO are used to optimize existing solutions for that. PSO is a method inspired by the social behavior of birds. In PSO, each member will change its position in the search space, according to personal or social experience of the whole society. In this paper, we combine the principles of PSO and crossover operator of genetic algorithm to propose a heuristic algorithm for solving the TSP more efficiently. Finally, some experimental results on our algorithm are applied in some instances in TSPLIB to demonstrate the effectiveness of our methods which also show that our algorithm can achieve better results than other approaches.
Directory of Open Access Journals (Sweden)
Kuei-Hsiang Chao
2013-01-01
Full Text Available This study investigated the output characteristics of photovoltaic module arrays with partial module shading. Accordingly, we presented a maximum power point tracking (MPPT method that can effectively track the global optimum of multipeak curves. This method was based on particle swarm optimization (PSO. The concept of linear decreases in weighting was added to improve the tracking performance of the maximum power point tracker. Simulation results were used to verify that this method could successfully track maximum power points in the output characteristic curves of photovoltaic modules with multipeak values. The results also established that the performance of the modified PSO-based MPPT method was superior to that of conventional PSO methods.
Color Image Enhancement Using Multiscale Retinex Based on Particle Swarm Optimization Method
Matin, F.; Jeong, Y.; Kim, K.; Park, K.
2018-01-01
This paper introduces, a novel method for the image enhancement using multiscale retinex and practical swarm optimization. Multiscale retinex is widely used image enhancement technique which intemperately pertains on parameters such as Gaussian scales, gain and offset, etc. To achieve the privileged effect, the parameters need to be tuned manually according to the image. In order to handle this matter, a developed retinex algorithm based on PSO has been used. The PSO method adjusted the parameters for multiscale retinex with chromaticity preservation (MSRCP) attains better outcome to compare with other existing methods. The experimental result indicates that the proposed algorithm is an efficient one and not only provides true color loyalty in low light conditions but also avoid color distortion at the same time.
Directory of Open Access Journals (Sweden)
Zhang De-Sheng
2016-01-01
Full Text Available Both efficiency and cavitation performance of the hydrofoil are the key technologies to design the tidal current turbine. In this paper, the hydrofoil efficiency and lift coefficient were improved based on particle swarm optimization method and XFoil codes. The cavitation performance of the optimized hydrofoil was also discussed by the computational fluid dynamic. Numerical results show the efficiency of the optimized hydrofoil was improved 11% ranging from the attack angle of 0-7° compared to the original NACA63-818 hydrofoil. The minimum pressure on leading edge of the optimized hydrofoil dropped above 15% at the high attack angle conditions of 10°, 15°, and 20°, respectively, which is benefit for the hydrofoil to avoiding the cavitation.
Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
Directory of Open Access Journals (Sweden)
Wen-Yeau Chang
2013-09-01
Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.
Sheu, Yae-Lin; Wang, Weichung; Hung, Yukai; Li, Pai-Chi
2010-02-01
Photoacoustic reconstruction for linear scanning geometry includes the delay-and-sum method, the spectral-domain method and the time-domain based method. In practice, the data collection using the planar detection geometry is not full-view, causing the details of the reconstructed object to be blurred and distorted. In addition to the exact formulation, we adopt a heuristic reconstruction method. In this paper, we demonstrate photoacoustic reconstruction for linear scanning geometry by formulating the image reconstruction into an optimization problem, and solve the problem with the particle swarm optimization (PSO) method. In this method, first we guess the initial optical energy distribution. According to photoacoustic model, described by the Helmholtz equation, the generated photoacoustic wave can be collected with planar detection geometry. The spherical Radon transform is adopted for the simulation of arbitrarily guessed optical energy distribution. Next we compare the collected signals generated from the guessed optical energy distribution with the measured signals by the sum of squared differences. By minimizing the error sum among various guesses, the initial optical energy distribution is obtained. In this regard, no limited-view is encountered. To guess the initial distribution efficiently such that the sum of the squared differences is minimized is an optimization problem with the dimension of unknowns being the size of the initial optical energy distribution. PSO is a derivative-free and population-based stochastic method that has been used to solve various optimization problems due to its simplicity and efficiency. High computational costs aroused from a large number of particles required can be alleviated with the use of the graphic processing units (GPUs). The proposed reconstruction method based on the PSO algorithm along with the spherical Radon transform is implemented on a NVIDIA Telsa C1060 GPU.
Directory of Open Access Journals (Sweden)
Hongjun Li
2012-01-01
Full Text Available This paper proposes a modified particle swarm optimization algorithm coupled with the finite element limit equilibrium method (FELEM for the minimum factor of safety and the location of associated noncircular critical failure surfaces for various geotechnical practices. During the search process, the stress compatibility constraints coupled with the geometrical and kinematical compatibility constraints are firstly established based on the features of slope geometry and stress distribution to guarantee realistic slip surfaces from being unreasonable. Furthermore, in the FELEM, based on rigorous theoretical analyses and derivation, it is noted that the physical meaning of the factor of safety can be formulated on the basis of strength reserving theory rather than the overloading theory. Consequently, compared with the limit equilibrium method (LEM and the shear strength reduction method (SSRM through several numerical examples, the FELEM in conjunction with the improved search strategy is proved to be an effective and efficient approach to routine analysis and design in geotechnical practices with a high level of confidence.
Novelty-driven Particle Swarm Optimization
DEFF Research Database (Denmark)
Galvao, Diana; Lehman, Joel Anthony; Urbano, Paulo
2015-01-01
Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higher-fitness areas. However......, in problems with many local optima, such focus often leads to premature convergence that precludes reaching the intended objective. To remedy this problem in certain types of domains, this paper introduces Novelty-driven Particle Swarm Optimization (NdPSO), which is motivated by the novelty search algorithm...... in genetic programming, this paper implements NdPSO as an extension of the grammatical swarm method, which combines PSO with genetic programming. The resulting NdPSO implementation is tested in three different domains representative of those in which it might provide advantage over objective-driven PSO...
Toushmalani, Reza
2013-01-01
The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. Second method [The Levenberg-Marquardt algorithm (LM)] is an approximation to the Newton method used also for training ANNs. In this paper first we discussed the gravity field of a fault, then describes the algorithms of PSO and LM And presents application of Levenberg-Marquardt algorithm, and a particle swarm algorithm in solving inverse problem of a fault. Most importantly the parameters for the algorithms are given for the individual tests. Inverse solution reveals that fault model parameters are agree quite well with the known results. A more agreement has been found between the predicted model anomaly and the observed gravity anomaly in PSO method rather than LM method.
Directory of Open Access Journals (Sweden)
Rocco Furferi
2016-10-01
Full Text Available An interesting current research field related to autonomous robots is mobile manipulation performed by cooperating robots (in terrestrial, aerial and underwater environments. Focusing on the underwater scenario, cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs is a complex and difficult application compared with the terrestrial or aerial ones because of many technical issues, such as underwater localization and limited communication. A decentralized approach for cooperative mobile manipulation of I-AUVs based on Artificial Neural Networks (ANNs is proposed in this article. This strategy exploits the potential field method; a multi-layer control structure is developed to manage the coordination of the swarm, the guidance and navigation of I-AUVs and the manipulation task. In the article, this new strategy has been implemented in the simulation environment, simulating the transportation of an object. This object is moved along a desired trajectory in an unknown environment and it is transported by four underwater mobile robots, each one provided with a seven-degrees-of-freedom robotic arm. The simulation results are optimized thanks to the ANNs used for the potentials tuning.
behaved particle swarm optimization (QPSO)
African Journals Online (AJOL)
Administrator
2011-06-13
Jun 13, 2011 ... popular OLS. Thus, QPSO-RBF estimator was more favorable to the control and fault diagnosis of the fermentation process, and consequently, it increased the yield of fermentation. Key words: Soft-sensing model, quantum-behaved particle swarm optimization algorithm, neural network. INTRODUCTION.
Optimal PMU Placement By Improved Particle Swarm Optimization
DEFF Research Database (Denmark)
Rather, Zakir Hussain; Liu, Leo; Chen, Zhe
2013-01-01
This paper presents an improved method of binary particle swarm optimization (IBPSO) technique for optimal phasor measurement unit (PMU) placement in a power network for complete system observability. Various effective improvements have been proposed to enhance the efficiency and convergence rate...... of conventional particle swarm optimization method. The proposed method of IBPSO ensures optimal PMU placement with and without consideration of zero injection measurements. The proposed method has been applied to standard test systems like 17 bus, IEEE 24-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus system...
Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches
Wu, Jui-Yu
2013-01-01
Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, so...
Hertono, G. F.; Ubadah; Handari, B. D.
2018-03-01
The traveling salesman problem (TSP) is a famous problem in finding the shortest tour to visit every vertex exactly once, except the first vertex, given a set of vertices. This paper discusses three modification methods to solve TSP by combining Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and 3-Opt Algorithm. The ACO is used to find the solution of TSP, in which the PSO is implemented to find the best value of parameters α and β that are used in ACO.In order to reduce the total of tour length from the feasible solution obtained by ACO, then the 3-Opt will be used. In the first modification, the 3-Opt is used to reduce the total tour length from the feasible solutions obtained at each iteration, meanwhile, as the second modification, 3-Opt is used to reduce the total tour length from the entire solution obtained at every iteration. In the third modification, 3-Opt is used to reduce the total tour length from different solutions obtained at each iteration. Results are tested using 6 benchmark problems taken from TSPLIB by calculating the relative error to the best known solution as well as the running time. Among those modifications, only the second and third modification give satisfactory results except the second one needs more execution time compare to the third modifications.
International Nuclear Information System (INIS)
Lu Songfeng; Sun Chengfu; Lu Zhengding
2010-01-01
This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.
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.
Directory of Open Access Journals (Sweden)
Hakan Gökdağ
2013-01-01
Full Text Available In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO, is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT. It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.
Software Project Scheduling Management by Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Dinesh B. Hanchate
2014-12-01
Full Text Available PSO (Particle Swarm Optimization is, like GA, a heuristic global optimization method based on swarm intelligence. In this paper, we present a particle swarm optimization algorithm to solve software project scheduling problem. PSO itself inherits very efficient local search method to find the near optimal and best-known solutions for all instances given as inputs required for SPSM (Software Project Scheduling Management. At last, this paper imparts PSO and research situation with SPSM. The effect of PSO parameter on project cost and time is studied and some better results in terms of minimum SCE (Software Cost Estimation and time as compared to GA and ACO are obtained.
Khatir, Samir; Dekemele, Kevin; Loccufier, Mia; Khatir, Tawfiq; Abdel Wahab, Magd
2018-02-01
In this paper, a technique is presented for the detection and localization of an open crack in beam-like structures using experimentally measured natural frequencies and the Particle Swarm Optimization (PSO) method. The technique considers the variation in local flexibility near the crack. The natural frequencies of a cracked beam are determined experimentally and numerically using the Finite Element Method (FEM). The optimization algorithm is programmed in MATLAB. The algorithm is used to estimate the location and severity of a crack by minimizing the differences between measured and calculated frequencies. The method is verified using experimentally measured data on a cantilever steel beam. The Fourier transform is adopted to improve the frequency resolution. The results demonstrate the good accuracy of the proposed technique.
Time Optimal Reachability Analysis Using Swarm Verification
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
and planning problems, response time optimization etc. We propose swarm verification to accelerate time optimal reachability using the real-time model-checker Uppaal. In swarm verification, a large number of model checker instances execute in parallel on a computer cluster using different, typically randomized...... search strategies. We develop four swarm algorithms and evaluate them with four models in terms scalability, and time- and memory consumption. Three of these cooperate by exchanging costs of intermediate solutions to prune the search using a branch-and-bound approach. Our results show that swarm...
Zhang, Bing; Sun, Xu; Gao, Lian-Ru; Yang, Li-Na
2011-09-01
For the inaccuracy of endmember extraction caused by abnormal noises of data during the mixed pixel decomposition process, particle swarm optimization (PSO), a swarm intelligence algorithm was introduced and improved in the present paper. By re-defining the position and velocity representation and data updating strategies, the algorithm of discrete particle swarm optimization (D-PSO) was proposed, which made it possible to search resolutions in discrete space and ultimately resolve combinatorial optimization problems. In addition, by defining objective functions and feasible solution spaces, endmember extraction was converted to combinatorial optimization problem, which can be resolved by D-PSO. After giving the detailed flow of applying D-PSO to endmember extraction and experiments based on simulative data and real data, it has been verified the algorithm's flexibility to handle data with abnormal noise and the reliability of endmember extraction were verified. Furthermore, the influence of different parameters on the algorithm's performances was analyzed thoroughly.
Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm
Yumin, Dong; Li, Zhao
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...
Study of particle swarm optimization particle trajectories
CSIR Research Space (South Africa)
Van den Bergh, F
2006-01-01
Full Text Available Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoretical analyses that concentrate on understanding particle trajectories...
Parallel Global Optimization with the Particle Swarm Algorithm (Preprint)
National Research Council Canada - National Science Library
Schutte, J. F; Reinbolt, J. A; Fregly, B. J; Haftka, R. T; George, A. D
2004-01-01
.... To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the Particle Swarm Optimization (PSO) algorithm...
Handayani, D.; Nuraini, N.; Tse, O.; Saragih, R.; Naiborhu, J.
2016-04-01
PSO is a computational optimization method motivated by the social behavior of organisms like bird flocking, fish schooling and human social relations. PSO is one of the most important swarm intelligence algorithms. In this study, we analyze the convergence of PSO when it is applied to with-in host dengue infection treatment model simulation in our early research. We used PSO method to construct the initial adjoin equation and to solve a control problem. Its properties of control input on the continuity of objective function and ability of adapting to the dynamic environment made us have to analyze the convergence of PSO. With the convergence analysis of PSO we will have some parameters that ensure the convergence result of numerical simulations on this model using PSO.
DEFF Research Database (Denmark)
Nica, Florin Valentin Traian; Ritchie, Ewen; Leban, Krisztina Monika
2013-01-01
, genetic algorithm and particle swarm are shortly presented in this paper. These two algorithms are tested to determine their performance on five different benchmark test functions. The algorithms are tested based on three requirements: precision of the result, number of iterations and calculation time...
Directory of Open Access Journals (Sweden)
Naeimeh Elkhani
2017-01-01
Full Text Available Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU. This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism.
Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem
Rahmalia, Dinita
2017-08-01
Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.
PARTICLE SWARM OPTIMIZATION BASED OF THE MAXIMUM ...
African Journals Online (AJOL)
2010-06-30
Jun 30, 2010 ... PARTICLE SWARM OPTIMIZATION BASED OF THE MAXIMUM. PHOTOVOLTAIC POWER TRACTIOQG UNDER DIFFERENT CONDITIONS. Y. Labbi*, D. Ben Attous and H. Sarhoud. Department of Electrotechnics, Faculty of Electrical Engineering El-Oued University. Center, Algeria. Received: 01 ...
LinkMind: link optimization in swarming mobile sensor networks.
Ngo, Trung Dung
2011-01-01
A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
Directory of Open Access Journals (Sweden)
Trung Dung Ngo
2011-08-01
Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
Directory of Open Access Journals (Sweden)
Ying Zhang
2016-02-01
Full Text Available Due to their special environment, Underwater Wireless Sensor Networks (UWSNs are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
2016-02-06
Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.
Elephant swarm water search algorithm for global optimization
Indian Academy of Sciences (India)
S Mandal
2018-02-07
Feb 7, 2018 ... Abstract. The rising complexity of real-life optimization problems has constantly inspired computer researchers to develop new efficient optimization methods. Evolutionary computation and metaheuristics based on swarm intelligence are very popular nature-inspired optimization techniques. In this paper ...
Chaos embedded particle swarm optimization algorithms
Energy Technology Data Exchange (ETDEWEB)
Alatas, Bilal [Firat University, Department of Computer Engineering, 23119 Elazig (Turkey)], E-mail: balatas@firat.edu.tr; Akin, Erhan [Firat University, Department of Computer Engineering, 23119 Elazig (Turkey)], E-mail: eakin@firat.edu.tr; Ozer, A. Bedri [Firat University, Department of Computer Engineering, 23119 Elazig (Turkey)], E-mail: bozer@firat.edu.tr
2009-05-30
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.
Chaos embedded particle swarm optimization algorithms
International Nuclear Information System (INIS)
Alatas, Bilal; Akin, Erhan; Ozer, A. Bedri
2009-01-01
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.
Gravity inversion of a fault by Particle swarm optimization (PSO).
Toushmalani, Reza
2013-01-01
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. In this paper we introduce and use this method in gravity inverse problem. We discuss the solution for the inverse problem of determining the shape of a fault whose gravity anomaly is known. Application of the proposed algorithm to this problem has proven its capability to deal with difficult optimization problems. The technique proved to work efficiently when tested to a number of models.
Heterogeneous architecture to process swarm optimization algorithms
Directory of Open Access Journals (Sweden)
Maria A. Dávila-Guzmán
2014-01-01
Full Text Available Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.
Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem
Directory of Open Access Journals (Sweden)
S Sarathambekai
2017-03-01
Full Text Available Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.
Cosmological parameter estimation using Particle Swarm Optimization
International Nuclear Information System (INIS)
Prasad, J; Souradeep, T
2014-01-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite
Cosmological parameter estimation using Particle Swarm Optimization
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Semisupervised Particle Swarm Optimization for Classification
Directory of Open Access Journals (Sweden)
Xiangrong Zhang
2014-01-01
Full Text Available A semisupervised classification method based on particle swarm optimization (PSO is proposed. The semisupervised PSO simultaneously uses limited labeled samples and large amounts of unlabeled samples to find a collection of prototypes (or centroids that are considered to precisely represent the patterns of the whole data, and then, in principle of the “nearest neighborhood,” the unlabeled data can be classified with the obtained prototypes. In order to validate the performance of the proposed method, we compare the classification accuracy of PSO classifier, k-nearest neighbor algorithm, and support vector machine on six UCI datasets, four typical artificial datasets, and the USPS handwritten dataset. Experimental results demonstrate that the proposed method has good performance even with very limited labeled samples due to the usage of both discriminant information provided by labeled samples and the structure information provided by unlabeled samples.
Particle Swarm Optimization With Interswarm Interactive Learning Strategy.
Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui
2016-10-01
The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.
Directory of Open Access Journals (Sweden)
Hong-Yun Zhang
2012-09-01
Full Text Available Quantum-behaved particle swarm optimization (QPSO is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE, particle swarm optimization (PSO and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems.
2016-03-01
optimization problem is by using Lagrange multipliers . First and foremost, the constraint must be expressed 60 algebraically in the form (1, ..., ...formulation. Once all constraints have been expressed, scalar constants known as Lagrange multipliers (1...) can be introduced to the...the unknowns [37]. = 0; = 1 to (65) = ; = 1 to (66) The reason why the Lagrange multipliers are appended is because
Optimal swarm formation for odor plume finding.
Marjovi, Ali; Marques, Lino
2014-12-01
This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors, and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.
PARTICLE SWARM OPTIMIZATION- EVOLUTION, OVERVIEW AND APPLICATIONS
Dr.R.Umarani,; V.Selvi
2010-01-01
The present work interprets on Particle Swarm Optimization and simple software agents so called particles, move in the explore breathing space of an optimization problem. The position of a particle represents a solution to the optimization problem at hand. Each particle searches for better positions in the search space by changing its velocity according to rules originally inspired by behavioral models of bird flocking. The outlines of the paper explicate the overview, evolution, applications...
Komsiyah, S.
2014-03-01
The objective in this paper is about economic dispatch problem of electric power generation where scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. In the operating of electric power system, an economic planning problem is one of variables that its must be considered since economically planning will give more efficiency in operational cost. In this paper the economic dispatch problem which has non linear cost function solved by using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving inspired by swarm intelligent and probabilities theories. To analize its accuracy, the economic dispatch solution by GPSO method will be compared with Lagrange multiplier method. From the running test result the GPSO method give economically planning calculation which it better than Lagrange multiplier method and the GPSO method faster to getting error convergence. Therefore the GPSO method have better performance to getting global best solution than the Lagrange method.
Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah
2015-01-01
The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.
Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging
DEFF Research Database (Denmark)
Rutkowski Olesen, Jakob; Cordero, Jorge; Zeng, Yifeng
2009-01-01
This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data...... by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid...
Selectively-informed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
2015-03-19
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
DEFF Research Database (Denmark)
Ngo, Trung Dung
2012-01-01
of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...
Improved quantum-behaved particle swarm optimization with local search strategy
Directory of Open Access Journals (Sweden)
Maolong Xi
2017-03-01
Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.
Glowworm swarm optimization theory, algorithms, and applications
Kaipa, Krishnanand N
2017-01-01
This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intellige...
An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.
Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun
2017-09-01
The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.
Swarm intelligence metaheuristics for enhanced data analysis and optimization.
Hanrahan, Grady
2011-09-21
The swarm intelligence (SI) computing paradigm has proven itself as a comprehensive means of solving complicated analytical chemistry problems by emulating biologically-inspired processes. As global optimum search metaheuristics, associated algorithms have been widely used in training neural networks, function optimization, prediction and classification, and in a variety of process-based analytical applications. The goal of this review is to provide readers with critical insight into the utility of swarm intelligence tools as methods for solving complex chemical problems. Consideration will be given to algorithm development, ease of implementation and model performance, detailing subsequent influences on a number of application areas in the analytical, bioanalytical and detection sciences.
A Review of Particle Swarm Optimization
Jain, N. K.; Nangia, Uma; Jain, Jyoti
2018-03-01
This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995-2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.
Directory of Open Access Journals (Sweden)
Huanqing Cui
2017-03-01
Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.
Application of a particle swarm optimization for shape optimization in hydraulic machinery
Moravec, Prokop; Rudolf, Pavel
A study of shape optimization has become increasingly popular in academia and industry. A typical problem is to find an optimal shape, which minimizes (or maximizes) a certain cost function and satisfies given constraints. Particle Swarm Optimization (PSO) has received a lot of attention in past years and is inspired by social behaviour of some animals such as flocking behaviour of birds. This paper focuses on a possibility of a diffuser shape optimization using particle swarm optimization (PSO), which is coupled with CFD simulation. Influence of main parameters of PSO-algorithm and later diffuser shapes obtained with this method are discussed and advantages/disadvantages summarized.
Application of a particle swarm optimization for shape optimization in hydraulic machinery
Directory of Open Access Journals (Sweden)
Moravec Prokop
2017-01-01
Full Text Available A study of shape optimization has become increasingly popular in academia and industry. A typical problem is to find an optimal shape, which minimizes (or maximizes a certain cost function and satisfies given constraints. Particle Swarm Optimization (PSO has received a lot of attention in past years and is inspired by social behaviour of some animals such as flocking behaviour of birds. This paper focuses on a possibility of a diffuser shape optimization using particle swarm optimization (PSO, which is coupled with CFD simulation. Influence of main parameters of PSO-algorithm and later diffuser shapes obtained with this method are discussed and advantages/disadvantages summarized.
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization
Abdulameer, Mohammed Hasan; 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
Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem
Directory of Open Access Journals (Sweden)
Ibidun Christiana Obagbuwa
2016-09-01
Full Text Available The Cockroach Swarm Optimization (CSO algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP, which is considered to be an NP-hard Combinatorial Optimization Problem (COP. A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO algorithm on TSP were compared to other meta-heuristic algorithms.
Particle swarm optimization based optimal bidding strategy in an ...
African Journals Online (AJOL)
user
Test results indicate that the proposed algorithm outperforms the Genetic. Algorithm approach with respect to total profit and convergence time. Keywords: Electricity Market, Market Clearing Price (MCP), Optimal bidding strategy, Particle Swarm Optimization (PSO). DOI: http://dx.doi.org/10.4314/ijest.v3i6.23. 1. Introduction.
A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.
Sun, Tao; Xu, Ming-Hai
2017-01-01
Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.
Particle swarm as optimization tool in complex nuclear engineering problems
International Nuclear Information System (INIS)
Medeiros, Jose Antonio Carlos Canedo
2005-06-01
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)
Cosmological parameter estimation using particle swarm optimization
Prasad, Jayanti; Souradeep, Tarun
2012-06-01
Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.
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.
A new hybrid teaching–learning particle swarm optimization ...
Indian Academy of Sciences (India)
This paper proposes a novel hybrid teaching–learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teaching–learning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after ...
Improved cuckoo search with particle swarm optimization for ...
Indian Academy of Sciences (India)
Content based image retrieval (CBIR); image compression; partial recurrent neural network (PRNN); particle swarm optimization (PSO); HAARwavelet; Cuckoo Search ... are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.
Improved cuckoo search with particle swarm optimization for ...
Indian Academy of Sciences (India)
work are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network. Keywords. Content based image retrieval (CBIR); image compression; partial recurrent neural network (PRNN); particle swarm optimization (PSO); HAAR wavelet;.
Chaotic particle swarm optimization for economic dispatch considering the generator constraints
International Nuclear Information System (INIS)
Cai, Jiejin; Ma, Xiaoqian; Li, Lixiang; Haipeng, Peng
2007-01-01
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
Anesthesiology Nurse Scheduling using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Leopoldo Altamirano
2012-02-01
Full Text Available In this article we present an approach designed to solve a real world problem: the Anesthesiology Nurse Scheduling Problem (ANSP at a public French hospital. The anesthesiology nurses are one of the most shared resources in the hospital and we attempt to find a fair/balanced schedule for them, taking into account a set of constraints and the nursesarsquo; stated preferences, concerning the different shifts. We propose a particle swarm optimization algorithm to solve the ANSP. Finally, we compare our technique with previous results obtained using integer programming.
Optimal power flow by particle swarm optimization with an aging ...
African Journals Online (AJOL)
In this paper, a particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) is applied for the solution of OPF problem of power system. This study is implemented on modified IEEE 30-bus test power system with different objectives that reflect minimization of either fuel cost or active power loss or sum ...
Celestial Navigation Fix Based on Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Tsou Ming-Cheng
2015-09-01
Full Text Available A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.
A quantum particle swarm optimizer with chaotic mutation operator
International Nuclear Information System (INIS)
Coelho, Leandro dos Santos
2008-01-01
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
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
Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms
Directory of Open Access Journals (Sweden)
Haifa Mehdi
2011-11-01
Full Text Available This paper presents an efficient and fast method for fine tuning the controller parameters of robot manipulators in constrained motion. The stability of the robotic system is proved using a Lyapunov-based impedance approach whereas the optimal design of the controller parameters are tuned, in offline, by a Particle Swarm Optimization (PSO algorithm. For designing the PSO method, different index performances are 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.
Electronic enclosure design using distributed particle swarm optimization
Scriven, Ian; Lu, Junwei; Lewis, Andrew
2013-02-01
This article proposes a method for designing electromagnetic compatibility shielding enclosures using a peer-to-peer based distributed optimization system based on a modified particle swarm optimization algorithm. This optimization system is used to obtain optimal solutions to a shielding enclosure design problem efficiently with respect to both electromagnetic shielding efficiency and thermal performance. During the optimization procedure it becomes evident that optimization algorithms and computational models must be properly matched in order to achieve efficient operation. The proposed system is designed to be tolerant of faults and resource heterogeneity, and as such would find use in environments where large-scale computing resources are not available, such as smaller engineering companies, where it would allow computer-aided design by optimization using existing resources with little to no financial outlay.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms
Directory of Open Access Journals (Sweden)
Beatriz A. Garro
2015-01-01
Full Text Available Artificial Neural Network (ANN design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO, Second Generation of Particle Swarm Optimization (SGPSO, and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE and the classification error (CER and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
Available transfer capability enhancement with FACTS using Cat Swarm Optimization
Directory of Open Access Journals (Sweden)
T. Nireekshana
2016-03-01
Full Text Available Determination and enhancement of Available Transfer Capability (ATC are important issues in deregulated operation of power systems. This paper investigates the use of FACTS devices, such as SVC and TCSC, to maximize power transfer transactions during normal and contingency situations. ATC is computed using Continuation Power Flow (CPF method considering both the thermal limits and voltage profile. Cat Swarm Optimization (CSO is used as an optimization tool to determine the location and controlling parameters of SVC and TCSC. The suggested methodology is tested on IEEE 14-bus system and also on IEEE 24-bus reliability test system for normal and different contingency cases.
Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms
Directory of Open Access Journals (Sweden)
Haifa Mehdi
2011-11-01
Full Text Available This paper presents an efficient and fast method for fine tuning the controller parameters of robot manipulators in constrained motion. The stability of the robotic system is proved using a Lyapunov‐based impedance approach whereas the optimal design of the controller parameters are tuned, in offline, by a Particle Swarm Optimization (PSO algorithm. For designing the PSOmethod,differentindexperformancesare considered in both joint and Cartesian spaces. A 3DOF manipulator constrained to a circular trajectory is finally used to validate the performances of the proposed approach. The simulation results show the stability and the performances of the proposed approach.
A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Tao Sun
2017-01-01
Full Text Available Quantum-behaved particle swarm optimization (QPSO algorithm is a variant of the traditional particle swarm optimization (PSO. The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA, because it is more like genetic algorithm (GA than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.
Kusumaningtyas, A. B.; Hidayat, M. N.; Ronilaya, F.
2018-04-01
Based on the data from State Electric Company on 15 January 2013, the undistributed power in the 150 kV sub system Grati-Paiton Region IV, that consist of 26 bus 150 kV and 2 bus generation 500 kV system, was recorded 3.286,00 MW. At the same time, the frequency of the system was down to 49 Hz. This lead to a deficit generation and unstable voltage condition in the system. Fast Voltage Stability Index (FVSI) method is used in this research to analyze the voltage stability of the buses. For buses with unstable voltage condition, reactive power will be injected through capacitor installation. The site where the capacitor will be installed is determined using the Fast Voltage Stability Index (FVSI) method while the size of the capacitor is determined using the Particle Swarm Optimization (PSO) method. The PSO method has been applied in some researches, such as to determine optimal placement and sizing in radial distribution network as well as in transmission network.. In this research, the PSO method is used to find the Qloss of an interconnection transmission system, which in turn, the value of the Qloss is used to determine the capacitance of the capacitor needed by the system.
Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches
Directory of Open Access Journals (Sweden)
Jui-Yu Wu
2013-01-01
Full Text Available Stochastic global optimization (SGO algorithms such as the particle swarm optimization (PSO approach have become popular for solving unconstrained global optimization (UGO problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO method and an artificial immune algorithm-based PSO (AIA-PSO method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
DEFF Research Database (Denmark)
Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte
2014-01-01
power loss minimization in distribution systems. In this paper, a new method to achieve power loss minimization in distribution systems by using a price signal to guide the demand side management is proposed. A fuzzy adaptive particle swarm optimization (FAPSO) is used as a tool for the power loss...
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 the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937
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 the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.
Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.
Phoa, Frederick Kin Hing; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee
Supersaturated designs (SSDs) are often used to reduce the number of experimental runs in screening experiments with a large number of factors. As more factors are used in the study, the search for an optimal SSD becomes increasingly challenging because of the large number of feasible selection of factor level settings. This paper tackles this discrete optimization problem via an algorithm based on swarm intelligence. Using the commonly used E ( s 2 ) criterion as an illustrative example, we propose an algorithm to find E ( s 2 )-optimal SSDs by showing that they attain the theoretical lower bounds in Bulutoglu and Cheng (2004) and Bulutoglu (2007). We show that our algorithm consistently produces SSDs that are at least as efficient as those from the traditional CP exchange method in terms of computational effort, frequency of finding the E ( s 2 )-optimal SSD and also has good potential for finding D 3 -, D 4 - and D 5 -optimal SSDs.
Roundness error assessment based on particle swarm optimization
International Nuclear Information System (INIS)
Zhao, J W; Chen, G Q
2005-01-01
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
Mekhmoukh, Abdenour; Mokrani, Karim
2015-11-01
In this paper, a new image segmentation method based on Particle Swarm Optimization (PSO) and outlier rejection combined with level set is proposed. A traditional approach to the segmentation of Magnetic Resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image. The algorithm is very sensitive to noise and in-homogeneities in the image, moreover, it depends on cluster centers initialization. To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm, a novel extended FCM algorithm for image segmentation is presented. In general, in the FCM algorithm the initial cluster centers are chosen randomly, with the help of PSO algorithm the clusters centers are chosen optimally. Our algorithm takes also into consideration the spatial neighborhood information. These a priori are used in the cost function to be optimized. For MR images, the resulting fuzzy clustering is used to set the initial level set contour. The results confirm the effectiveness of the proposed algorithm. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Combined Data with Particle Swarm Optimization for Structural Damage Detection
Directory of Open Access Journals (Sweden)
Fei Kang
2013-01-01
Full Text Available This paper proposes a damage detection method based on combined data of static and modal tests using particle swarm optimization (PSO. To improve the performance of PSO, some immune properties such as selection, receptor editing, and vaccination are introduced into the basic PSO and an improved PSO algorithm is formed. Simulations on three benchmark functions show that the new algorithm performs better than PSO. The efficiency of the proposed damage detection method is tested on a clamped beam, and the results demonstrate that it is more efficient than PSO, differential evolution, and an adaptive real-parameter simulated annealing genetic algorithm.
Human behavior-based particle swarm optimization.
Liu, Hao; Xu, Gang; Ding, Gui-Yan; Sun, Yu-Bo
2014-01-01
Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c 1 and c 2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost.
Application of particle swarm optimization in path planning of mobile robot
Wang, Yong; Cai, Feng; Wang, Ying
2017-08-01
In order to realize the optimal path planning of mobile robot in unknown environment, a particle swarm optimization algorithm based on path length as fitness function is proposed. The location of the global optimal particle is determined by the minimum fitness value, and the robot moves along the points of the optimal particles to the target position. The process of moving to the target point is done with MATLAB R2014a. Compared with the standard particle swarm optimization algorithm, the simulation results show that this method can effectively avoid all obstacles and get the optimal path.
Single image defogging based on particle swarm optimization
Guo, Fan; Zhou, Cong; Liu, Li-jue; Tang, Jin
2017-11-01
Due to the lack of enough information to solve the equation of image degradation model, existing defogging methods generally introduce some parameters and set these values fixed. Inappropriate parameter setting leads to difficulty in obtaining the best defogging results for different input foggy images. Therefore, a single image defogging algorithm based on particle swarm optimization (PSO) is proposed in this letter to adaptively and automatically select optimal parameter values for image defogging algorithms. The proposed method is applied to two representative defogging algorithms by selecting the two main parameters and optimizing them using the PSO algorithm. Comparative study and qualitative evaluation demonstrate that the better quality results are obtained by using the proposed parameter selection method.
Optimization of Transformation Coefficients Using Direct Search and Swarm Intelligence
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Manusov V.Z.
2017-04-01
Full Text Available This research considers optimization of tap position of transformers in power systems to reduce power losses. Now, methods based on heuristic rules and fuzzy logic, or methods that optimize parts of the whole system separately, are applied to this problem. The first approach requires expert knowledge about processes in the network. The second methods are not able to consider all the interrelations of system’s parts, while changes in segment affect the entire system. Both approaches are tough to implement and require adjustment to the tasks solved. It needs to implement algorithms that can take into account complex interrelations of optimized variables and self-adapt to optimization task. It is advisable to use algorithms given complex interrelations of optimized variables and independently adapting from optimization tasks. Such algorithms include Swarm Intelligence algorithms. Their main features are self-organization, which allows them to automatically adapt to conditions of tasks, and the ability to efficiently exit from local extremes. Thus, they do not require specialized knowledge of the system, in contrast to fuzzy logic. In addition, they can efficiently find quasi-optimal solutions converging to the global optimum. This research applies Particle Swarm Optimization algorithm (PSO. The model of Tajik power system used in experiments. It was found out that PSO is much more efficient than greedy heuristics and more flexible and easier to use than fuzzy logic. PSO allows reducing active power losses from 48.01 to 45.83 MW (4.5%. With al, the effect of using greedy heuristics or fuzzy logic is two times smaller (2.3%.
Analog Circuit Fault Diagnosis Approach Based on Improved Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Ming-Fang WANG
2014-07-01
Full Text Available The basic thought of particle swarm optimization is introduced firstly, then particle swarm optimization algorithm model is established. The application of the improved particle swarm optimization algorithm to power supply system fault diagnosis is analyzed in accordance with problem of the algorithm, and migration strategy is added to particle swarm optimization algorithm. Finally the parameters of the wide area damping controller are adjusted by the improved particle swarm optimization algorithm.
Particle swarm optimization applied to automatic lens design
Qin, Hua
2011-06-01
This paper describes a novel application of Particle Swarm Optimization (PSO) technique to lens design. A mathematical model is constructed, and merit functions in an optical system are employed as fitness functions, which combined radiuses of curvature, thicknesses among lens surfaces and refractive indices regarding an optical system. By using this function, the aberration correction is carried out. A design example using PSO is given. Results show that PSO as optical design tools is practical and powerful, and this method is no longer dependent on the lens initial structure and can arbitrarily create search ranges of structural parameters of a lens system, which is an important step towards automatic design with artificial intelligence.
Particle Swarm Optimization approach to defect detection in armour ceramics.
Kesharaju, Manasa; Nagarajah, Romesh
2017-03-01
In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.
Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza
2017-06-01
Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.
DEFF Research Database (Denmark)
Chen, Shuheng; Hu, Weihao; Su, Chi
2015-01-01
A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid PSO (FAHPSO) is proposed. The objective is to minimize comprehensive cost, consisting of power loss and operation cost of transformers...... algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33-node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove...... that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods....
Directory of Open Access Journals (Sweden)
Li Wang
2017-02-01
Full Text Available The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO algorithm that is based on random perturbation (RP-PSO. The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.
Particle Swarm Optimization for Structural Design Problems
Directory of Open Access Journals (Sweden)
Hamit SARUHAN
2010-02-01
Full Text Available The aim of this paper is to employ the Particle Swarm Optimization (PSO technique to a mechanical engineering design problem which is minimizing the volume of a cantilevered beam subject to bending strength constraints. Mechanical engineering design problems are complex activities which are computing capability are more and more required. The most of these problems are solved by conventional mathematical programming techniques that require gradient information. These techniques have several drawbacks from which the main one is becoming trapped in local optima. As an alternative to gradient-based techniques, the PSO does not require the evaluation of gradients of the objective function. The PSO algorithm employs the generation of guided random positions when they search for the global optimum point. The PSO which is a nature inspired heuristics search technique imitates the social behavior of bird flocking. The results obtained by the PSO are compared with Mathematical Programming (MP. It is demonstrated that the PSO performed and obtained better convergence reliability on the global optimum point than the MP. Using the MP, the volume of 2961000 mm3 was obtained while the beam volume of 2945345 mm3 was obtained by the PSO.
Swarm algorithms with chaotic jumps for optimization of multimodal functions
Krohling, Renato A.; Mendel, Eduardo; Campos, Mauro
2011-11-01
In this article, the use of some well-known versions of particle swarm optimization (PSO) namely the canonical PSO, the bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) is investigated on multimodal optimization problems. A hybrid approach which consists of swarm algorithms combined with a jump strategy in order to escape from local optima is developed and tested. The jump strategy is based on the chaotic logistic map. The hybrid algorithm was tested for all three versions of PSO and simulation results show that the addition of the jump strategy improves the performance of swarm algorithms for most of the investigated optimization problems. Comparison with the off-the-shelf PSO with local topology (l best model) has also been performed and indicates the superior performance of the standard PSO with chaotic jump over the standard both using local topology (l best model).
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
Design of Wire Antennas by Using an Evolved Particle Swarm Optimization Algorithm
Lepelaars, E.S.A.M.; Zwamborn, A.P.M.; Rogovic, A.; Marasini, C.; Monorchio, A.
2007-01-01
A Particle Swarm Optimization (PSO) algorithm has been used in conjunction with a full-wave numerical code based on the Method of Moments (MoM) to design and optimize wire antennas. The PSO is a robust stochastic evolutionary numerical technique that is very effective in optimizing multidimensional
International Nuclear Information System (INIS)
Hu, Chao; Jain, Gaurav; Zhang, Puqiang; Schmidt, Craig; Gomadam, Parthasarathy; Gorka, Tom
2014-01-01
Highlights: • We develop a data-driven method for the battery capacity estimation. • Five charge-related features that are indicative of the capacity are defined. • The kNN regression model captures the dependency of the capacity on the features. • Results with 10 years’ continuous cycling data verify the effectiveness of the method. - Abstract: Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the battery health condition by estimating the battery capacity over the life-time. This paper presents a data-driven method for estimating the capacity of Li-ion battery based on the charge voltage and current curves. The contributions of this paper are three-fold: (i) the definition of five characteristic features of the charge curves that are indicative of the capacity, (ii) the development of a non-linear kernel regression model, based on the k-nearest neighbor (kNN) regression, that captures the complex dependency of the capacity on the five features, and (iii) the adaptation of particle swarm optimization (PSO) to finding the optimal combination of feature weights for creating a kNN regression model that minimizes the cross validation (CV) error in the capacity estimation. Verification with 10 years’ continuous cycling data suggests that the proposed method is able to accurately estimate the capacity of Li-ion battery throughout the whole life-time
A Novel Distributed Quantum-Behaved Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Yangyang Li
2017-01-01
Full Text Available Quantum-behaved particle swarm optimization (QPSO is an improved version of particle swarm optimization (PSO and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases.
He, Runnan; Wang, Kuanquan; Li, Qince; Yuan, Yongfeng; Zhao, Na; Liu, Yang; Zhang, Henggui
2017-12-01
Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.
A new inertia weight control strategy for particle swarm optimization
Zhu, Xianming; Wang, Hongbo
2018-04-01
Particle Swarm Optimization is a member of swarm intelligence algorithms, which is inspired by the behavior of bird flocks. The inertia weight, one of the most important parameters of PSO, is crucial for PSO, for it balances the performance of exploration and exploitation of the algorithm. This paper proposes a new inertia weight control strategy and PSO with this new strategy is tested by four benchmark functions. The results shows that the new strategy provides the PSO with better performance.
Optimization of Gain, Impedance, and Bandwidth of Yagi-Uda Array Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Munish Rattan
2008-01-01
Full Text Available Particle swarm optimization (PSO is a new, high-performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It is a global optimization technique-like genetic algorithm (GA but has less computational cost compared to GA. In this paper, PSO has been used to optimize the gain, impedance, and bandwidth of Yagi-Uda array. To evaluate the performance of designs, a method of moments code NEC2 has been used. The results are comparable to those obtained using GA.
Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation
Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah
2018-04-01
The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.
Optimal configuration of power grid sources based on optimal particle swarm algorithm
Wen, Yuanhua
2018-04-01
In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.
Applying Sequential Particle Swarm Optimization Algorithm to Improve Power Generation Quality
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Abdulhafid Sallama
2014-10-01
Full Text Available Swarm Optimization approach is a heuristic search method whose mechanics are inspired by the swarming or collaborative behaviour of biological populations. It is used to solve constrained, unconstrained, continuous and discrete problems. Swarm intelligence systems are widely used and very effective in solving standard and large-scale optimization, provided that the problem does not require multi solutions. In this paper, particle swarm optimisation technique is used to optimise fuzzy logic controller (FLC for stabilising a power generation and distribution network that consists of four generators. The system is subject to different types of faults (single and multi-phase. Simulation studies show that the optimised FLC performs well in stabilising the network after it recovers from a fault. The controller is compared to multi-band and standard controllers.
Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization
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Ya-zhong Luo
2014-01-01
Full Text Available A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.
Improved multi-objective clustering algorithm using particle swarm optimization.
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Congcong Gong
Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.
Strategic bidding in electricity markets using particle swarm optimization
International Nuclear Information System (INIS)
Yucekaya, Ahmet D.; Valenzuela, Jorge; Dozier, Gerry
2009-01-01
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)
Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History
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Danping Wang
2017-01-01
Full Text Available A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional, 10 CEC2005 benchmark functions (30-dimensional, and a real-world problem (multilevel image segmentation problems. Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.
International Nuclear Information System (INIS)
Dutta, Rajdeep; Ganguli, Ranjan; Mani, V
2011-01-01
Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures
Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm
Directory of Open Access Journals (Sweden)
V.K. Chawla
2018-01-01
Full Text Available Use of Automated guided vehicles (AGVs is highly significant in Flexible Manufacturing Sys-tem (FMS in which material handling in form of jobs is performed from one work center to an-other work center. A multifold increase in through put of FMS can be observed by application of multi load AGVs. In this paper, Particle Swarm Optimization (PSO integrated with Memetic Algorithm (MA named as Modified Memetic Particle Swarm Optimization Algorithm (MMP-SO is applied to yield initial feasible solutions for scheduling of multi load AGVs for minimum travel and waiting time in the FMS. The proposed MMPSO algorithm exhibits balanced explora-tion and exploitation for global search method of standard Particle Swarm Optimization (PSO algorithm and local search method of Memetic Algorithm (MA which further results into yield of efficient and effective initial feasible solutions for the multi load AGVs scheduling problem.
DEFF Research Database (Denmark)
Hou, Peng; Hu, Weihao; Soltani, Mohsen
2015-01-01
Levelized Production Cost (LPC) as the objective function. The optimization procedure is performed by Particle Swarm Optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective...
DEFF Research Database (Denmark)
Vlachogiannis, Ioannis (John); Lee, K Y
2009-01-01
In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem...
A Diversity-Guided Particle Swarm Optimizer - the ARPSO
DEFF Research Database (Denmark)
Vesterstrøm, Jacob Svaneborg; Riget, Jacques
2002-01-01
The particle swarm optimization (PSO) algorithm is a new population based search strat- egy, which has exhibited good performance on well-known numerical test problems. How- ever, on strongly multi-modal test problems the PSO tends to suffer from premature convergence. This is due to a decrease...... of diversity in search space that leads to a to- tal implosion and ultimately fitness stagnation of the swarm. An accepted hypothesis is that maintenance of high diversity is crucial for preventing premature convergence in multi-modal optimization. We introduce the attractive and repulsive PSO (ARPSO......) in trying to overcome the problem of premature convergence. It uses a diversity measure to control the swarm. The result is an algorithm that alternates between phases of attraction and repulsion. The performance of the ARPSO is compared to a basic PSO (bPSO) and a genetic algorithm (GA). The results show...
Compressive Sensing Image Fusion Based on Particle Swarm Optimization Algorithm
Li, X.; Lv, J.; Jiang, S.; Zhou, H.
2017-09-01
In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. We propose a new method of image fusion that utilizes HIS transformation and the recently developed theory of compressive sensing that is called HIS-CS image fusion. In this algorithm, the particle swarm optimization algorithm is used to select the fusion coefficient ω. In the iterative process, the image fusion coefficient ω is taken as particle, and the optimal value is obtained by combining the optimal objective function. Then we use the compression-aware weighted fusion algorithm for remote sensing image fusion, taking the coefficient ω as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.
Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm
Zhang, Jian; Gan, Yang
2018-04-01
The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.
L. Jubair Ahmed; A. Ebenezer Jeyakumar
2013-01-01
Thresholding is one of the most important techniques for performing image segmentation. In this paper to compute optimum thresholds for Maximum Tsallis entropy thresholding (MTET) model, a new hybrid algorithm is proposed by integrating the Comprehensive Learning Particle Swarm Optimizer (CPSO) with the Powell’s Conjugate Gradient (PCG) method. Here the CPSO will act as the main optimizer for searching the near-optimal thresholds while the PCG method will be used to fine tune the best solutio...
Improved particle swarm optimization approach for nonconvex static ...
African Journals Online (AJOL)
increases due to the multiple constraints that need to be satisfied. This paper proposes an improved particle swarm optimization approach (IPSO) for solving nonconvex static and dynamic economic dispatch. The classical PSO (CPSO) approach suffers from the problem of premature convergence, particularly for complex ...
A new hybrid teaching–learning particle swarm optimization ...
Indian Academy of Sciences (India)
Ramanpreet Singh
2017-11-07
Nov 7, 2017 ... A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path. RAMANPREET SINGH*, HIMANSHU CHAUDHARY and AMIT K SINGH. Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur 302017, India e-mail: ...
On the premature convergence of particle swarm optimization
DEFF Research Database (Denmark)
Larsen, Rie B.; Jouffroy, Jerome; Lassen, Benny
2016-01-01
This paper discusses convergence issues of the basic particle swarm optimization algorithm for different pa- rameters. For the one-dimensional case, it is shown that, for a specific range of parameters, the particles will converge prematurely, i.e. away from the actual minimum of the objective...
Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio
DEFF Research Database (Denmark)
Pinto, Tiago; Vale, Zita; Sousa, Tiago
2014-01-01
, 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...
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
Abstract. This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network. (BPNN). This analysis is carried out following a series of experiments ...
Directory of Open Access Journals (Sweden)
Patel G.C.M.
2016-09-01
Full Text Available The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.. It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA, particle swarm optimization (PSO and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.
Exergoeconomic optimization of a thermal power plant using particle swarm optimization
Directory of Open Access Journals (Sweden)
Groniewsky Axel
2013-01-01
Full Text Available The basic concept in applying numerical optimization methods for power plants optimization problems is to combine a State of the art search algorithm with a powerful, power plant simulation program to optimize the energy conversion system from both economic and thermodynamic viewpoints. Improving the energy conversion system by optimizing the design and operation and studying interactions among plant components requires the investigation of a large number of possible design and operational alternatives. State of the art search algorithms can assist in the development of cost-effective power plant concepts. The aim of this paper is to present how nature-inspired swarm intelligence (especially PSO can be applied in the field of power plant optimization and how to find solutions for the problems arising and also to apply exergoeconomic optimization technics for thermal power plants.
Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization
Birge, Brian
2013-01-01
The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.
Reversals and collisions optimize protein exchange in bacterial swarms
Energy Technology Data Exchange (ETDEWEB)
Amiri, Aboutaleb; Harvey, Cameron; Buchmann, Amy; Christley, Scott; Shrout, Joshua D.; Aranson, Igor S.; Alber, Mark
2017-03-01
Swarming groups of bacteria coordinate their behavior by self-organizing as a population to move over surfaces in search of nutrients and optimal niches for colonization. Many open questions remain about the cues used by swarming bacteria to achieve this self-organization. While chemical cue signaling known as quorum sensing is well-described, swarming bacteria often act and coordinate on time scales that could not be achieved via these extracellular quorum sensing cues. Here, cell-cell contact-dependent protein exchange is explored as amechanism of intercellular signaling for the bacterium Myxococcus xanthus. A detailed biologically calibrated computational model is used to study how M. xanthus optimizes the connection rate between cells and maximizes the spread of an extracellular protein within the population. The maximum rate of protein spreading is observed for cells that reverse direction optimally for swarming. Cells that reverse too slowly or too fast fail to spread extracellular protein efficiently. In particular, a specific range of cell reversal frequencies was observed to maximize the cell-cell connection rate and minimize the time of protein spreading. Furthermore, our findings suggest that predesigned motion reversal can be employed to enhance the collective behavior of biological synthetic active systems.
Energy group structure determination using particle swarm optimization
International Nuclear Information System (INIS)
Yi, Ce; Sjoden, Glenn
2013-01-01
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
Pebble bed reactor fuel cycle optimization using particle swarm algorithm
Energy Technology Data Exchange (ETDEWEB)
Tavron, Barak, E-mail: btavron@bgu.ac.il [Planning, Development and Technology Division, Israel Electric Corporation Ltd., P.O. Box 10, Haifa 31000 (Israel); Shwageraus, Eugene, E-mail: es607@cam.ac.uk [Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ (United Kingdom)
2016-10-15
Highlights: • Particle swarm method has been developed for fuel cycle optimization of PBR reactor. • Results show uranium utilization low sensitivity to fuel and core design parameters. • Multi-zone fuel loading pattern leads to a small improvement in uranium utilization. • Thorium mixes with highly enriched uranium yields the best uranium utilization. - Abstract: Pebble bed reactors (PBR) features, such as robust thermo-mechanical fuel design and on-line continuous fueling, facilitate wide range of fuel cycle alternatives. A range off fuel pebble types, containing different amounts of fertile or fissile fuel material, may be loaded into the reactor core. Several fuel loading zones may be used since radial mixing of the pebbles was shown to be limited. This radial separation suggests the possibility to implement the “seed-blanket” concept for the utilization of fertile fuels such as thorium, and for enhancing reactor fuel utilization. In this study, the particle-swarm meta-heuristic evolutionary optimization method (PSO) has been used to find optimal fuel cycle design which yields the highest natural uranium utilization. The PSO method is known for solving efficiently complex problems with non-linear objective function, continuous or discrete parameters and complex constrains. The VSOP system of codes has been used for PBR fuel utilization calculations and MATLAB script has been used to implement the PSO algorithm. Optimization of PBR natural uranium utilization (NUU) has been carried out for 3000 MWth High Temperature Reactor design (HTR) operating on the Once Trough Then Out (OTTO) fuel management scheme, and for 400 MWth Pebble Bed Modular Reactor (PBMR) operating on the multi-pass (MEDUL) fuel management scheme. Results showed only a modest improvement in the NUU (<5%) over reference designs. Investigation of thorium fuel cases showed that the use of HEU in combination with thorium results in the most favorable reactor performance in terms of
GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS
International Nuclear Information System (INIS)
Rogers, Adam; Fiege, Jason D.
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 automatically, which represents the trade-off between the image χ 2 and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest χ 2 is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.
Energy Technology Data Exchange (ETDEWEB)
Sanchez-Huerta, V; Ramirez-Arredondo, Juan M. [Universidad de Quintana Roo, Chetumal, Quintana Roo (Mexico)]. E-mail: vsanchez@gdl.cinvestav.mx; Arriaga-Hurtado, L. G. [CIDETEQ, Queretaro (Mexico)
2009-09-15
Sizing an electric energy system requires an analysis of investment, maintenance and operating costs. In the case of a generation system that uses renewable sources, optimal capacity becomes more complex compared to a conventional system, because of the randomness of renewable resources (wind, solar) and the still high costs of wind and photovoltage generator modules. This work presents the optimal sizing of a wind-solar-fuel cell generation system, minimizing the costs of the system while satisfying the energy demands of an isolated charge. The optimization method used is based on an evolutionary programming technique known as particle swarms (PSO-particle swarm optimization). The generation of energy with a hybrid system is discussed, based on the profile of insolation and wind availability at the site, with the objective of satisfying a specific electric demand. [Spanish] El dimensionamiento de un sistema de generacion de energia electrica requiere un analisis de los costos de inversion, mantenimiento y operacion. En el caso de un sistema de generacion que utiliza fuentes renovables la capacidad optima resulta mas compleja con respecto a un sistema convencional, debido a la aleatoriedad de los recursos renovables (eolico, solar), y a los aun altos costos de generadores eolicos y modulos fotovoltaicos. En este trabajo se presenta el dimensionamiento optimo de un sistema de generacion eolico-solar-celda de combustible minimizando los costos del sistema que satisfaga la energia demandada por una carga aislada. El metodo de optimizacion utilizado esta basado en una tecnica de programacion evolutiva conocida como enjambre de particulas (PSO por sus siglas en ingles: particle swarm optimization). Se plantea la generacion de energia del sistema hibrido con base a la insolacion y el perfil del viento disponible en sitio, con objeto de satisfacer una demanda electrica determinada.
Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution
Directory of Open Access Journals (Sweden)
Prabha Umapathy
2010-01-01
Full Text Available This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization (PSO. The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight (CIW, a time-varying inertia weight (TVIW, and global-local best inertia weight (GLbestIW, are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.
A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Biwei Tang
2016-01-01
Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.
Improved SpikeProp for Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
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.
Ruiz-Cruz, Riemann; Sanchez, Edgar N; Ornelas-Tellez, Fernando; Loukianov, Alexander G; Harley, Ronald G
2013-12-01
In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established that this control law minimizes a meaningful cost function. The CLFs depend on matrix selection in order to achieve the control objectives; this matrix is determined by two mechanisms: initially, fixed parameters are proposed for this matrix by a trial-and-error method and then by using the PSO algorithm. The inverse optimal control scheme is illustrated via simulations for the DFIG, including the comparison between both mechanisms.
Directory of Open Access Journals (Sweden)
Jiaxi Wang
2016-01-01
Full Text Available The shunting schedule of electric multiple units depot (SSED is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.
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
Control parameter optimization for AP1000 reactor using Particle Swarm Optimization
International Nuclear Information System (INIS)
Wang, Pengfei; Wan, Jiashuang; Luo, Run; Zhao, Fuyu; Wei, Xinyu
2016-01-01
Highlights: • The PSO algorithm is applied for control parameter optimization of AP1000 reactor. • Key parameters of the MSHIM control system are optimized. • Optimization results are evaluated though simulations and quantitative analysis. - Abstract: The advanced mechanical shim (MSHIM) core control strategy is implemented in the AP1000 reactor for core reactivity and axial power distribution control simultaneously. The MSHIM core control system can provide superior reactor control capabilities via automatic rod control only. This enables the AP1000 to perform power change operations automatically without the soluble boron concentration adjustments. In this paper, the Particle Swarm Optimization (PSO) algorithm has been applied for the parameter optimization of the MSHIM control system to acquire better reactor control performance for AP1000. System requirements such as power control performance, control bank movement and AO control constraints are reflected in the objective function. Dynamic simulations are performed based on an AP1000 reactor simulation platform in each iteration of the optimization process to calculate the fitness values of particles in the swarm. The simulation platform is developed in Matlab/Simulink environment with implementation of a nodal core model and the MSHIM control strategy. Based on the simulation platform, the typical 10% step load decrease transient from 100% to 90% full power is simulated and the objective function used for control parameter tuning is directly incorporated in the simulation results. With successful implementation of the PSO algorithm in the control parameter optimization of AP1000 reactor, four key parameters of the MSHIM control system are optimized. It has been demonstrated by the calculation results that the optimized MSHIM control system parameters can improve the reactor power control capability and reduce the control rod movement without compromising AO control. Therefore, the PSO based optimization
An Improved Particle Swarm Optimization Algorithm and Its Application in the Community Division
Directory of Open Access Journals (Sweden)
Jiang Hao
2016-01-01
Full Text Available With the deepening of the research on complex networks, the method of detecting and classifying social network is springing up. In this essay, the basic particle swarm algorithm is improved based on the GN algorithm. Modularity is taken as a measure of community division [1]. In view of the dynamic network community division, scrolling calculation method is put forward. Experiments show that using the improved particle swarm optimization algorithm can improve the accuracy of the community division and can also get higher value of the modularity in the dynamic community
Survey of Methods and Algorithms of Robot Swarm Aggregation
E Shlyakhov, N.; Vatamaniuk, I. V.; Ronzhin, A. L.
2017-01-01
The paper considers the problem of swarm aggregation of autonomous robots with the use of three methods based on the analogy of the behavior of biological objects. The algorithms substantiating the requirements for hardware realization of sensor, computer and network resources and propulsion devices are presented. Techniques for efficiency estimation of swarm aggregation via space-time characteristics are described. The developed model of the robot swarm reconfiguration into a predetermined three-dimensional shape is presented.
PID control for chaotic synchronization using particle swarm optimization
Energy Technology Data Exchange (ETDEWEB)
Chang, W.-D. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)], E-mail: wdchang@mail.stu.edu.tw
2009-01-30
In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.
PID control for chaotic synchronization using particle swarm optimization
International Nuclear Information System (INIS)
Chang, W.-D.
2009-01-01
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.
Directory of Open Access Journals (Sweden)
Xun Zhang
2014-01-01
Full Text Available Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.
Pozzobon, Victor; Perre, Patrick
2018-01-21
This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.
Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Xiyang Liu
2016-01-01
Full Text Available Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.
DAILY SCHEDULING OF SMALL HYDRO POWER PLANTS DISPATCH WITH MODIFIED PARTICLES SWARM OPTIMIZATION
Directory of Open Access Journals (Sweden)
Sinvaldo Rodrigues Moreno
2015-04-01
Full Text Available This paper presents a new approach for short-term hydro power scheduling of reservoirs using an algorithm-based Particle Swarm Optimization (PSO. PSO is a population-based algorithm designed to find good solutions to optimization problems, its characteristics have encouraged its adoption to tackle a variety of problems in different fields. In this paper the authors consider an optimization problem related to a daily scheduling of small hydro power dispatch. The goal is construct a feasible solution that maximize the cascade electricity production, following the environmental constraints and water balance. The paper proposes an improved Particle Swarm Optimization (PSO algorithm, which takes advantage of simplicity and facility of implementation. The algorithm was successfully applied to the optimization of the daily schedule strategies of small hydro power plants, considering maximum water utilization and all constraints related to simultaneous water uses. Extensive computational tests and comparisons with other heuristics methods showed the effectiveness of the proposed approach.
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
Particle swarm optimization for programming deep brain stimulation arrays
Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.
2017-02-01
Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon
Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis
Muslim, M. A.; Rukmana, S. H.; Sugiharti, E.; Prasetiyo, B.; Alimah, S.
2018-03-01
Data mining has become a basic methodology for computational applications in the field of medical domains. Data mining can be applied in the health field such as for diagnosis of breast cancer, heart disease, diabetes and others. Breast cancer is most common in women, with more than one million cases and nearly 600,000 deaths occurring worldwide each year. The most effective way to reduce breast cancer deaths was by early diagnosis. This study aims to determine the level of breast cancer diagnosis. This research data uses Wisconsin Breast Cancer dataset (WBC) from UCI machine learning. The method used in this research is the algorithm C4.5 and Particle Swarm Optimization (PSO) as a feature option and to optimize the algorithm. C4.5. Ten-fold cross-validation is used as a validation method and a confusion matrix. The result of this research is C4.5 algorithm. The particle swarm optimization C4.5 algorithm has increased by 0.88%.
Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm
International Nuclear Information System (INIS)
Canedo Medeiros, Jose Antonio Carlos; Schirru, Roberto
2008-01-01
In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results
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.
Directory of Open Access Journals (Sweden)
Kazem Mohammadi- Aghdam
2015-10-01
Full Text Available This paper proposes the application of a new version of the heuristic particle swarm optimization (PSO method for designing water distribution networks (WDNs. The optimization problem of looped water distribution networks is recognized as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimization techniques. In this paper, the concept of dynamic swarm size is considered in an attempt to increase the convergence speed of the original PSO algorithm. In this strategy, the size of the swarm is dynamically changed according to the iteration number of the algorithm. Furthermore, a novel mutation approach is introduced to increase the diversification property of the PSO and to help the algorithm to avoid trapping in local optima. The new version of the PSO algorithm is called dynamic mutated particle swarm optimization (DMPSO. The proposed DMPSO is then applied to solve WDN design problems. Finally, two illustrative examples are used for comparison to verify the efficiency of the proposed DMPSO as compared to other intelligent algorithms.
Petersen, Hugh
2002-01-01
Describes an eighth grade art project for which students created bug swarms on scratchboard. Explains that the project also teaches students about design principles, such as balance. Discusses how the students created their drawings. (CMK)
International Nuclear Information System (INIS)
Parvin, Dan; Clarke, Sean
2015-01-01
Particle Swarm Imaging (PSIM) overcomes some of the challenges associated with the accurate declaration of measurement uncertainties of radionuclide inventories within waste items when the distribution of activity is unknown. Implementation requires minimal equipment, making use of gamma‑ray measurements taken from different locations around the waste item, using only a single electrically cooled HRGS gamma‑ray detector for objects up to a UK ISO freight container in size. The PSIM technique is a computational method that iteratively ‘homes‑in’ on the true location of activity concentrations in waste items. PSIM differs from conventional assay techniques by allowing only viable solutions - that is those that could actually give rise to the measured data - to be considered. Thus PSIM avoids the drawback of conventional analyses, namely, the adoption of unrealistic assumptions about the activity distribution that inevitably leads to the declaration of pessimistic (and in some cases optimistic) activity estimates and uncertainties. PSIM applies an optimisation technique based upon ‘particle swarming’ methods to determine a set of candidate solutions within a ‘search space’ defined by the interior volume of a waste item. The positions and activities of the swarm are used in conjunction with a mathematical model to simulate the measurement response for the current swarm location. The swarm is iteratively updated (with modified positions and activities) until a match with sufficient quality is obtained between the simulated and actual measurement data. This process is repeated to build up a distribution of candidate solutions, which is subsequently analysed to calculate a measurement result and uncertainty along with a visual image of the activity distribution. The application of ‘swarming’ computational methods to non‑destructive assay (NDA) measurements is considered novel and this paper is intended to introduce the PSIM concept and provide
Optimal power flow by particle swarm optimization with an aging ...
African Journals Online (AJOL)
DR OKE
The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for ... PSO algorithm is one of the swarm intelligence techniques based on simulating the food-searching behaviour of birds (Kennedy & Eberhart ...... Applied Artificial Intelligence, vol. 24, pp.
Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics
Rosalie, Martin; Brust, Matthias,; Danoy, Grégoire; Chaumette, Serge; Bouvry, Pascal
2017-01-01
International audience; Cooperative usage of multiple UAVs as a swarm can deliver high-quality surveillance performance. However, the communication capabilities within the UAV swarm must be maintained for local data propagation to swarm members in favor of achieving an efficient global behavior. In this paper, we address the problem of optimizing two adversary criteria for such a UAV swarm: (a) maximizing the area coverage, while (b) preserving network connectivity. Our approach, called CACOC...
Cat Swarm Optimization algorithm for optimal linear phase FIR filter design.
Saha, Suman Kumar; Ghoshal, Sakti Prasad; Kar, Rajib; Mandal, Durbadal
2013-11-01
In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics. CSO is generated by observing the behaviour of cats and composed of two sub-models. In CSO, one can decide how many cats are used in the iteration. Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode. The final solution would be the best position of one of the cats. CSO keeps the best solution until it reaches the end of the iteration. The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE). The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems. The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE. The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Yu, Xiang; Zhang, Xueqing
2017-01-01
Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.
Directory of Open Access Journals (Sweden)
Zhen-Lun Yang
2015-01-01
Full Text Available An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.
Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing
2015-01-01
An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate. PMID:26064085
Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing
2015-01-01
An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.
Improved Quantum Particle Swarm Optimization for Mangroves Classification
Directory of Open Access Journals (Sweden)
Zhehuang Huang
2016-01-01
Full Text Available Quantum particle swarm optimization (QPSO is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.
Particle Swarm Optimization for HW/SW Partitioning
Abdelhalim, M. B.; Habib, S. E. &#;D.
2009-01-01
In this chapter, the recent introduction of the Particle Swarm Optimization technique to solve the HW/SW partitioning problem is reviewed, along with its â€œre-exited PSOâ€ modification. The re-exited PSO algorithm is a recently-introduced restarting technique for PSO. The Re-exited PSO proved to be highly effective for solving the HW/SW partitioning problem. Efficient cost function formulation is of a paramount importance for an efficient optimization algorithm. Each component in the design...
Cho, Ming-Yuan; Hoang, Thi Thom
2017-01-01
Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary seque...
Performance Evaluation of Dynamic Particle Swarm Optimization
Ms. Hemlata S. Urade; Rahila Patel
2012-01-01
In this paper the concept of dynamic particle swarmoptimization is introduced. The dynamic PSO is different fromthe existing PSO’s and some local version of PSO in terms ofswarm size and topology. Experiment conducted for benchmarkfunctions of single objective optimization problem, which showsthe better performance rather the basic PSO. The paper alsocontains the comparative analysis for Simple PSO and DynamicPSO which shows the better result for dynamic PSO rather thansimple PSO.
Particle swarm optimization with random keys applied to the nuclear reactor reload problem
International Nuclear Information System (INIS)
Meneses, Anderson Alvarenga de Moura; Fundacao Educacional de Macae; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto
2007-01-01
In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)
Directory of Open Access Journals (Sweden)
Zhengwu Fan
2017-01-01
Full Text Available In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO. Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature.
Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller
Energy Technology Data Exchange (ETDEWEB)
Miao, Fuqing; Kim, Chol Min; Ahn, Seok Young [Pusan National University, Busan (Korea, Republic of); Park, Hong Seok [Ulsan University, Ulsan (Korea, Republic of)
2015-11-15
This paper presents a multi-objective optimization of the impeller shape of an axial-flow pump based on the Modified particle swarm optimization (MPSO) algorithm. At first, an impeller shape was designed and used as a reference in the optimization process then NPSHr and η of the axial flow pump were numerically investigated by using the commercial software ANSYS with the design variables concerning hub angle β{sub h}, chord angle β{sub c}, cascade solidity of chord σ{sub c} and maximum thickness of blade H. By using the Group method of data handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to the design variables were obtained. A benchmark test was employed to evaluate the performance of the MPSO algorithm in comparison with other particle swarm algorithms. Later the MPSO approach was used for Pareto based optimization. Finally, the MPSO optimization result and CFD simulation result were compared in a re-evaluation process. By using swarm intelligence based on the modified PSO algorithm, better performance pump with higher efficiency and lower NPSHr could be obtained. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape design.
Particle swarm optimization with random keys applied to the nuclear reactor reload problem
Energy Technology Data Exchange (ETDEWEB)
Meneses, Anderson Alvarenga de Moura [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear; Fundacao Educacional de Macae (FUNEMAC), RJ (Brazil). Faculdade Professor Miguel Angelo da Silva Santos; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mails: ameneses@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; schirru@lmp.ufrj.br
2007-07-01
In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)
Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller
International Nuclear Information System (INIS)
Miao, Fuqing; Kim, Chol Min; Ahn, Seok Young; Park, Hong Seok
2015-01-01
This paper presents a multi-objective optimization of the impeller shape of an axial-flow pump based on the Modified particle swarm optimization (MPSO) algorithm. At first, an impeller shape was designed and used as a reference in the optimization process then NPSHr and η of the axial flow pump were numerically investigated by using the commercial software ANSYS with the design variables concerning hub angle β h , chord angle β c , cascade solidity of chord σ c and maximum thickness of blade H. By using the Group method of data handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to the design variables were obtained. A benchmark test was employed to evaluate the performance of the MPSO algorithm in comparison with other particle swarm algorithms. Later the MPSO approach was used for Pareto based optimization. Finally, the MPSO optimization result and CFD simulation result were compared in a re-evaluation process. By using swarm intelligence based on the modified PSO algorithm, better performance pump with higher efficiency and lower NPSHr could be obtained. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape design
Directory of Open Access Journals (Sweden)
Imran Rahman
2016-03-01
Full Text Available Transportation electrification has undergone major changes since the last decade. Success of smart grid with renewable energy integration solely depends upon the large-scale penetration of plug-in hybrid electric vehicles (PHEVs for a sustainable and carbon-free transportation sector. One of the key performance indicators in hybrid electric vehicle is the State-of-Charge (SoC which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged Accelerated particle swarm optimization (APSO technique was applied and compared with standard particle swarm optimization (PSO considering charging time and battery capacity. Simulation results obtained for maximizing the highly nonlinear objective function indicate that APSO achieves some improvements in terms of best fitness and computation time.
OPTIMIZATION OF PLY STACKING SEQUENCE OF COMPOSITE DRIVE SHAFT USING PARTICLE SWARM ALGORITHM
Directory of Open Access Journals (Sweden)
CHANNAKESHAVA K. R.
2011-06-01
Full Text Available In this paper an attempt has been made to optimize ply stacking sequence of single piece E-Glass/Epoxy and Boron /Epoxy composite drive shafts using Particle swarm algorithm (PSA. PSA is a population based evolutionary stochastic optimization technique which is a resent heuristic search method, where mechanics are inspired by swarming or collaborative behavior of biological population. PSA programme is developed to optimize the ply stacking sequence with an objective of weight minimization by considering design constraints as torque transmission capacity, fundamental natural frequency, lateral vibration and torsional buckling strength having number of laminates, ply thickness and stacking sequence as design variables. The weight savings of the E-Glass/epoxy and Boron /Epoxy shaft from PAS were 51% and 85 % of the steel shaft respectively. The optimum results of PSA obtained are compared with results of genetic algorithm (GA results and found that PSA yields better results than GA.
CFSO3: A New Supervised Swarm-Based Optimization Algorithm
Directory of Open Access Journals (Sweden)
Antonino Laudani
2013-01-01
Full Text Available We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3 allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3 the tuning of parameters is a priori designed for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3 together with validations on classical benchmarks are presented.
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Daqing Wu
2012-01-01
Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.
Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization
Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard
2002-01-01
The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.
Optimized Landing of Autonomous Unmanned Aerial Vehicle Swarms
2012-06-01
ISR Intelligence , Surveillance and Reconnaissance LZ Landing Zone LZs Landing Zones NAS National Airspace NPS Naval Postgraduate School UAS Unmanned... intelligent combination of both a centralized control and decentralized control solution that can take advantage of the strengths of both these methods...in a few hours. xvii THIS PAGE INTENTIONALLY LEFT BLANK xviii CHAPTER 1: INTRODUCTION 1.1 Background “The fiercest serpent may be overcome by a swarm
Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo
2017-01-01
In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human
Zhang, Yong-Feng; Chiang, Hsiao-Dong
2017-09-01
A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.
A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers
International Nuclear Information System (INIS)
Mariani, Viviana Cocco; Klassen Duck, Anderson Rodrigo; Guerra, Fabio Alessandro; Santos Coelho, Leandro dos; Rao, Ravipudi Venkata
2012-01-01
Particle swarm optimization (PSO) method is a population-based optimization technique of swarm intelligence field in which each solution called “particle” flies around in a multidimensional problem search space. During the flight, every particle adjusts its position according to its own experience, as well as the experience of neighboring particles, using the best position encountered by itself and its neighbors. In this paper, a new quantum particle swarm optimization (QPSO) approach combined with Zaslavskii chaotic map sequences (QPSOZ) to shell and tube heat exchanger optimization is presented based on the minimization from economic view point. The results obtained in this paper for two case studies using the proposed QPSOZ approach, are compared with those obtained by using genetic algorithm, PSO and classical QPSO showing the best performance of QPSOZ. In order to verify the capability of the proposed method, two case studies are also presented showing that significant cost reductions are feasible with respect to traditionally designed exchangers. Referring to the literature test cases, reduction of capital investment up to 20% and 6% for the first and second cases, respectively, were obtained. Therefore, the annual pumping cost decreased markedly 72% and 75%, with an overall decrease of total cost up to 30% and 27%, respectively, for the cases 1 and 2, respectively, showing the improvement potential of the proposed method, QPSOZ. - Highlights: ► Shell and tube heat exchanger is minimized from economic view point. ► A new quantum particle swarm optimization (QPSO) combined with Zaslavskii chaotic map sequences (QPSOZ) is proposed. ► Reduction of capital investment up to 20% and 6% for the first and second cases was obtained. ► Annual pumping cost decreased 72% and 75%, with an overall decrease of total cost up to 30% and 27% using QPSOZ.
International Nuclear Information System (INIS)
Huang, Chia-Ling
2015-01-01
This paper proposes a new swarm intelligence method known as the Particle-based Simplified Swarm Optimization (PSSO) algorithm while undertaking a modification of the Updating Mechanism (UM), called N-UM and R-UM, and simultaneously applying an Orthogonal Array Test (OA) to solve reliability–redundancy allocation problems (RRAPs) successfully. One difficulty of RRAP is the need to maximize system reliability in cases where the number of redundant components and the reliability of corresponding components in each subsystem are simultaneously decided with nonlinear constraints. In this paper, four RRAP benchmarks are used to display the applicability of the proposed PSSO that advances the strengths of both PSO and SSO to enable optimizing the RRAP that belongs to mixed-integer nonlinear programming. When the computational results are compared with those of previously developed algorithms in existing literature, the findings indicate that the proposed PSSO is highly competitive and performs well. - Highlights: • This paper proposes a particle-based simplified swarm optimization algorithm (PSSO) to optimize RRAP. • Furthermore, the UM and an OA are adapted to advance in optimizing RRAP. • Four systems are introduced and the results demonstrate the PSSO performs particularly well
International Nuclear Information System (INIS)
Boonchuay, Chanwit; Ongsakul, Weerakorn
2011-01-01
In this paper, an optimal risky bidding strategy for a generating company (GenCo) by self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC) is proposed. A significant risk index based on mean-standard deviation ratio (MSR) is maximised to provide the optimal bid prices and quantities. The Monte Carlo (MC) method is employed to simulate rivals' behaviour in competitive environment. Non-convex operating cost functions of thermal generating units and minimum up/down time constraints are taken into account. The proposed bidding strategy is implemented in a multi-hourly trading in a uniform price spot market and compared to other particle swarm optimisation (PSO). Test results indicate that the proposed SPSO-TVAC approach can provide a higher MSR than the other PSO methods. It is potentially applicable to risk management of profit variation of GenCo in spot market.
A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.
Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing
2017-01-01
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.
A novel e-shape communication antenna design using particle swarm optimization (PSO)
Mohanageetha, D.; Pavithra, R.
2013-01-01
An E-shape patch antenna is designed and demonstrated their effectiveness using Particle Swarm Optimization (PSO), which is used for wireless applications. The concept of PSO is briefly introduced in the design procedure and the design parameters are explained. This work focuses on identifying the increasing popularity of swarm intelligence specifically among the electromagnetic community. It is implemented using PSO combined with numerical algorithms for electromagnetic solutions, such as the Finite Element Method (FEM) and the Method of Moments (MOM). In both the realizations, the PSO technique drives the design variables such as antenna dimensions and geometrical features. The fitness function is evaluated for the optimizer. This is achieved by using CAD FEKO 6.1, electromagnetic simulation software. The model is designed with a resonant frequency of 2.65GHz.
Directory of Open Access Journals (Sweden)
Zhou Feng
2013-09-01
Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.
A novel neutron energy spectrum unfolding code using particle swarm optimization
International Nuclear Information System (INIS)
Shahabinejad, H.; Sohrabpour, M.
2017-01-01
A novel neutron Spectrum Deconvolution using Particle Swarm Optimization (SDPSO) code has been developed to unfold the neutron spectrum from a pulse height distribution and a response matrix. The Particle Swarm Optimization (PSO) imitates the bird flocks social behavior to solve complex optimization problems. The results of the SDPSO code have been compared with those of the standard spectra and recently published Two-steps Genetic Algorithm Spectrum Unfolding (TGASU) code. The TGASU code have been previously compared with the other codes such as MAXED, GRAVEL, FERDOR and GAMCD and shown to be more accurate than the previous codes. The results of the SDPSO code have been demonstrated to match well with those of the TGASU code for both under determined and over-determined problems. In addition the SDPSO has been shown to be nearly two times faster than the TGASU code. - Highlights: • Introducing a novel method for neutron spectrum unfolding. • Implementation of a particle swarm optimization code for neutron unfolding. • Comparing results of the PSO code with those of recently published TGASU code. • Match results of the PSO code with those of TGASU code. • Greater convergence rate of implemented PSO code than TGASU code.
Energy Technology Data Exchange (ETDEWEB)
Cunha, Joao J. da [Eletronuclear Eletrobras Termonuclear, Gerencia de Analise de Seguranca Nuclear, Rua da Candelaria, 65, 7o andar. Centro, Rio de Janeiro 20091-906 (Brazil); Lapa, Celso Marcelo F., E-mail: lapa@ien.gov.b [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil); Alvim, Antonio Carlos M. [Universidade Federal do Rio de Janeiro, COPPE/Nuclear, P.O. Box 68509, Cidade Universitaria, Ilha do Fundao s/n, Rio de Janeiro 21945-970 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil); Lima, Carlos A. Souza [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Politecnico, Universidade do Estado do Rio de Janeiro, Pos-Graduacao em Modelagem Computacional, Rua Alberto Rangel, s/n, Vila Nova, Nova Friburgo 28630-050 (Brazil); Pereira, Claudio Marcio do N.A. [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil)
2010-03-15
This work presents a methodology to investigate the viability of using particle swarm optimization technique to obtain the best combination of physical and operational parameters that lead to the best adjusted dimensionless groups, calculated by similarity laws, that are able to simulate the most relevant physical phenomena in single-phase flow under natural circulation and to offer an appropriate alternative reduced scale design for reactor primary loops with this flow characteristics. A PWR reactor core, under natural circulation, based on LOFT test facility, was used as the case study. The particle swarm optimization technique was applied to a problem with these thermo-hydraulics conditions and results demonstrated the viability and adequacy of the method to design similar systems with these characteristics.
Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Yudong Zhang
2011-05-01
Full Text Available This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM based texture features. Then, the features were reduced by principle component analysis (PCA. Finally, a two-hidden-layer forward neural network (NN was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO. K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP, adaptive BP (ABP, momentum BP (MBP, Particle Swarm Optimization (PSO, and Resilient back-propagation (RPROP methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.
Hybrid particle swarm optimization algorithm and its application in nuclear engineering
International Nuclear Information System (INIS)
Liu, C.Y.; Yan, C.Q.; Wang, J.J.
2014-01-01
Highlights: • We propose a hybrid particle swarm optimization algorithm (HPSO). • Modified Nelder–Mead simplex search method is applied in HPSO. • The algorithm has a high search precision and rapidly calculation speed. • HPSO can be used in the nuclear engineering optimization design problems. - Abstract: A hybrid particle swarm optimization algorithm with a feasibility-based rule for solving constrained optimization problems has been developed in this research. Firstly, the global optimal solution zone can be obtained through particle swarm optimization process, and then the refined search of the global optimal solution will be achieved through the modified Nelder–Mead simplex algorithm. Simulations based on two well-studied benchmark problems demonstrate the proposed algorithm will be an efficient alternative to solving constrained optimization problems. The vertical electrical heating pressurizer is one of the key components in reactor coolant system. The mathematical model of pressurizer has been established in steady state. The optimization design of pressurizer weight has been carried out through HPSO algorithm. The results show the pressurizer weight can be reduced by 16.92%. The thermal efficiencies of conventional PWR nuclear power plants are about 31–35% so far, which are much lower than fossil fueled plants based in a steam cycle as PWR. The thermal equilibrium mathematic model for nuclear power plant secondary loop has been established. An optimization case study has been conducted to improve the efficiency of the nuclear power plant with the proposed algorithm. The results show the thermal efficiency is improved by 0.5%
Zheng, Y.; Chen, J.
2017-09-01
A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.
Particle Swarm Optimization Based on Local Attractors of Ordinary Differential Equation System
Directory of Open Access Journals (Sweden)
Wenyu Yang
2014-01-01
Full Text Available Particle swarm optimization (PSO is inspired by sociological behavior. In this paper, we interpret PSO as a finite difference scheme for solving a system of stochastic ordinary differential equations (SODE. In this framework, the position points of the swarm converge to an equilibrium point of the SODE and the local attractors, which are easily defined by the present position points, also converge to the global attractor. Inspired by this observation, we propose a class of modified PSO iteration methods (MPSO based on local attractors of the SODE. The idea of MPSO is to choose the next update state near the present local attractor, rather than the present position point as in the original PSO, according to a given probability density function. In particular, the quantum-behaved particle swarm optimization method turns out to be a special case of MPSO by taking a special probability density function. The MPSO methods with six different probability density functions are tested on a few benchmark problems. These MPSO methods behave differently for different problems. Thus, our framework not only gives an interpretation for the ordinary PSO but also, more importantly, provides a warehouse of PSO-like methods to choose from for solving different practical problems.
R2-Based Multi/Many-Objective Particle Swarm Optimization
Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar
2016-01-01
We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA. PMID:27656200
Directory of Open Access Journals (Sweden)
A. M. Dalavi
2015-09-01
Full Text Available Optimization of hole-making operations plays a crucial role in which tool travel and tool switch scheduling are the two major issues. Industrial applications such as moulds, dies, engine block etc. consist of large number of holes having different diameters, depths and surface finish. This results into to a large number of machining operations like drilling, reaming or tapping to achieve the final size of individual hole. Optimal sequence of operations and associated cutting speeds, which reduce the overall processing cost of these hole-making operations are essential to reach desirable products. In order to achieve this, an attempt is made by developing an effective methodology. An example of the injection mould is considered to demonstrate the proposed approach. The optimization of this example is carried out using recently developed particle swarm optimization (PSO algorithm. The results obtained using PSO are compared with those obtained using tabu search method. It is observed that results obtained using PSO are slightly better than those obtained using tabu search method.
Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks
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Y. Harold Robinson
2015-01-01
Full Text Available Mobile ad hoc network (MANET is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO that uses continuous time recurrent neural network (CTRNN to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.
Rayleigh wave dispersion curve inversion by using particle swarm optimization and genetic algorithm
Buyuk, Ersin; Zor, Ekrem; Karaman, Abdullah
2017-04-01
Inversion of surface wave dispersion curves with its highly nonlinear nature has some difficulties using traditional linear inverse methods due to the need and strong dependence to the initial model, possibility of trapping in local minima and evaluation of partial derivatives. There are some modern global optimization methods to overcome of these difficulties in surface wave analysis such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). GA is based on biologic evolution consisting reproduction, crossover and mutation operations, while PSO algorithm developed after GA is inspired from the social behaviour of birds or fish of swarms. Utility of these methods require plausible convergence rate, acceptable relative error and optimum computation cost that are important for modelling studies. Even though PSO and GA processes are similar in appearence, the cross-over operation in GA is not used in PSO and the mutation operation is a stochastic process for changing the genes within chromosomes in GA. Unlike GA, the particles in PSO algorithm changes their position with logical velocities according to particle's own experience and swarm's experience. In this study, we applied PSO algorithm to estimate S wave velocities and thicknesses of the layered earth model by using Rayleigh wave dispersion curve and also compared these results with GA and we emphasize on the advantage of using PSO algorithm for geophysical modelling studies considering its rapid convergence, low misfit error and computation cost.
Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references
Janapati, Ravi Chander; Balaswamy, Ch.; Soundararajan, K.
2016-08-01
Localization is the key research area in Wireless Sensor Networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao Bound (CRB). This censoring scheme can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper Distributed localization algorithm PSO with CRB is proposed. Proposed method shows better results in terms of position accuracy, latency and complexity.
Hybrid firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block
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Padmavathi Kora
2016-03-01
Full Text Available Abnormal Cardiac beat identification is a key process in the detection of heart ailments. This work proposes a technique for the detection of Bundle Branch Block (BBB using hybrid Firefly and Particle Swarm Optimization (FFPSO technique in combination with Levenberg Marquardt Neural Network (LMNN classifier. BBB is developed when there is a block along the electrical impulses travel to make heart to beat. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining the two meta-heuristic optimization methods, Firefly algorithm (FFA and Particle Swarm Optimization (PSO, for feature optimization of ECG (BBB and normal patterns. One of the major controlling forces is the light intensity attraction of FFA algorithm that models the optimum solution. The light intensity attraction process of the FFA algorithm depends on random directions for search, this may delay in achieving the global optimization solution. The hybrid technique FFPSO, integrates the concepts from FF algorithm and PSO and creates new individuals. In the FFPSO method the local search is performed through the modified light intensity attraction step with PSO operator. The FFPSO features are compared with the classical FF, PSO features. The FFPSO feature values are given as the input to the Levenberg Marquardt Neural Network (LM NN classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.
Krohling, Renato A; Coelho, Leandro dos Santos
2006-12-01
In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability distribution is used to generate random numbers for the accelerating coefficients of the local and global terms. We propose a Gaussian probability distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.
Application of a particle swarm optimization in an optimal power flow ...
African Journals Online (AJOL)
In this paper an efficient and Particle Swarm Optimization (PSO) has been presented for solving the economic dispatch problem. The objective is to minimize the total generation fuel and keep the power outputs of generators; bus voltages and transformer tap setting in their secure limits. The conventional load flow and ...
Diyana Rosli, Anis; Adenan, Nur Sabrina; Hashim, Hadzli; Ezan Abdullah, Noor; Sulaiman, Suhaimi; Baharudin, Rohaiza
2018-03-01
This paper shows findings of the application of Particle Swarm Optimization (PSO) algorithm in optimizing an Artificial Neural Network that could categorize between ripeness and unripeness stage of citrus suhuensis. The algorithm would adjust the network connections weights and adapt its values during training for best results at the output. Initially, citrus suhuensis fruit’s skin is measured using optically non-destructive method via spectrometer. The spectrometer would transmit VIS (visible spectrum) photonic light radiation to the surface (skin of citrus) of the sample. The reflected light from the sample’s surface would be received and measured by the same spectrometer in terms of reflectance percentage based on VIS range. These measured data are used to train and test the best optimized ANN model. The accuracy is based on receiver operating characteristic (ROC) performance. The result outcomes from this investigation have shown that the achieved accuracy for the optimized is 70.5% with a sensitivity and specificity of 60.1% and 80.0% respectively.
Directory of Open Access Journals (Sweden)
Xue-cun Yang
2015-01-01
Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.
A Swarm Optimization Algorithm for Multimodal Functions and Its Application in Multicircle Detection
Directory of Open Access Journals (Sweden)
Erik Cuevas
2013-01-01
Full Text Available In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global are known, the implementation can be quickly switched to another solution without much interrupting the design process. This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents emulate a group of animals which interact with each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi-circle detection, achieving satisfactory results.
Speed control of optimal designed PMBLDC motor using improved fuzzy particle swarm optimization
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Reza Saravani
2014-09-01
Full Text Available Permanent brushless dc motors have been used in many areas. Considering to their vast advantages, researchers have studied extensively for speed control and reducing the torque ripple of this motors. But a little study was done for both speed control and optimum design of them. This paper presents for the optimal design of a PMBLDC motor with goal of reducing volume and building cost. In addition the speed control aim is considered using a multi-objective nonlinear cost function which is solved by fuzzy particle swarm optimization. First characteristics of motor are expressed as functions of motor geometries. Then cost function which combines the step response characteristic of motor speed, building cost and its volume is constructed and minimized. To reach this goal in this application the new improved fuzzy particle swam optimization is used for the first time. The results of simulations show that this method has good ability and efficiency in reaching global best point in compare of GA and PSO methods.
Optimierung von FSS-Bandpassfiltern mit Hilfe der Schwarmintelligenz (Particle Swarm Optimization)
Wu, G.; Hansen, V.; Kreysa, E.; Gemünd, H.-P.
2006-09-01
In diesem Beitrag wird ein neues Verfahren zur Optimierung von Bandpassfiltern aus mehrlagigen frequenzselektiven Schirmen (FSS), die in ein Dielektrikum eingebettet sind, vorgestellt. Das Ziel ist es, die Parameter der gesamten Struktur so zu optimieren, dass ihre Transmissionseigenschaften hohe Filteranforderungen erfüllen. Als Optimierungsverfahren wird die Particle Swarm Optimization (PSO) eingesetzt. PSO ist eine neue stochastische Optimierungsmethode, die in verschieden Gebieten, besonders aber bei der Optimierung nicht linearer Probleme mit mehreren Zielfunktionen erfolgreich eingesetzt wird. In dieser Arbeit wird die PSO in die Spektralbereichsanalyse zur Berechnung komplexer FSS-Strukturen integriert. Die numerische Berechnung basiert auf einer Integralgleichungsformulierung mit Hilfe der spektralen Greenschen Funktion für geschichtete Strukturen. This paper presents a novel procedure for the optimization of band-pass filters consisting of frequency selective surfaces (FSS) embedded in a dielectric. The aim is to optimize the parameters of the complete structure so that the transmission characteristics of the filters fulfill the demanding requirements. The Particle Swarm Optimization (PSO) is used as the optimization procedure. PSO is a new stochastic optimization method that is successfully applied in different areas for the optimization of non-linear problems with several object-functions. In this work, PSO is integrated into the spectral domain analysis for the calculation of the complex FSS structures. The numerical computation is based on the formulation of an integral equation with the help of the spectral Green's function for layered media.
Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm
International Nuclear Information System (INIS)
Duan, Chen; Wang, Xinggang; Shu, Shuiming; Jing, Changwei; Chang, Huawei
2014-01-01
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
Optimierung von FSS-Bandpassfiltern mit Hilfe der Schwarmintelligenz (Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
G. Wu
2006-01-01
Full Text Available In diesem Beitrag wird ein neues Verfahren zur Optimierung von Bandpassfiltern aus mehrlagigen frequenzselektiven Schirmen (FSS, die in ein Dielektrikum eingebettet sind, vorgestellt. Das Ziel ist es, die Parameter der gesamten Struktur so zu optimieren, dass ihre Transmissionseigenschaften hohe Filteranforderungen erfüllen. Als Optimierungsverfahren wird die Particle Swarm Optimization (PSO eingesetzt. PSO ist eine neue stochastische Optimierungsmethode, die in verschieden Gebieten, besonders aber bei der Optimierung nicht linearer Probleme mit mehreren Zielfunktionen erfolgreich eingesetzt wird. In dieser Arbeit wird die PSO in die Spektralbereichsanalyse zur Berechnung komplexer FSS-Strukturen integriert. Die numerische Berechnung basiert auf einer Integralgleichungsformulierung mit Hilfe der spektralen Greenschen Funktion für geschichtete Strukturen. This paper presents a novel procedure for the optimization of band-pass filters consisting of frequency selective surfaces (FSS embedded in a dielectric. The aim is to optimize the parameters of the complete structure so that the transmission characteristics of the filters fulfill the demanding requirements. The Particle Swarm Optimization (PSO is used as the optimization procedure. PSO is a new stochastic optimization method that is successfully applied in different areas for the optimization of non-linear problems with several object-functions. In this work, PSO is integrated into the spectral domain analysis for the calculation of the complex FSS structures. The numerical computation is based on the formulation of an integral equation with the help of the spectral Green's function for layered media.
Application of particle swarm optimization to interpret Rayleigh wave dispersion curves
Song, Xianhai; Tang, Li; Lv, Xiaochun; Fang, Hongping; Gu, Hanming
2012-09-01
Rayleigh waves have been used increasingly as an appealing tool to obtain near-surface shear (S)-wave velocity profiles. However, inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this study, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme based on particle swarm optimization (PSO). PSO is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate calculation efficiency and stability of PSO to inversion of surface wave data, we first inverted three noise-free and three noise-corrupted synthetic data sets. Then, we made a comparative analysis with genetic algorithms (GA) and a Monte Carlo (MC) sampler and reconstructed a histogram of model parameters sampled on a low-misfit region less than 15% relative error to further investigate the performance of the proposed inverse procedure. Finally, we inverted a real-world example from a waste disposal site in NE Italy to examine the applicability of PSO on Rayleigh wave dispersion curves. Results from both synthetic and field data demonstrate that particle swarm optimization can be used for quantitative interpretation of Rayleigh wave dispersion curves. PSO seems superior to GA and MC in terms of both reliability and computational efforts. The great advantages of PSO are fast in locating the low misfit region and easy to implement. Also there are only three parameters to tune (inertia weight or constriction factor, local and global acceleration constants). Theoretical results exist to explain how to tune these parameters.
Generation Expansion Planning as Particle Swarm Optimization with Gridified SATyrus
International Nuclear Information System (INIS)
Diacovo, R.; Franca, F. M. G.; Lima, P. M. V.
2007-01-01
This work introduces our first attempt on using the Grid to solve a real-life problem with the SATyrus platform. In electrical engineering, a challenging task is to find the less expensive ways to expand the energy production capacity, supporting an increasing demand. This is the definition of the generation expansion planning problem (GEP). We decided to investigate the Particle Swarm Optimization (PSO) paradigm for this task, due to its efficiency and arbitrary memory requirements, the last one being a desirable characteristic for any solver running on a Grid environment. The PSO was used in conjunction with the SATyrus platform, which stands for an energy function synthesizer. We hope the results presented here will help to evolve SATyrus into a reliable generic problem solver. (Author)
A Swarm Optimization approach for clinical knowledge mining.
Christopher, J Jabez; Nehemiah, H Khanna; Kannan, A
2015-10-01
Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models
Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237
Application of Improved Particle Swarm Optimization Algorithm in UCAV Path Planning
Ma, Qianzhi; Lei, Xiujuan
For the calculation complexity and the convergence in Unmanned Combat Aerial Vehicle (UCAV) path planning, the path planning method based on Second-order Oscillating Particle Swarm Optimization (SOPSO) was proposed to improve the properties of solutions, in which the searching ability of particles was enhanced by controlling the process of oscillating convergence and asymptotic convergence. A novel method of perceiving threats was applied for advancing the feasibility of the path. A comparison of the results was made by WPSO, CFPSO and SOPSO, which showed that the method we proposed in this paper was effective. SOPSO was much more suitable for solving this kind of problem.
Guo, Siqiu; Zhang, Tao; Song, Yulong; Qian, Feng
2018-04-23
This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.
Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.
Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung
2017-04-01
Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.
International Nuclear Information System (INIS)
Luo, Xiongbiao; Wan, Ying; He, Xiangjian
2015-01-01
Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was
International Nuclear Information System (INIS)
Zou, Dexuan; Li, Steven; Li, Zongyan; Kong, Xiangyong
2017-01-01
Highlights: • A new global particle swarm optimization (NGPSO) is proposed. • NGPSO has strong convergence and desirable accuracy. • NGPSO is used to handle the economic emission dispatch with or without transmission losses. • The equality constraint can be satisfied by solving a quadratic equation. • The inequality constraints can be satisfied by using penalty function method. - Abstract: A new global particle swarm optimization (NGPSO) algorithm is proposed to solve the economic emission dispatch (EED) problems in this paper. NGPSO is different from the traditional particle swarm optimization (PSO) algorithm in two aspects. First, NGPSO uses a new position updating equation which relies on the global best particle to guide the searching activities of all particles. Second, it uses the randomization based on the uniform distribution to slightly disturb the flight trajectories of particles during the late evolutionary process. The two steps enable NGPSO to effectively execute a number of global searches, and thus they increase the chance of exploring promising solution space, and reduce the probabilities of getting trapped into local optima for all particles. On the other hand, the two objective functions of EED are normalized separately according to all candidate solutions, and then they are incorporated into one single objective function. The transformation steps are very helpful in eliminating the difference caused by the different dimensions of the two functions, and thus they strike a balance between the fuel cost and emission. In addition, a simple and common penalty function method is employed to facilitate the satisfactions of EED’s constraints. Based on these improvements in PSO, objective functions and constraints handling, high-quality solutions can be obtained for EED problems. Five examples are chosen to testify the performance of three improved PSOs on solving EED problems with or without transmission losses. Experimental results show that
Luo, Xiongbiao; Wan, Ying; He, Xiangjian
2015-04-01
Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm
Directory of Open Access Journals (Sweden)
Bao-Shou Zhang
2016-09-01
Full Text Available Four-rotor dish-shaped unmanned underwater vehicles (FRDS UUVs are new type underwater vehicles. The main goal of this paper is to develop a quick method to optimize the design of hydraulic support landing platform for the new UUV. In this paper, the geometry configuration and instability type of the platform are defined. Computational investigations are carried out to study the hydrodynamic performance of the landing platform using the Computational Fluid Dynamics (CFD method. Then, the response surface model of the optimization objective is established. The intelligent particle swarm optimization (PSO is applied to finding the optimal solution. The result demonstrates that the stability of landing platform is significantly improved with the global objective index increasing from 1.045 to 1.158 (10.86% higher after the optimization process.
Design of shared unit-dose drug distribution network using multi-level particle swarm optimization.
Chen, Linjie; Monteiro, Thibaud; Wang, Tao; Marcon, Eric
2018-03-01
Unit-dose drug distribution systems provide optimal choices in terms of medication security and efficiency for organizing the drug-use process in large hospitals. As small hospitals have to share such automatic systems for economic reasons, the structure of their logistic organization becomes a very sensitive issue. In the research reported here, we develop a generalized multi-level optimization method - multi-level particle swarm optimization (MLPSO) - to design a shared unit-dose drug distribution network. Structurally, the problem studied can be considered as a type of capacitated location-routing problem (CLRP) with new constraints related to specific production planning. This kind of problem implies that a multi-level optimization should be performed in order to minimize logistic operating costs. Our results show that with the proposed algorithm, a more suitable modeling framework, as well as computational time savings and better optimization performance are obtained than that reported in the literature on this subject.
Parallel particle swarm optimization algorithm in nuclear problems
Energy Technology Data Exchange (ETDEWEB)
Waintraub, Marcel; Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)], e-mail: marcel@ien.gov.br, e-mail: cmnap@ien.gov.br; Schirru, Roberto [Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Lab. de Monitoracao de Processos], e-mail: schirru@lmp.ufrj.br
2009-07-01
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)
Parallel particle swarm optimization algorithm in nuclear problems
International Nuclear Information System (INIS)
Waintraub, Marcel; Pereira, Claudio M.N.A.; Schirru, Roberto
2009-01-01
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)
Particle swarm optimization with scale-free interactions.
Directory of Open Access Journals (Sweden)
Chen Liu
Full Text Available The particle swarm optimization (PSO algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks.
Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.
Elhossini, Ahmed; Areibi, Shawki; Dony, Robert
2010-01-01
This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
Optimization of wireless sensor networks based on chicken swarm optimization algorithm
Wang, Qingxi; Zhu, Lihua
2017-05-01
In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.
Nurmaini, Siti
2013-01-01
Dalam paper ini dijelaskan teknik komunikasi swarm robot untuk mencapai suatu target yang telah ditentukan. Pada percobaan ini digunakan 3 robot sederhana yang identik dengan 3 sensor infra-red, sensor kompas dan X-Bee. Untuk mencapai target dan menentukan posisi dari masing-masing robot digunakan sebuah sensor kamera dengan metode deteksi perbedaan warna. Swarm robot dan sensor kamera terhubung dengan komputer yang berfungsi sebagai pusat informasi dan penyim...
A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization
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Lizhi Cui
2014-01-01
Full Text Available This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO, for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1 the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2 the GRCM-PSO method is able to handle the real HPLC-DAD data set.
A self-learning particle swarm optimizer for global optimization problems.
Li, Changhe; Yang, Shengxiang; Nguyen, Trung Thanh
2012-06-01
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization
Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin
2017-09-01
Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.
Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization
International Nuclear Information System (INIS)
Yong, Li; Ying-Gan, Tang
2010-01-01
A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method
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Khanagha Ali
2010-01-01
Full Text Available Blind identification of MIMO FIR systems has widely received attentions in various fields of wireless data communications. Here, we use Particle Swarm Optimization (PSO as the update mechanism of the well-known inverse filtering approach and we show its good performance compared to original method. Specially, the proposed method is shown to be more robust against lower SNR scenarios or in cases with smaller lengths of available data records. Also, a modified version of PSO is presented which further improves the robustness and preciseness of PSO algorithm. However the most important promise of the modified version is its drastically faster convergence compared to standard implementation of PSO.
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Francisco S. de Albuquerque Filho
2013-01-01
Full Text Available This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.
Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization
Schutte, Jaco F.; Koh, Byung; Reinbolt, Jeffrey A.; Haftka, Raphael T.; George, Alan D.; Fregly, Benjamin J.
2006-01-01
Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm’s global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms—a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units. PMID:16060353
Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following
Wiech, Jakub; Eremeyev, Victor A.; Giorgio, Ivan
2018-04-01
In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method.
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Abdelhafid HASNI
2009-03-01
Full Text Available Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO algorithm which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results.
Optimasi Desain Heat Exchanger dengan Menggunakan Metode Particle Swarm Optimization
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Rifnaldi Veriyawan
2014-09-01
Full Text Available Industri proses terutama perminyakan adalah salah satu industri membutuhkan energi panas dengan jumlah kapasitas besar. Dengan berjalan perkembangan teknologi dibutuhkannya proses perpindahan panas dalam jumlah besar. Tetapi dengan besarnya penukaran panas yang diberikan maka besar pula luas permukaan. Dibutuhkannya optimasi pada desain heat exchanger terutama shell-and-tube¬. Dalam tugas akhir ini, Algoritma particle swarm optimization (PSO digunakan untuk mengoptimasikan nilai koefesien perpindahan panas keseluruhan dengan mendapatkan nilai terbaik. Perumusan fungsi tujuan nilai perpindahan panas keseluruhan (U, dan luas permukaan (A yang digunakan untuk mencari nilai fungsi objektif pada PSO. Partikel dalam PSO menyatakan sebagai posisi atau solusi dari hasil optimasi didapatnya nilai perpindahan panas maksimal dengan luas permukaan dan pressure drop dibawah data desain atau datasheet. Partikel tersebut dalam pemodelan berupa rentang nilai minimal dan maksimal dari diameter luar diantara (do dan jumlah baffle (Nb. Dari hasil optimasi pada tiga HE didapatkan nilai U dan A secara berturut-turut; HE E-1111 472 W/m2C dan 289 m2 ;pada HE E-1107 174 W/m2C dan 265 m2 ; dan HE E-1102 618 W/m2C dan 574 m2. Nilai perpindahan panas keseluruhan yang telah dioptimasi sesuai dengan fungsi objektif dapat dikatakan HE shell-and-tube mencapai titik optimal.
Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization
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Moncef Gabbouj
2009-01-01
Full Text Available Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO for finding optimal (number of dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis- similarities over HSV (or HSL color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.
Empirically characteristic analysis of chaotic PID controlling particle swarm optimization.
Yan, Danping; Lu, Yongzhong; Zhou, Min; Chen, Shiping; Levy, David
2017-01-01
Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO), we herein propose a chaotic proportional integral derivative (PID) controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles' search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles' premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA) and PSO.
Wang, Y. M.; Xu, W. C.; Wu, S. Q.; Chai, C. W.; Liu, X.; Wang, S. H.
2018-03-01
The torsional oscillation is the dominant vibration form for the impression cylinder of printing machine (printing cylinder for short), directly restricting the printing speed up and reducing the quality of the prints. In order to reduce torsional vibration, the active control method for the printing cylinder is obtained. Taking the excitation force and moment from the cylinder gap and gripper teeth open & closing cam mechanism as variable parameters, authors establish the dynamic mathematical model of torsional vibration for the printing cylinder. The torsional active control method is based on Particle Swarm Optimization(PSO) algorithm to optimize input parameters for the serve motor. Furthermore, the input torque of the printing cylinder is optimized, and then compared with the numerical simulation results. The conclusions are that torsional vibration active control based on PSO is an availability method to the torsional vibration of printing cylinder.
DEFF Research Database (Denmark)
Vesterstrøm, Jacob Svaneborg; Thomsen, Rene
2004-01-01
Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance...
Janaki, Sathya D.; Geetha, K.
2017-06-01
Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting
Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.
2016-01-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048
Mohamad, Mohd Saberi; Omatu, Sigeru; Deris, Safaai; Yoshioka, Michifumi; Abdullah, Afnizanfaizal; Ibrahim, Zuwairie
2013-04-24
Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes. We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle's position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets. The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO.
Applying Swarm Optimization Techniques to Calculate Execution Time for Software Modules
Nagy Ramadan Darwish; Ahmed A. Mohamed; Bassem S. M. Zohdy
2016-01-01
This research aims to calculate the execution time for software modules, using Particle Swarm Optimization (PSO) and Parallel Particle Swarm Optimization (PPSO), in order to calculate the proper time. A comparison is made between MATLAB Code without Algorithm (MCWA), PSO and PPSO to figure out the time produced when executing any software module. The proposed algorithms which include the PPSO increase the speed of executing the algorithm itself, in order to achieve quick results. This researc...
Directory of Open Access Journals (Sweden)
Neeraj Kanwar
2015-01-01
Full Text Available This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.
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Hui Wang
2018-02-01
Full Text Available The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply’s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS composed of batteries and ultracapacitors in this paper. A quantum-behaved particle swarm optimization (QPSO algorithm that optimizes the HESS capacity is used. Based on the respective power compensation capabilities of ultracapacitors and batteries, a rational energy scheduling strategy is proposed using the principle of a low-pass filter and can help to avoid frequent batteries charging and discharging. Considering the rated power of each energy storage type, the respective compensation power is corrected. By determining whether the charging state reaches the limit, the value is corrected again. Additionally, a mathematical model that minimizes the daily cost of the HESS is derived. This paper takes an isolated micrgrid in north China as an example to verify the effectiveness of this method. The comparison between QPSO and a traditional particle swarm algorithm shows that QPSO can find the optimal solution faster and the HESS has lower daily cost. Simulation results for an isolated microgrid verified the effectiveness of the HESS optimal capacity configuration method.
An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm
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Kai Hu
2015-01-01
Full Text Available Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR.
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Chao Wang
2016-08-01
Full Text Available In this article, an adaptive particle swarm optimization wavelet neural network with double sliding modes controller is proposed to address the complex nonlinearities and uncertainties in the electric load simulator. The adaptive double sliding modes–particle swarm optimization wavelet neural network algorithm with the self-learning structures and parameters is designed as a torque tracking controller, in which a number of hidden nodes are added and pruned by the structure learning algorithm, and the parameters are online adjusted by the adaptive particle swarm optimization at the same time. Moreover, one conventional sliding mode is introduced to track the time-varying reference command, and the other complementary sliding mode is adopted to attenuate the effect of the approximation error. Furthermore, the relative parameters should comply with some estimation laws on the basis of the Lyapunov theory used to guarantee the system stability. Finally, the simulation experiments are carried out on the hardware-in-the-loop platform for the electric load simulator, the performance of the adaptive double sliding modes–particle swarm optimization wavelet neural network with structure learning is verified compared with some similar control methods. In addition, different amplitudes and frequencies of the reference commands are introduced to further evaluate the effectiveness and robustness of the proposed algorithms.
Dynamic path planning for mobile robot based on particle swarm optimization
Wang, Yong; Cai, Feng; Wang, Ying
2017-08-01
In the contemporary, robots are used in many fields, such as cleaning, medical treatment, space exploration, disaster relief and so on. The dynamic path planning of robot without collision is becoming more and more the focus of people's attention. A new method of path planning is proposed in this paper. Firstly, the motion space model of the robot is established by using the MAKLINK graph method. Then the A* algorithm is used to get the shortest path from the start point to the end point. Secondly, this paper proposes an effective method to detect and avoid obstacles. When an obstacle is detected on the shortest path, the robot will choose the nearest safety point to move. Moreover, calculate the next point which is nearest to the target. Finally, the particle swarm optimization algorithm is used to optimize the path. The experimental results can prove that the proposed method is more effective.
Chen, Shyi-Ming; Hsin, Wen-Chyuan
2015-07-01
In this paper, we propose a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the slopes of fuzzy sets. We also propose a particle swarm optimization (PSO)-based weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of fuzzy rules for weighted fuzzy interpolative reasoning. We apply the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm to deal with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm outperforms the existing methods for dealing with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems.
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Fei Wang
2017-07-01
Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.
A Machine Learning and Optimization Toolkit for the Swarm
2014-11-17
Ptolemy II TerraSwarm Research Center 6 11/17/14 Machine Learning: 1...Toolkit in Ptolemy II TerraSwarm Research Center 7 11/17/14 State Es0ma0on: • Par0cle Filtering The Machine...Learning Toolkit in Ptolemy II TerraSwarm Research Center 8 11/17/14 Op0miza0on: • CompositeOp0mizer: An actor-‐
Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices
Ali, Abdelrahman; Siddharth, Siddharth; Syed, Zainab; El-Sheimy, Naser
2012-01-01
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually 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 magnetometers. 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. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs) when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS) applications.
Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices
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Naser El-Sheimy
2012-09-01
Full Text Available Inertial Navigation Systems (INS consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually 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 magnetometers. 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. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS applications.
Manipulator inverse kinematics control based on particle swarm optimization neural network
Wen, Xiulan; Sheng, Danghong; Guo, Jing
2008-10-01
The inverse kinematics control of a robotic manipulator requires solving non-linear equations having transcendental functions and involving time-consuming calculations. Particle Swarm Optimization (PSO), which is based on the behaviour of insect swarms and exploits the solution space by taking into account the experience of the single particle as well as that of the entire swarm, is similar to the genetic algorithm (GA) in that it performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable solution. In this paper, PSO is firstly proposed to optimize feed-forward neural network for manipulator inverse kinematics. Compared with the results of the fast back propagation learning algorithm (FBP), conventional GA genetic algorithm based elitist reservation (EGA), improved GA (IGA) and immune evolutionary computation (IEC), the simulation results verify the particle swarm optimization neural network (PSONN) is effective for manipulator inverse kinematics control.
A Survey of Formal Methods for Intelligent Swarms
Truszkowski, Walt; Rash, James; Hinchey, Mike; Rouff, Chrustopher A.
2004-01-01
cutting edge in system correctness, and requires higher levels of assurance than other (traditional) missions that use a single or small number of spacecraft that are deterministic in nature and have near continuous communication access. One of the highest possible levels of assurance comes from the application of formal methods. Formal methods are mathematics-based tools and techniques for specifying and verifying (software and hardware) systems. They are particularly useful for specifying complex parallel systems, such as exemplified by the ANTS mission, where the entire system is difficult for a single person to fully understand, a problem that is multiplied with multiple developers. Once written, a formal specification can be used to prove properties of a system (e.g., the underlying system will go from one state to another or not into a specific state) and check for particular types of errors (e.g., race or livelock conditions). A formal specification can also be used as input to a model checker for further validation. This report gives the results of a survey of formal methods techniques for verification and validation of space missions that use swarm technology. Multiple formal methods were evaluated to determine their effectiveness in modeling and assuring the behavior of swarms of spacecraft using the ANTS mission as an example system. This report is the first result of the project to determine formal approaches that are promising for formally specifying swarm-based systems. From this survey, the most promising approaches were selected and are discussed relative to their possible application to the ANTS mission. Future work will include the application of an integrated approach, based on the selected approaches identified in this report, to the formal specification of the ANTS mission.
Empirically characteristic analysis of chaotic PID controlling particle swarm optimization
Yan, Danping; Lu, Yongzhong; Zhou, Min; Chen, Shiping; Levy, David
2017-01-01
Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO), we herein propose a chaotic proportional integral derivative (PID) controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles’ search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles’ premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA) and PSO. PMID:28472050
Empirically characteristic analysis of chaotic PID controlling particle swarm optimization.
Directory of Open Access Journals (Sweden)
Danping Yan
Full Text Available Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO, we herein propose a chaotic proportional integral derivative (PID controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles' search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles' premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA and PSO.
OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD
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Dhananjay Kumar
2016-01-01
Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.
Direction Tracking of Multiple Moving Targets Using Quantum Particle Swarm Optimization
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Gao Hongyuan
2016-01-01
Full Text Available Based on weighted signal covariance (WSC matrix and maximum likelihood (ML estimation, a directionof-arrival (DOA estimation method of multiple moving targets is designed and named as WSC-ML in the presence of impulse noise. In order to overcome the shortcoming of the multidimensional search cost of maximum likelihood estimation, a novel continuous quantum particle swarm optimization (QPSO is proposed for this continuous optimization problem. And a tracking method of multiple moving targets in impulsive noise environment is proposed and named as QPSO-WSC-ML. Later, we make use of rank-one updating to update the weighted signal covariance matrix of WSC-ML. Simulation results illustrate the proposed QPSO-WSC-ML method is efficient and robust for the direction tracking of multiple moving targets in the presence of impulse noise.
International Nuclear Information System (INIS)
Azadani, E. Nasr; Hosseinian, S.H.; Moradzadeh, B.
2010-01-01
Competitive bidding for energy and ancillary services is increasingly recognized as an important part of electricity markets. In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constraint handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive electricity market considering the system constraints. The proposed technique shows promising performance for smooth and non smooth cost function as well. Three different systems are examined to demonstrate the effectiveness and the accuracy of the proposed algorithm. (author)
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MUDASIR AHMED MEMON
2017-01-01
Full Text Available In this paper, PSO (Particle Swarm Optimization based technique is proposed to derive optimized switching angles that minimizes the THD (Total Harmonic Distortion and reduces the effect of selected low order non-triple harmonics from the output of the multilevel inverter. Conventional harmonic elimination techniques have plenty of limitations, and other heuristic techniques also not provide the satisfactory results. In this paper, single phase symmetrical cascaded H-Bridge 11-Level multilevel inverter is considered, and proposed algorithm is utilized to obtain the optimized switching angles that reduced the effect of 5th, 7th, 11th and 13th non-triplen harmonics from the output voltage of the multilevel inverter. A simulation result indicates that this technique outperforms other methods in terms of minimizing THD and provides high-quality output voltage waveform.
Application of particle swarm optimization in gas turbine engine fuel controller gain tuning
Montazeri-Gh, M.; Jafari, S.; Ilkhani, M. R.
2012-02-01
This article presents the application of particle swarm optimization (PSO) for gain tuning of the gas turbine engine (GTE) fuel controller. For this purpose, the structure of a fuel controller is firstly designed based on the GTE control requirements and constraints. The controller gains are then tuned by PSO where the tuning process is formulated as an engineering optimization problem. In this study, the response time during engine acceleration and deceleration as well as the engine fuel consumption are considered as the objective functions. A computer simulation is also developed to evaluate the objective values for a single spool GTE. The GTE model employed for the simulation is a Wiener model, the parameters of which are extracted from experimental tests. In addition, the effect of neighbour acceleration on PSO results is studied. The results show that the neighbour acceleration factor has a considerable effect on the convergence rate of the PSO process. The PSO results are also compared with the results obtained through a genetic algorithm (GA) to show the relative merits of PSO. Moreover, the PSO results are compared with the results obtained from the dynamic programming (DP) method in order to illustrate the ability of proposed method in finding the global optimal solution. Furthermore, the objective function is also defined in multi-objective manner and the multi-objective particle swarm optimization (MOPSO) is applied to find the Pareto-front for the problem. Finally, the results obtained from the simulation of the optimized controller confirm the effectiveness of the proposed approach to design an optimal fuel controller resulting in an improved GTE performance as well as protection against the physical limitations.
Long, Zhili; Wang, Rui; Fang, Jiwen; Dai, Xufei; Li, Zuohua
2017-07-01
Piezoelectric actuators invariably exhibit hysteresis nonlinearities that tend to become significant under the open-loop condition and could cause oscillations and errors in nanometer-positioning tasks. Chaotic map modified particle swarm optimization (MPSO) is proposed and implemented to identify the Prandtl-Ishlinskii model for piezoelectric actuators. Hysteresis compensation is attained through application of an inverse Prandtl-Ishlinskii model, in which the parameters are formulated based on the original model with chaotic map MPSO. To strengthen the diversity and improve the searching ergodicity of the swarm, an initial method of adaptive inertia weight based on a chaotic map is proposed. To compare and prove that the swarm's convergence occurs before stochastic initialization and to attain an optimal particle swarm optimization algorithm, the parameters of a proportional-integral-derivative controller are searched using self-tuning, and the simulated results are used to verify the search effectiveness of chaotic map MPSO. The results show that chaotic map MPSO is superior to its competitors for identifying the Prandtl-Ishlinskii model and that the inverse Prandtl-Ishlinskii model can provide hysteresis compensation under different conditions in a simple and effective manner.
Luu, Keurfon; Noble, Mark; Gesret, Alexandrine; Belayouni, Nidhal; Roux, Pierre-François
2018-04-01
Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, global optimization methods based on MCMC are insensitive to the initial model but turn out to be computationally expensive. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few tuning parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim. We propose a Competitive PSO (CPSO) to help particles to escape from local minima with a simple implementation that improves swarm's diversity. The model space can be sampled by running the optimizer multiple times and by keeping all the models explored by the swarms in the different runs. A traveltime tomography algorithm based on CPSO is successfully applied on a real 3D data set in the context of induced seismicity.
International Nuclear Information System (INIS)
Sadeghzadeh, H.; Ehyaei, M.A.; Rosen, M.A.
2015-01-01
Highlights: • Calculating pressure drop and heat transfer coefficient by Delaware method. • The accuracy of the Delaware method is more than the Kern method. • The results of the PSO are better than the results of the GA. • The optimization results suggest that yields the best and most economic optimization. - Abstract: The use of genetic and particle swarm algorithms in the design of techno-economically optimum shell-and-tube heat exchangers is demonstrated. A cost function (including costs of the heat exchanger based on surface area and power consumption to overcome pressure drops) is the objective function, which is to be minimized. Selected decision variables include tube diameter, central baffles spacing and shell diameter. The Delaware method is used to calculate the heat transfer coefficient and the shell-side pressure drop. The accuracy and efficiency of the suggested algorithm and the Delaware method are investigated. A comparison of the results obtained by the two algorithms shows that results obtained with the particle swarm optimization method are superior to those obtained with the genetic algorithm method. By comparing these results with those from various references employing the Kern method and other algorithms, it is shown that the Delaware method accompanied by genetic and particle swarm algorithms achieves more optimum results, based on assessments for two case studies
Hayana Hasibuan, Eka; Mawengkang, Herman; Efendi, Syahril
2017-12-01
The use of Partical Swarm Optimization Algorithm in this research is to optimize the feature weights on the Voting Feature Interval 5 algorithm so that we can find the model of using PSO algorithm with VFI 5. Optimization of feature weight on Diabetes or Dyspesia data is considered important because it is very closely related to the livelihood of many people, so if there is any inaccuracy in determining the most dominant feature weight in the data will cause death. Increased accuracy by using PSO Algorithm ie fold 1 from 92.31% to 96.15% increase accuracy of 3.8%, accuracy of fold 2 on Algorithm VFI5 of 92.52% as well as generated on PSO Algorithm means accuracy fixed, then in fold 3 increase accuracy of 85.19% Increased to 96.29% Accuracy increased by 11%. The total accuracy of all three trials increased by 14%. In general the Partical Swarm Optimization algorithm has succeeded in increasing the accuracy to several fold, therefore it can be concluded the PSO algorithm is well used in optimizing the VFI5 Classification Algorithm.
Solving the Container Stowage Problem (CSP) using Particle Swarm Optimization (PSO)
Matsaini; Santosa, Budi
2018-04-01
Container Stowage Problem (CSP) is a problem of containers arrangement into ships by considering rules such as: total weight, weight of one stack, destination, equilibrium, and placement of containers on vessel. Container stowage problem is combinatorial problem and hard to solve with enumeration technique. It is an NP-Hard Problem. Therefore, to find a solution, metaheuristics is preferred. The objective of solving the problem is to minimize the amount of shifting such that the unloading time is minimized. Particle Swarm Optimization (PSO) is proposed to solve the problem. The implementation of PSO is combined with some steps which are stack position change rules, stack changes based on destination, and stack changes based on the weight type of the stacks (light, medium, and heavy). The proposed method was applied on five different cases. The results were compared to Bee Swarm Optimization (BSO) and heuristics method. PSO provided mean of 0.87% gap and time gap of 60 second. While BSO provided mean of 2,98% gap and 459,6 second to the heuristcs.
PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.
Ng, Marcus C K; Fong, Simon; Siu, Shirley W I
2015-06-01
Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .
Chuang, Li-Yeh; Moi, Sin-Hua; Lin, Yu-Da; Yang, Cheng-Hong
2016-10-01
Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset. Nine chaotic maps were used to improve PSO method results and compared the searching ability amongst all CPSO methods. The XOR and ZZ disease models were used to compare all chaotic maps combined with PSO method. Efficacy evaluations of CPSO methods were based on statistical values from the chi-square test (χ 2 ). The results showed that chaotic maps could improve the searching ability of PSO method when population are trapped in the local optimum. The minor allele frequency (MAF) indicated that, amongst all CPSO methods, the numbers of SNPs, sample size, and the highest χ 2 value in all datasets were found in the Sinai chaotic map combined with PSO method. We used the simple linear regression results of the gbest values in all generations to compare the all methods. Sinai chaotic map combined with PSO method provided the highest β values (β≥0.32 in XOR disease model and β≥0.04 in ZZ disease model) and the significant p-value (p-value<0.001 in both the XOR and ZZ disease models). The Sinai chaotic map was found to effectively enhance the fitness values (χ 2 ) of PSO method, indicating that the Sinai chaotic map combined with PSO method is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models. Copyright © 2016 Elsevier B.V. All rights reserved.
Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control
Directory of Open Access Journals (Sweden)
Allaoua Boumediene
2008-01-01
Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.
Optimisation of thin shell parts by using particle swarm optimisation (PSO) method
Hidayah, M. H. N.; Shayfull, Z.; Nasir, S. M.; Sazli, S. M.; Fathullah, M.
2017-09-01
This paper proposes an optimization model of process parameters in Plastic Injection Moulding (PIM) in which the quality characteristics for the plastic injection product that been study are warpage. In this study, plastic dispenser of dental floss (thin shell part) has been analysed with thermoplastic material of Polypropylene (PP) used as the moulded material. Design of Experiment (DOE), Response surface methodology (RSM) and Particle Swarm Optimisation (PSO) method were used to analyse the optimal process parameters setting. From optimal processing parameter, the value of warpage inx, y and z-axis have been optimised by 22.1%, 27.34% and 23.81%, respectively.
Tuning PID attitude stabilization of a quadrotor using particle swarm optimization (experimental
Directory of Open Access Journals (Sweden)
Khodja Mohammed Abdallah
2017-01-01
Full Text Available Proportional, Integral and Derivative (PID controllers are the most popular type of controller used in industrial applications because of their notable simplicity and effective implementation. However, manual tuning of these controllers is tedious and often leads to poor performance. The conventional Ziegler-Nichols (Z-N method of PID tuning was done experimentally enables easy identification stable PID parameters in a short time, but is accompanied by overshoot, high steady-state error, and large rise time. Therefore, in this study, the modern heuristics approach of Particle Swarm Optimization (PSO was employed to enhance the capabilities of the conventional Z-N technique. PSO with the constriction coefficient method experimentally demonstrated the ability to efficiently and effectively identify optimal PID controller parameters for attitude stabilization of a quadrotor.
Analysis of Population Diversity of Dynamic Probabilistic Particle Swarm Optimization Algorithms
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Qingjian Ni
2014-01-01
Full Text Available In evolutionary algorithm, population diversity is an important factor for solving performance. In this paper, combined with some population diversity analysis methods in other evolutionary algorithms, three indicators are introduced to be measures of population diversity in PSO algorithms, which are standard deviation of population fitness values, population entropy, and Manhattan norm of standard deviation in population positions. The three measures are used to analyze the population diversity in a relatively new PSO variant—Dynamic Probabilistic Particle Swarm Optimization (DPPSO. The results show that the three measure methods can fully reflect the evolution of population diversity in DPPSO algorithms from different angles, and we also discuss the impact of population diversity on the DPPSO variants. The relevant conclusions of the population diversity on DPPSO can be used to analyze, design, and improve the DPPSO algorithms, thus improving optimization performance, which could also be beneficial to understand the working mechanism of DPPSO theoretically.
Particle swarm optimization based PID controller tuning for level control of two tank system
Vincent, Anju K.; Nersisson, Ruban
2017-11-01
Automatic control plays a vital role in industrial operation. In process industries, in order to have an improved and stable control system, we need a robust tuning method. In this paper Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process. A two tank system is considered. Initially a PID controller is designed using an Internal Model Control (IMC). The results are compared with the PSO based controller setting. The performance of the controller is compared and analyzed by time domain specification. In order to validate the robustness of PID controller, disturbance is imposed. The system is simulated using MATLAB. The results show that the proposed method provides better controller performance.
Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich
2018-05-01
Application of the multi-arm space robot will be more effective than single arm especially when the target is tumbling. This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode. In order to overcome the dynamics singularities issue, the direct kinematics equations in conjunction with constrained PSO are employed for coordinated trajectory planning of dual-arm space robot. The joint trajectories are parametrized with Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for coordinated trajectory planning of two kinematically redundant manipulators mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.
Energy Technology Data Exchange (ETDEWEB)
Modarres, Hamid Reza; Kor, Mohammad; Abkhoshk, Emad; Alfi, Alireza; Lower, James C.
2009-06-15
In recent years, use of artificial neural networks have increased for estimation of Hardgrove grindability index (HGI) of coals. For training of the neural networks, gradient descent methods such as Backpropagaition (BP) method are used frequently. However they originally showed good performance in some non-linearly separable problems, but have a very slow convergence and can get stuck in local minima. In this paper, to overcome the lack of gradient descent methods, a novel particle swarm optimization and artificial neural network was employed for predicting the HGI of Kentucky coals by featuring eight coal parameters. The proposed approach also compared with two kinds of artificial neural network (generalized regression neural network and back propagation neural network). Results indicate that the neural networks - particle swarm optimization method gave the most accurate HGI prediction.
Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT
Directory of Open Access Journals (Sweden)
Xiaohua Nie
2017-01-01
Full Text Available Cat Swarm Optimization (CSO algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.
Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.
Nie, Xiaohua; Wang, Wei; Nie, Haoyao
2017-01-01
Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of "premature convergence," that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.
Energy Technology Data Exchange (ETDEWEB)
Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR, Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br
2009-04-15
The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.
Elephant swarm water search algorithm for global optimization
Indian Academy of Sciences (India)
The rising complexity of real-life optimization problems has constantly inspired computer researchers to develop new efficient optimization methods. ... reach nearest to global minima and enabled inference of all true regulations of GRN correctly with less computational time compared with the other existing metaheuristics.
International Nuclear Information System (INIS)
Kong, Xiangyong; Gao, Liqun; Ouyang, Haibin; Li, Steven
2015-01-01
In most research on redundancy allocation problem (RAP), the redundancy strategy for each subsystem is assumed to be predetermined and fixed. This paper focuses on a specific RAP with multiple strategy choices (RAP-MSC), in which both active redundancy and cold standby redundancy can be selected as an additional decision variable for individual subsystems. To do so, the component type, redundancy strategy and redundancy level for each subsystem should be chosen subject to the system constraints appropriately such that the system reliability is maximized. Meanwhile, imperfect switching for cold standby redundancy is considered and a k-Erlang distribution is introduced to model the time-to-failure component as well. Given the importance and complexity of RAP-MSC, we propose a new efficient simplified version of particle swarm optimization (SPSO) to solve such NP-hard problems. In this method, a new position updating scheme without velocity is presented with stochastic disturbance and a low probability. Moreover, it is compared with several well-known PSO variants and other state-of-the-art approaches in the literature to evaluate its performance. The experiment results demonstrate the superiority of SPSO as an alternative for solving the RAP-MSC. - Highlights: • A more realistic RAP form with multiple strategy choices is focused. • Redundancy strategies are to be selected rather than fixed in general RAP. • A new simplified particle swarm optimization is proposed. • Higher reliabilities are achieved than the state-of-the-art approaches.
Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.
Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar
2017-11-03
Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.
International Nuclear Information System (INIS)
Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo
2008-01-01
In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)
Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
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Bingbing Zhang
2017-03-01
Full Text Available Swarm is a European Space Agency (ESA project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD reduced-dynamic orbit solutions can be determined using space-borne GPS data and optimized pseudo-stochastic pulses under high ionospheric activity. We choose Swarm space-borne GPS data from 1–25 October 2014, and Swarm reduced-dynamic orbits are obtained. Orbit quality is assessed by GPS phase observation residuals and compared with Precise Science Orbits (PSOs released by ESA. Results show that pseudo-stochastic pulses with a time interval of 6 min and a priori standard deviation (STD of 10−2 mm/s in radial (R, along-track (T and cross-track (N directions are optimized to Swarm ZD reduced-dynamic precise orbit determination (POD. During high ionospheric activity, the mean Root Mean Square (RMS of Swarm GPS phase residuals is at 9–11 mm, Swarm orbit solutions are also compared with Swarm PSOs released by ESA and the accuracy of Swarm orbits can reach 2–4 cm in R, T and N directions. Independent Satellite Laser Ranging (SLR validation indicates that Swarm reduced-dynamic orbits have an accuracy of 2–4 cm. Swarm-B orbit quality is better than those of Swarm-A and Swarm-C. The Swarm orbits can be applied to the geomagnetic, geoelectric and gravity field recovery.
Raja, Muhammad Asif Zahoor; Khan, Junaid Ali; Ahmad, Siraj-ul-Islam; Qureshi, Ijaz Mansoor
2012-01-01
A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method. PMID:22919371
Raja, Muhammad Asif Zahoor; Khan, Junaid Ali; Ahmad, Siraj-ul-Islam; Qureshi, Ijaz Mansoor
2012-01-01
A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method.
Shah, Rahul H.
production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities. The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.
Particle Swarm Optimization of Low-Thrust, Geocentric-to-Halo-Orbit Transfers
Abraham, Andrew J.
Missions to Lagrange points are becoming increasingly popular amongst spacecraft mission planners. Lagrange points are locations in space where the gravity force from two bodies, and the centrifugal force acting on a third body, cancel. To date, all spacecraft that have visited a Lagrange point have done so using high-thrust, chemical propulsion. Due to the increasing availability of low-thrust (high efficiency) propulsive devices, and their increasing capability in terms of fuel efficiency and instantaneous thrust, it has now become possible for a spacecraft to reach a Lagrange point orbit without the aid of chemical propellant. While at any given time there are many paths for a low-thrust trajectory to take, only one is optimal. The traditional approach to spacecraft trajectory optimization utilizes some form of gradient-based algorithm. While these algorithms offer numerous advantages, they also have a few significant shortcomings. The three most significant shortcomings are: (1) the fact that an initial guess solution is required to initialize the algorithm, (2) the radius of convergence can be quite small and can allow the algorithm to become trapped in local minima, and (3) gradient information is not always assessable nor always trustworthy for a given problem. To avoid these problems, this dissertation is focused on optimizing a low-thrust transfer trajectory from a geocentric orbit to an Earth-Moon, L1, Lagrange point orbit using the method of Particle Swarm Optimization (PSO). The PSO method is an evolutionary heuristic that was originally written to model birds swarming to locate hidden food sources. This PSO method will enable the exploration of the invariant stable manifold of the target Lagrange point orbit in an effort to optimize the spacecraft's low-thrust trajectory. Examples of these optimized trajectories are presented and contrasted with those found using traditional, gradient-based approaches. In summary, the results of this dissertation find
Cho, Ming-Yuan; Hoang, Thi Thom
2017-01-01
Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.
Directory of Open Access Journals (Sweden)
Ming-Yuan Cho
2017-01-01
Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.
Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng
2016-01-01
Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.
Directory of Open Access Journals (Sweden)
Weitian Lin
2014-01-01
Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.
Energy and operation management of a microgrid using particle swarm optimization
Radosavljević, Jordan; Jevtić, Miroljub; Klimenta, Dardan
2016-05-01
This article presents an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different distributed generation units and energy storage devices. The proposed approach employs PSO to minimize the total energy and operating cost of the microgrid via optimal adjustment of the control variables of the EOM, while satisfying various operating constraints. Owing to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties and market prices, a probabilistic approach in the EOM is introduced. The proposed method is examined and tested on a typical grid-connected microgrid including fuel cell, gas-fired microturbine, wind turbine, photovoltaic and energy storage devices. The obtained results prove the efficiency of the proposed approach to solve the EOM of the microgrids.
Directory of Open Access Journals (Sweden)
Shilian Zheng
2014-08-01
Full Text Available In a dynamic spectrum access network, when a primary user (licensed user reappears on the current channel, cognitive radios (CRs need to vacate the channel and reestablish a communications link on some other channel to avoid interference to primary users, resulting in spectrum handoff. This paper studies the problem of designing target channel visiting order for spectrum handoff to minimize expected spectrum handoff delay. A particle swarm optimization (PSO based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithm performs far better than random target channel visiting scheme. The solutions obtained by PSO are very close to the optimal solution which further validates the effectiveness of the proposed method.
Energy Aware Swarm Optimization with Intercluster Search for Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Shanmugasundaram Thilagavathi
2015-01-01
Full Text Available Wireless sensor networks (WSNs are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO algorithm with modified connected dominating set (CDS based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH. Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-01-01
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655
Guo, Y C; Wang, H; Wu, H P; Zhang, M Q
2015-12-21
Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.
Statistical Stability Analysis for Particle Swarm Optimization Dynamics with Random Coefficients
Koguma, Yuji; Aiyhosi, Eitaro
Particle Swarm Optimization (PSO), a meta-heuristic global optimization method, has attracted special interest for its simple algorithm and high searching ability. The updating formula of PSO involves coefficients with random numbers as parameters to enhance diversification ability in searching for the global optimum. However, the randomness makes stability of the searching points difficult to be analyzed mathematically, and the users need to adjust the parameter values by trial and error. In this paper, stability of the stochastic dynamics of PSO is analyzed mathematically and exact stability condition taking the randomness into consideration is presented with an index “statistical eigenvalue”, which is a new concept to evaluate the degree of the stability of PSO dynamics. Accuracy and effectiveness of the proposed stability discrimination using the presented index are certified in numerical simulation for simple examples.
Wu, Qi
2010-03-01
Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.
Particle Swarm Optimization Based of the Maximum Photovoltaic ...
African Journals Online (AJOL)
Photovoltaic electricity is seen as an important source of renewable energy. The photovoltaic array is an unstable source of power since the peak power point depends on the temperature and the irradiation level. A maximum peak power point tracking is then necessary for maximum efficiency. In this work, a Particle Swarm ...
Differential Evolution and Particle Swarm Optimization for Partitional Clustering
DEFF Research Database (Denmark)
Krink, Thiemo; Paterlini, Sandra
2006-01-01
for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE...
Application of Advanced Particle Swarm Optimization Techniques to Wind-thermal Coordination
DEFF Research Database (Denmark)
Singh, Sri Niwas; Østergaard, Jacob; Yadagiri, J.
2009-01-01
wind-thermal coordination algorithm is necessary to determine the optimal proportion of wind and thermal generator capacity that can be integrated into the system. In this paper, four versions of Particle Swarm Optimization (PSO) techniques are proposed for solving wind-thermal coordination problem...
Directory of Open Access Journals (Sweden)
Qi Hu
2013-04-01
Full Text Available State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data’s real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.
Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm
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Kia Saeed
2015-03-01
Full Text Available Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane’s elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar’s weight, a 20% decrease in the concrete’s weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.
A swarm intelligence-based tuning method for the Sliding Mode Generalized Predictive Control.
Oliveira, J B; Boaventura-Cunha, J; Moura Oliveira, P B; Freire, H
2014-09-01
This work presents an automatic tuning method for the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Quadratic Programming (QP), thus yielding an online dual optimization scheme. Simulations and performance indexes for common process models in industry, such as nonminimum phase and time delayed systems, result in a better performance, improving robustness and tracking accuracy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
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Alireza Pourrousta
2012-04-01
Full Text Available Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.
International Nuclear Information System (INIS)
Nicolau, Andressa; Schirru, Roberto; Medeiros, Jose A.C.C.
2009-01-01
This work presents the results of a performance evaluation study of the quantum based algorithms, QEA (Quantum Inspired Evolutionary Algorithm) and QSE (Quantum Swarm Evolutionary), when applied to the transient identification optimization problem of a nuclear power station operating at 100% of full power. For the sake of evaluation of the algorithms 3 benchmark functions were used. When compared to other similar optimization methods QEA showed that it can be an efficient optimization tool, not only for combinatorial problems but also for numerical problems, particularly for complex problems as the identification of transients in a nuclear power station. (author)
PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization
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Shuangqing Chen
2018-01-01
Full Text Available Particle swarm optimization (PSO and fireworks algorithm (FWA are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems.
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Shidrokh Goudarzi
2015-01-01
Full Text Available The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2 and mean square error (MSE based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.
He, Yaoyao; Yang, Shanlin; Xu, Qifa
2013-07-01
In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.
Pei, Zongrui; Eisenbach, Markus
2017-06-01
Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), the local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.
Kishore Singh, Kunal; Singh, Upendra Kumar
2017-04-01
Particle swarm optimization (PSO) is a global optimization technique that works similarly to swarms of birds searching for food. A MATLAB code in the PSO algorithm has been developed to estimate the depth to the bottom of a 2.5-D sedimentary basin and coefficients of regional background from observed gravity anomalies. The density contrast within the source is assumed to vary parabolically with depth. Initially, the PSO algorithm is applied on synthetic data with and without some Gaussian noise, and its validity is tested by calculating the depth of the Gediz Graben, western Anatolia, and the Godavari sub-basin, India. The Gediz Graben consists of Neogen sediments, and the metamorphic complex forms the basement of the graben. A thick uninterrupted sequence of Permian-Triassic and partly Jurassic and Cretaceous sediments forms the Godavari sub-basin. The PSO results are better correlated with results obtained by the Marquardt method and borehole information.
Image de-noising based on mathematical morphology and multi-objective particle swarm optimization
Dou, Liyun; Xu, Dan; Chen, Hao; Liu, Yicheng
2017-07-01
To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.
Directory of Open Access Journals (Sweden)
Jing Li
2017-01-01
Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.
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Mostafa Lotfi Forushani
2012-04-01
Full Text Available This paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required for designing a set of direct force-modes for the longitudinal axis based on particle swarm optimization (PSO algorithm. The autopilot system for military or civil aircraft is an essential component and in this paper, the autopilot system via 6 degree of freedom model for the control and guidance of aircraft in which the autopilot design will perform based on defining the longitudinal and the lateral-directional axes are supposed. The effectiveness of the proposed controller is illustrated by considering HIMAT aircraft. The simulation results verify merits of the proposed controller.
Proportional–Integral–Derivative (PID Controller Tuning using Particle Swarm Optimization Algorithm
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J. S. Bassi
2012-08-01
Full Text Available The proportional-integral-derivative (PID controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI method of particle swarm optimization (PSO algorithm for tuning the optimal proportional-integral derivative (PID controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler- Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler- Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
Xu, Xue-song
2014-12-01
Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton-Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.
Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Boumediene ALLAOUA
2009-12-01
Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.
Directory of Open Access Journals (Sweden)
Olimpia PECINGINA
2016-05-01
Full Text Available Throughout the ages, man has continuously been involved with the process of optimization. In its earliest form, optimization consisted of unscientific rituals and prejudices like pouring libations and sacrificing animals to the gods, con- sulting the oracles, observing the positions of the stars, and watching the flight of birds. When the circumstances were appropriate, the timing was thought to be auspicious (or optimum for planting the crops or embarking on a war.
Fusing Swarm Intelligence and Self-Assembly for Optimizing Echo State Networks.
Martin, Charles E; Reggia, James A
2015-01-01
Optimizing a neural network's topology is a difficult problem for at least two reasons: the topology space is discrete, and the quality of any given topology must be assessed by assigning many different sets of weights to its connections. These two characteristics tend to cause very "rough." objective functions. Here we demonstrate how self-assembly (SA) and particle swarm optimization (PSO) can be integrated to provide a novel and effective means of concurrently optimizing a neural network's weights and topology. Combining SA and PSO addresses two key challenges. First, it creates a more integrated representation of neural network weights and topology so that we have just a single, continuous search domain that permits "smoother" objective functions. Second, it extends the traditional focus of self-assembly, from the growth of predefined target structures, to functional self-assembly, in which growth is driven by optimality criteria defined in terms of the performance of emerging structures on predefined computational problems. Our model incorporates a new way of viewing PSO that involves a population of growing, interacting networks, as opposed to particles. The effectiveness of our method for optimizing echo state network weights and topologies is demonstrated through its performance on a number of challenging benchmark problems.
Directory of Open Access Journals (Sweden)
Michala Jakubcová
2015-01-01
Full Text Available The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Directory of Open Access Journals (Sweden)
Yudong Zhang
2015-01-01
Full Text Available Particle swarm optimization (PSO is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO, population topology (as fully connected, von Neumann, ring, star, random, etc., hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization, extensions (to multiobjective, constrained, discrete, and binary optimization, theoretical analysis (parameter selection and tuning, and convergence analysis, and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms. On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
International Nuclear Information System (INIS)
Lee, Tsung-Ying; Chen, Chun-Lung
2007-01-01
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)
Kumyaito, Nattapon; Yupapin, Preecha; Tamee, Kreangsak
2018-01-08
An effective training plan is an important factor in sports training to enhance athletic performance. A poorly considered training plan may result in injury to the athlete, and overtraining. Good training plans normally require expert input, which may have a cost too great for many athletes, particularly amateur athletes. The objectives of this research were to create a practical cycling training plan that substantially improves athletic performance while satisfying essential physiological constraints. Adaptive Particle Swarm Optimization using ɛ-constraint methods were used to formulate such a plan and simulate the likely performance outcomes. The physiological constraints considered in this study were monotony, chronic training load ramp rate and daily training impulse. A comparison of results from our simulations against a training plan from British Cycling, which we used as our standard, showed that our training plan outperformed the benchmark in terms of both athletic performance and satisfying all physiological constraints.
Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng
2018-02-01
Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.
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Jun-Jie Ma
2007-03-01
Full Text Available The effectiveness of wireless sensor networks (WSNs depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named Ã¢Â€Âœvirtual force directed co-evolutionary particle swarm optimizationÃ¢Â€Â (VFCPSO, since this algorithm combines the co-evolutionary particle swarm optimization (CPSO with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.
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Yudong Zhang
2013-01-01
Full Text Available Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM with RBF kernel, using particle swarm optimization (PSO to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.
CSIR Research Space (South Africa)
Debba, Pravesh
2010-11-01
Full Text Available This paper reports on the results from ordinary least squares and ridge regression as statistical methods, and is compared to numerical optimization methods such as the stochastic method for global optimization, simulated annealing, particle swarm...
Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel
2012-11-21
We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.
Phase Behaviors of Reservoir Fluids with Capillary Eff ect Using Particle Swarm Optimization
Ma, Zhiwei
2013-05-06
The study of phase behavior is important for the oil and gas industry. Many approaches have been proposed and developed for phase behavior calculation. In this thesis, an alternative method is introduced to study the phase behavior by means of minimization of Helmholtz free energy. For a system at fixed volume, constant temperature and constant number of moles, the Helmholtz free energy reaches minimum at the equilibrium state. Based on this theory, a stochastic method called Particle Swarm Optimization (PSO) algorithm, is implemented to compute the phase diagrams for several pure component and mixture systems. After comparing with experimental and the classical PT-ash calculation, we found the phase diagrams obtained by minimization of the Helmholtz Free Energy approach match the experimental and theoretical diagrams very well. Capillary effect is also considered in this thesis because it has a significant influence on the phase behavior of reservoir fluids. In this part, we focus on computing the phase envelopes, which consists of bubble and dew point lines. Both fixed and calculated capillary pressure from the Young-Laplace equation cases are introduced to study their effects on phase envelopes. We found that the existence of capillary pressure will change the phase envelopes. Positive capillary pressure reduces the dew point and bubble point temperatures under the same pressure condition, while the negative capillary pressure increases the dew point and bubble point temperatures. In addition, the change of contact angle and pore radius will affect the phase envelope. The effect of the pore radius on the phase envelope is insignificant when the radius is very large. These results may become reference for future research and study. Keywords: Phase Behavior; Particle Swarm Optimization; Capillary Pressure; Reservoir Fluids; Phase Equilibrium; Phase Envelope.
Mutation particle swarm optimization of the BP-PID controller for piezoelectric ceramics
Zheng, Huaqing; Jiang, Minlan
2016-01-01
PID control is the most common used method in industrial control because its structure is simple and it is easy to implement. PID controller has good control effect, now it has been widely used. However, PID method has a few limitations. The overshoot of the PID controller is very big. The adjustment time is long. When the parameters of controlled plant are changing over time, the parameters of controller could hardly change automatically to adjust to changing environment. Thus, it can't meet the demand of control quality in the process of controlling piezoelectric ceramic. In order to effectively control the piezoelectric ceramic and improve the control accuracy, this paper replaced the learning algorithm of the BP with the mutation particle swarm optimization algorithm(MPSO) on the process of the parameters setting of BP-PID. That designed a better self-adaptive controller which is combing the BP neural network based on mutation particle swarm optimization with the conventional PID control theory. This combination is called the MPSO-BP-PID. In the mechanism of the MPSO, the mutation operation is carried out with the fitness variance and the global best fitness value as the standard. That can overcome the precocious of the PSO and strengthen its global search ability. As a result, the MPSO-BP-PID can complete controlling the controlled plant with higher speed and accuracy. Therefore, the MPSO-BP-PID is applied to the piezoelectric ceramic. It can effectively overcome the hysteresis, nonlinearity of the piezoelectric ceramic. In the experiment, compared with BP-PID and PSO-BP-PID, it proved that MPSO is effective and the MPSO-BP-PID has stronger adaptability and robustness.
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
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Mohammad Naebi
2016-01-01
Full Text Available Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%.
An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel
Muthukumar, V.; Suresh Babu, A.; Venkatasamy, R.; Senthil Kumar, N.
2015-01-01
This study employed Accelerated Particle Swarm Optimization (APSO) algorithm to optimize the machining parameters that lead to a maximum Material Removal Rate (MRR), minimum surface roughness and minimum kerf width values for Wire Electrical Discharge Machining (WEDM) of AISI D3 die-steel. Four machining parameters that are optimized using APSO algorithm include Pulse on-time, Pulse off-time, Gap voltage, Wire feed. The machining parameters are evaluated by Taguchi's L9 Orthogonal Array (OA). Experiments are conducted on a CNC WEDM and output responses such as material removal rate, surface roughness and kerf width are determined. The empirical relationship between control factors and output responses are established by using linear regression models using Minitab software. Finally, APSO algorithm, a nature inspired metaheuristic technique, is used to optimize the WEDM machining parameters for higher material removal rate and lower kerf width with surface roughness as constraint. The confirmation experiments carried out with the optimum conditions show that the proposed algorithm was found to be potential in finding numerous optimal input machining parameters which can fulfill wide requirements of a process engineer working in WEDM industry.
Aranza, M. F.; Kustija, J.; Trisno, B.; Hakim, D. L.
2016-04-01
PID Controller (Proportional Integral Derivative) was invented since 1910, but till today still is used in industries, even though there are many kind of modern controllers like fuzz controller and neural network controller are being developed. Performance of PID controller is depend on on Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). These gains can be got by using method Ziegler-Nichols (ZN), gain-phase margin, Root Locus, Minimum Variance dan Gain Scheduling however these methods are not optimal to control systems that nonlinear and have high-orde, in addition, some methods relative hard. To solve those obstacles, particle swarm optimization (PSO) algorithm is proposed to get optimal Kp, Ki and Kd. PSO is proposed because PSO has convergent result and not require many iterations. On this research, PID controller is applied on AVR (Automatic Voltage Regulator). Based on result of analyzing transient, stability Root Locus and frequency response, performance of PID controller is better than Ziegler-Nichols.
Desmarais, Jacques K.; Spiteri, Raymond J.
2017-12-01
A parallelized implementation of the particle swarm optimization algorithm is developed. We use the optimization procedure to speed up a previously published algorithm for airborne electromagnetic data interpretation. This algorithm is the only parametrized automated procedure for extracting the three-dimensionally varying geometrical parameters of conductors embedded in a resistive environment, such as igneous and metamorphic terranes. When compared to the original algorithm, the new optimization procedure is faster by two orders of magnitude (factor of 100). Synthetic model tests show that for the chosen system architecture and objective function, the particle swarm optimization approach depends very weakly on the rate of communication of the processors. Optimal wall-clock times are obtained using three processors. The increased performance means that the algorithm can now easily be used for fast routine interpretation of airborne electromagnetic surveys consisting of several anomalies, as is displayed by a test on MEGATEM field data collected at the Chibougamau site, Québec.
A coordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization
DEFF Research Database (Denmark)
Xu, Lizhong; Yang, Guangya; Xu, Zhao
2013-01-01
. Particle swarm optimization (PSO) is employed to solve this model for the operation schedule to minimize the total operational cost of Microgrid by coordinating the CHP, electric heater, boiler and heat storage. The efficacy of the model and methodology is verified with different operation scenarios....
Agent based Particle Swarm Optimization for Load Frequency Control of Distribution Grid
DEFF Research Database (Denmark)
Cha, Seung-Tae; Saleem, Arshad; Wu, Qiuwei
2012-01-01
This paper presents a Particle Swarm Optimization (PSO) based on multi-agent controller. Real-time digital simulator (RTDS) is used for modelling the power system, while a PSO based multi-agent LFC algorithm is developed in JAVA for communicating with resource agents and determines the scenario...
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review.
Bonyadi, Mohammad Reza; Michalewicz, Zbigniew
2017-01-01
This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.
Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.
Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee
2014-10-01
Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.
An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection
Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail
2013-01-01
One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This
An improved swarm optimization for parameter estimation and biological model selection.
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Afnizanfaizal Abdullah
Full Text Available One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete
Santosa, B.; Siswanto, N.; Fiqihesa
2018-04-01
This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution
Design optimization of pin fin geometry using particle swarm optimization algorithm.
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Nawaf Hamadneh
Full Text Available Particle swarm optimization (PSO is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters.
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Wenhui Hou
2016-01-01
Full Text Available In order to extract the maximum power from PV system, the maximum power point tracking (MPPT technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP. In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions.
Lukemire, Joshua; Mandal, Abhyuday; Wong, Weng Kee
2016-01-01
Identifying optimal designs for generalized linear models with a binary response can be a challenging task, especially when there are both continuous and discrete independent factors in the model. Theoretical results rarely exist for such models, and the handful that do exist come with restrictive assumptions. This paper investigates the use of particle swarm optimization (PSO) to search for locally $D$-optimal designs for generalized linear models with discrete and continuous factors and a b...
Energy Technology Data Exchange (ETDEWEB)
Meneses, Anderson Alvarenga de Moura; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear]. E-mail: ameneses@con.ufrj.br; schirru@lmp.ufrj.br
2005-07-01
This work focuses on the usage the Artificial Intelligence technique Particle Swarm Optimization (PSO) to optimize the fuel recharge at a nuclear reactor. This is a combinatorial problem, in which the search of the best feasible solution is done by minimizing a specific objective function. However, in this first moment it is possible to compare the fuel recharge problem with the Traveling Salesman Problem (TSP), since both of them are combinatorial, with one advantage: the evaluation of the TSP objective function is much more simple. Thus, the proposed methods have been applied to two TSPs: Oliver 30 and Rykel 48. In 1995, KENNEDY and EBERHART presented the PSO technique to optimize non-linear continued functions. Recently some PSO models for discrete search spaces have been developed for combinatorial optimization. Although all of them having different formulation from the ones presented here. In this paper, we use the PSO theory associated with to the Random Keys (RK)model, used in some optimizations with Genetic Algorithms. The Particle Swarm Optimization with Random Keys (PSORK) results from this association, which combines PSO and RK. The adaptations and changings in the PSO aim to allow the usage of the PSO at the nuclear fuel recharge. This work shows the PSORK being applied to the proposed combinatorial problem and the obtained results. (author)
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Ying-Yi Hong
2014-01-01
Full Text Available The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.
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Yang Liu
2016-01-01
Full Text Available This paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV path planning with particle swarm optimization (PSO technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samples t-test is made and an index called straight line rate (SLR is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques.
Power and time slot allocation in cognitive relay networks using particle swarm optimization.
Derakhshan-Barjoei, Pouya; Dadashzadeh, Gholamreza; Razzazi, Farbod; Razavizadeh, S Mohammad
2013-01-01
The two main problems in cognitive radio networks are power and time slot allocation problems which require a precise analysis and guarantee the quality of service in both the primary and secondary users. In this paper, these two problems are considered and a method is proposed to solve the resulting optimization problem. Our proposed method provides an improved performance in solving the constrained nonlinear multiobject optimization for the power control and beamforming in order to reach the maximum capacity and proper adaption of time slots, and as a result a new scheme for joint power and time slot allocation in cognitive relay networks is proposed. We adopt space diversity access as the secondary users access scheme and divide the time between multiple secondary users according to their contribution to primary user's transmission. Helping primary users provides more opportunities for secondary users to access the channel since the primary users can release the channel sooner. In contrast, primary network leases portion of channel access time to the secondary users for their transmission using particle swarm optimization (PSO). Numerical studies show good performance of the proposed scheme with a dynamic cost function in a nonstationary environment.
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I. Sharma
2016-09-01
Full Text Available In this paper, a linear phase FIR filter is designed through recently proposed nature inspired optimization algorithm known as Cuckoo search (CS. A comparative study of Cuckoo search (CS, particle swarm optimization (PSO and artificial bee colony (ABC nature inspired optimization methods in the field of linear phase FIR filter design is also presented. For this purpose, an improved L1 weighted error function is formulated in frequency domain, and minimized through CS, PSO and ABC respectively. The error or objective function has a controlling parameter wt which controls the amount of ripple in the desired band of frequency. The performance of FIR filter is examined through three key parameters; Maximum Pass Band Ripple (MPR, Maximum Stopband Ripple (MSR and Stopband Attenuation (As. Comparative study and the simulation results reveal that the designed filter with CS gives better performance in terms of Maximum Stopband Ripple (MSR, and Stopband Attenuation (As for low order filter design, and for higher order it also gives better performance in term of Maximum Passband Ripple (MPR. Superiority of the proposed technique is also shown through comparison with other recently proposed methods.
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Fereydoun Naghibi
2016-12-01
Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was
Joint Optimization of Microstrip Patch Antennas Using Particle Swarm Optimization for UWB Systems
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Muhammad Zubair
2013-01-01
Full Text Available Ultra wideband (UWB systems are the most appropriate for high data rate wireless transmission with low power consumption. However, the antenna design for UWB has been a challenging task. Moreover, it is always desirable to have more freedom by designing different shape antennas with identical characteristics so that they can be used in either transmitter or receiver depending on other physical constraints such as area. To tackle these issues, in this paper, we have investigated a joint optimization of three different shape-printed monopole antennas, namely, printed square monopole antenna, printed circular monopole antenna and printed hexagonal monopole antenna, for UWB applications. More specifically, we have obtained the optimized geometrical parameters of these antennas by minimizing the mean-square-error for desired lower band edge frequency, quality factor, and bandwidth. The objective of joint optimization is to have identical frequency characteristics for the aforementioned three types of PMA which will give a freedom to interchangeably use them at either side, transmitting or receiving. Moreover, we employ particle swarm optimization (PSO algorithm for our problem as it is well known in the literature that PSO performs well in electromagnetic and antenna applications. Simulation results are presented to show the performance of the proposed design.
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Hongjin Wang
2015-09-01
Full Text Available To efficiently recover the waste heat from a diesel engine exhaust, a regenerative organic Rankine cycle (RORC system was employed, and butane, R124, R416A, and R134a were used as the working fluids. The resulting diesel engine-RORC combined system was defined and the relevant evaluation indexes were proposed. First, the variation tendency of the exhaust energy rate under various diesel engine operating conditions was analyzed using experimental data. The thermodynamic model of the RORC system was established based on the first and second laws of thermodynamics, and the net power output and exergy destruction rate of the RORC system were selected as the objective functions. A particle swarm optimization (PSO algorithm was used to optimize the operating parameters of the RORC system, including evaporating pressure, intermediate pressure, and degree of superheat. The operating performances of the RORC system and diesel engine-RORC combined system were studied for the four selected working fluids under various operating conditions of the diesel engine. The results show that the operating performances of the RORC system and the combined system using butane are optimal on the basis of optimizing the operating parameters; when the engine speed is 2200 r/min and engine torque is 1215 N·m, the net power output of the RORC system using butane is 36.57 kW, and the power output increasing ratio (POIR of the combined system using butane is 11.56%.
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Aristeidis Antonakis
2017-04-01
Full Text Available In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.
Cui, Zhihua; Zhang, Yi
2014-02-01
As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed.
Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm
Cui, Xue; Gao, Jian; Feng, Yunbin; Zou, Chenlu; Liu, Huanlei
2018-01-01
In this paper, an optimization model with the minimum active power loss and minimum voltage deviation of node and maximum static voltage stability margin as the optimization objective is proposed for the reactive power optimization problems. By defining the index value of reactive power compensation, the optimal reactive power compensation node was selected. The particle swarm optimization algorithm was improved, and the selection pool of global optimal and the global optimal of probability (p-gbest) were introduced. A set of Pareto optimal solution sets is obtained by this algorithm. And by calculating the fuzzy membership value of the pareto optimal solution sets, individuals with the smallest fuzzy membership value were selected as the final optimization results. The above improved algorithm is used to optimize the reactive power of IEEE14 standard node system. Through the comparison and analysis of the results, it has been proven that the optimization effect of this algorithm was very good.
International Nuclear Information System (INIS)
Coelho, Leandro dos Santos; Mariani, Viviana Cocco
2009-01-01
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the chaotic systems theory, this paper proposed a novel chaotic PSO combined with an implicit filtering (IF) local search method to solve economic dispatch problems. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed PSO introduces chaos mapping using Henon map sequences which increases its convergence rate and resulting precision. The chaotic PSO approach is used to produce good potential solutions, and the IF is used to fine-tune of final solution of PSO. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results are promising and show the effectiveness of the proposed approach.
CALIBRATION OF SEMI-ANALYTIC MODELS OF GALAXY FORMATION USING PARTICLE SWARM OPTIMIZATION
International Nuclear Information System (INIS)
Ruiz, Andrés N.; Domínguez, Mariano J.; Yaryura, Yamila; Lambas, Diego García; Cora, Sofía A.; Martínez, Cristian A. Vega-; Gargiulo, Ignacio D.; Padilla, Nelson D.; Tecce, Tomás E.; Orsi, Álvaro; Arancibia, Alejandra M. Muñoz
2015-01-01
We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs
A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm
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Michala Jakubcová
2014-01-01
Full Text Available 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 equally large complexes according to the particle’s functional value (SCE-PSO, and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO. All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process.
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Ghouraf Djamel Eddine
2016-05-01
Full Text Available Power system stability considered a necessary condition for normal functioning of an electrical network. The role of regulation and control systems is to ensure that stability by determining the essential elements that influence it. This paper proposes a Particle Swarm Optimization (PSO based multiobjective function to tuning optimal parameters of Power System Stabilizer (PSS; this later is used as auxiliary to generator excitation system in order to damp electro mechanicals oscillations of the rotor and consequently improve Power system stability. The computer simulation results obtained by developed graphical user interface (GUI have proved the efficiency of PSS optimized by a Particle Swarm Optimization, in comparison with a conventional PSS, showing stable system responses almost insensitive to large parameter variations.Our present study was performed using a GUI realized under MATLAB in our work.
Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.
2018-03-01
Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.
Honey Bees Inspired Optimization Method: The Bees Algorithm
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Ernesto Mastrocinque
2013-11-01
Full Text Available Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.
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Reza Sirjani
2018-03-01
Full Text Available Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV solar farms can only produce active power during the day, while at night, they are completely idle. At the same time, though, active power should be supported by reactive power. Reactive power compensation in power systems improves power quality and stability. The use during the night of a PV solar farm inverter as a static synchronous compensator (or PV-STATCOM device has recently been proposed which can improve system performance and increase the utility of a PV solar farm. In this paper, a method for optimal PV-STATCOM placement and sizing is proposed using empirical data. Considering the objectives of power loss and cost minimization as well as voltage improvement, two sub-problems of placement and sizing, respectively, are solved by a power loss index and adaptive particle swarm optimization (APSO. Test results show that APSO not only performs better in finding optimal solutions but also converges faster compared with bee colony optimization (BCO and lightening search algorithm (LSA. Installation of a PV solar farm, STATCOM, and PV-STATCOM in a system are each evaluated in terms of efficiency and cost.
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Bruno Avila Leal de Meirelles Herrera
2015-12-01
Full Text Available ABSTRACT The Traveling Salesman Problem (TSP is one of the most well-known and studied problems of Operations Research field, more specifically, in the Combinatorial Optimization field. As the TSP is a NP (Non-Deterministic Polynomial time-hard problem, there are several heuristic methods which have been proposed for the past decades in the attempt to solve it the best possible way. The aim of this work is to introduce and to evaluate the performance of some approaches for achieving optimal solution considering some symmetrical and asymmetrical TSP instances, which were taken from the Traveling Salesman Problem Library (TSPLIB. The analyzed approaches were divided into three methods: (i Lin-Kernighan-Helsgaun (LKH algorithm; (ii LKH with initial tour based on uniform distribution; and (iii an hybrid proposal combining Particle Swarm Optimization (PSO with quantum inspired behavior and LKH for local search procedure. The tested algorithms presented promising results in terms of computational cost and solution quality.
Ghouraf Djamel Eddine
2016-01-01
Power system stability considered a necessary condition for normal functioning of an electrical network. The role of regulation and control systems is to ensure that stability by determining the essential elements that influence it. This paper proposes a Particle Swarm Optimization (PSO) based multiobjective function to tuning optimal parameters of Power System Stabilizer (PSS); this later is used as auxiliary to generator excitation system in order to damp electro mechanicals oscillations of...
International Nuclear Information System (INIS)
Meneses, Anderson Alvarenga de Moura; Schirru, Roberto
2005-01-01
This work focuses on the usage the Artificial Intelligence technique Particle Swarm Optimization (PSO) to optimize the fuel recharge at a nuclear reactor. This is a combinatorial problem, in which the search of the best feasible solution is done by minimizing a specific objective function. However, in this first moment it is possible to compare the fuel recharge problem with the Traveling Salesman Problem (TSP), since both of them are combinatorial, with one advantage: the evaluation of the TSP objective function is much more simple. Thus, the proposed methods have been applied to two TSPs: Oliver 30 and Rykel 48. In 1995, KENNEDY and EBERHART presented the PSO technique to optimize non-linear continued functions. Recently some PSO models for discrete search spaces have been developed for combinatorial optimization. Although all of them having different formulation from the ones presented here. In this paper, we use the PSO theory associated with to the Random Keys (RK)model, used in some optimizations with Genetic Algorithms. The Particle Swarm Optimization with Random Keys (PSORK) results from this association, which combines PSO and RK. The adaptations and changings in the PSO aim to allow the usage of the PSO at the nuclear fuel recharge. This work shows the PSORK being applied to the proposed combinatorial problem and the obtained results. (author)
International Nuclear Information System (INIS)
Lian, Jijian; He, Longjun; Ma, Bin; Peng, Wenxiang; Li, Huokun
2013-01-01
Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybrid algorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures. (paper)
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Cancan Yi
2016-01-01
Full Text Available Variational mode decomposition (VMD is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.
Gerist, Saleheh; Maheri, Mahmoud R.
2016-12-01
In order to solve structural damage detection problem, a multi-stage method using particle swarm optimization is presented. First, a new spars recovery method, named Basis Pursuit (BP), is utilized to preliminarily identify structural damage locations. The BP method solves a system of equations which relates the damage parameters to the structural modal responses using the sensitivity matrix. Then, the results of this stage are subsequently enhanced to the exact damage locations and extents using the PSO search engine. Finally, the search space is reduced by elimination of some low damage variables using micro search (MS) operator embedded in the PSO algorithm. To overcome the noise present in structural responses, a method known as Basis Pursuit De-Noising (BPDN) is also used. The efficiency of the proposed method is investigated by three numerical examples: a cantilever beam, a plane truss and a portal plane frame. The frequency response is used to detect damage in the examples. The simulation results demonstrate the accuracy and efficiency of the proposed method in detecting multiple damage cases and exhibit its robustness regarding noise and its advantages compared to other reported solution algorithms.
Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search
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Simon Fong
2013-01-01
Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.
A novel neutron energy spectrum unfolding code using particle swarm optimization
Shahabinejad, H.; Sohrabpour, M.
2017-07-01
A novel neutron Spectrum Deconvolution using Particle Swarm Optimization (SDPSO) code has been developed to unfold the neutron spectrum from a pulse height distribution and a response matrix. The Particle Swarm Optimization (PSO) imitates the bird flocks social behavior to solve complex optimization problems. The results of the SDPSO code have been compared with those of the standard spectra and recently published Two-steps Genetic Algorithm Spectrum Unfolding (TGASU) code. The TGASU code have been previously compared with the other codes such as MAXED, GRAVEL, FERDOR and GAMCD and shown to be more accurate than the previous codes. The results of the SDPSO code have been demonstrated to match well with those of the TGASU code for both under determined and over-determined problems. In addition the SDPSO has been shown to be nearly two times faster than the TGASU code.
Multi-objective particle swarm optimization using Pareto-based set and aggregation approach
Huang, Song; Wang, Yan; Ji, Zhicheng
2017-07-01
Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs.
UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization
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Yudong Zhang
2013-01-01
Full Text Available Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.
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Babak Salamat
2017-05-01
Full Text Available The aim of this paper is to provide a realistic stochastic trajectory generation method for unmanned aerial vehicles that offers a tool for the emulation of trajectories in typical flight scenarios. Three scenarios are defined in this paper. The trajectories for these scenarios are implemented with quintic B-splines that grant smoothness in the second-order derivatives of Euler angles and accelerations. In order to tune the parameters of the quintic B-spline in the search space, a multi-objective optimization method called particle swarm optimization (PSO is used. The proposed technique satisfies the constraints imposed by the configuration of the unmanned aerial vehicle (UAV. Further particular constraints can be introduced such as: obstacle avoidance, speed limitation, and actuator torque limitations due to the practical feasibility of the trajectories. Finally, the standard rapidly-exploring random tree (RRT* algorithm, the standard (A* algorithm and the genetic algorithm (GA are simulated to make a comparison with the proposed algorithm in terms of execution time and effectiveness in finding the minimum length trajectory.
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A. K. M. Foysal Ahmed
2018-03-01
Full Text Available The classical capacitated vehicle routing problem (CVRP is a very popular combinatorial optimization problem in the field of logistics and supply chain management. Although CVRP has drawn interests of many researchers, no standard way has been established yet to obtain best known solutions for all the different problem sets. We propose an efficient algorithm Bilayer Local Search-based Particle Swarm Optimization (BLS-PSO along with a novel decoding method to solve CVRP. Decoding method is important to relate the encoded particle position to a feasible CVRP solution. In bilayer local search, one layer of local search is for the whole population in any iteration whereas another one is applied only on the pool of the best particles generated in different generations. Such searching strategies help the BLS-PSO to perform better than the existing proposals by obtaining best known solutions for most of the existing benchmark problems within very reasonable computational time. Computational results also show that the performance achieved by the proposed algorithm outperforms other PSO-based approaches.
Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)
Wang, Mingming; Luo, Jianjun; Walter, Ulrich
2015-07-01
This paper investigates the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in free-floating mode. Due to the path dependent dynamic singularities, the volume of available workspace of the space robot is limited and enormous joint velocities are required when such singularities are met. In order to overcome this effect, the direct kinematics equations in conjunction with PSO are employed for trajectory planning of free-floating space robot. The joint trajectories are parametrized with the Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) redundant manipulator mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.
Sue-Ann, Goh; Ponnambalam, S. G.
This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.
Particle Swarm Optimization with Power-Law Parameter Based on the Cross-Border Reset Mechanism
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WANG, H.
2017-11-01
Full Text Available In order to improve the performance of traditional particle swarm optimization, this paper introduces the principle of Levy flight and cross-border reset mechanism. In the proposed particle swarm optimization, the dynamic variation of parameters meets the power-law distribution and the pattern of particles transition conforms to the Levy flight in the process of algorithm optimization. It means the particles make long distance movements in the search space with a small probability and make short distance movements with a large probability. Therefore, the particles can jump out of local optimum more easily and coordinate the global search and local search of particle swarm optimization. This paper also designs the cross-border reset mechanism to make particles regain optimization ability when stranding on the border of search space after a long distance movement. The simulation results demonstrate the proposed algorithms are easier to jump out of local optimum and have higher accuracy when compared with the existing similar algorithms based on benchmark test functions and handwriting character recognition system.
International Nuclear Information System (INIS)
Coban, Ramazan
2011-01-01
Research highlights: → A closed-loop fuzzy logic controller based on the particle swarm optimization algorithm was proposed for controlling the power level of nuclear research reactors. → The proposed control system was tested for various initial and desired power levels, and it could control the reactor successfully for most situations. → The proposed controller is robust against the disturbances. - Abstract: In this paper, a closed-loop fuzzy logic controller based on the particle swarm optimization algorithm is proposed for controlling the power level of nuclear research reactors. The principle of the fuzzy logic controller is based on the rules constructed from numerical experiments made by means of a computer code for the core dynamics calculation and from human operator's experience and knowledge. In addition to these intuitive and experimental design efforts, consequent parts of the fuzzy rules are optimally (or near optimally) determined using the particle swarm optimization algorithm. The contribution of the proposed algorithm to a reactor control system is investigated in details. The performance of the controller is also tested with numerical simulations in numerous operating conditions from various initial power levels to desired power levels, as well as under disturbance. It is shown that the proposed control system performs satisfactorily under almost all operating conditions, even in the case of very small initial power levels.
International Nuclear Information System (INIS)
Shen, Peihong; Zhao, Zhiguo; Zhan, Xiaowen; Li, Jingwei
2017-01-01
In this paper, an energy management strategy based on logic threshold is proposed for a plug-in hybrid electric vehicle. The plug-in hybrid electric vehicle powertrain model is established using MATLAB/Simulink based on experimental tests of the power components, which is validated by the comparison with the verified simulation model which is built in the AVL Cruise. The influence of the driving torque demand decision on the fuel economy of plug-in hybrid electric vehicle is studied using a simulation. The optimization method for the driving torque demand decision, which refers to the relationship between the accelerator pedal opening and driving torque demand, from the perspective of fuel economy is formulated. The dynamically changing inertia weight particle swarm optimization is used to optimize the decision parameters. The simulation results show that the optimized driving torque demand decision can improve the PHEV fuel economy by 15.8% and 14.5% in the fuel economy test driving cycle of new European driving cycle and worldwide harmonized light vehicles test respectively, using the same rule-based energy management strategy. The proposed optimization method provides a theoretical guide for calibrating the parameters of driving torque demand decision to improve the fuel economy of the real plug-in hybrid electric vehicle. - Highlights: • The influence of the driving torque demand decision on the fuel economy is studied. • The optimization method for the driving torque demand decision is formulated. • An improved particle swarm optimization is utilized to optimize the parameters. • Fuel economy is improved by using the optimized driving torque demand decision.
Approach to analytically minimize the LCD moiré by image-based particle swarm optimization.
Tsai, Yu-Lin; Tien, Chung-Hao
2015-10-01
In this paper, we proposed a methodology to optimize the parametric window of a liquid crystal display (LCD) system, whose visual performance was deteriorated by the pixel moiré arising in between multiple periodic structures. Conventional analysis and minimization of moiré patterns are limited by few parameters. With the proposed image-based particle swarm optimization (PSO), we enable a multivariable optimization at the same time. A series of experiments was conducted to validate the methodology. Due to its versatility, the proposed technique will certainly have a promising impact on the fast optimization in LCD design with more complex configuration.
Impact of Chaos Functions on Modern Swarm Optimizers.
Emary, E; Zawbaa, Hossam M
2016-01-01
Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO), antlion optimizer (ALO) and moth-flame optimizer (MFO) in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.
Impact of Chaos Functions on Modern Swarm Optimizers.
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E Emary
Full Text Available Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO, antlion optimizer (ALO and moth-flame optimizer (MFO in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.
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Wenliao Du
2013-01-01
Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.
Guo, Weian; Si, Chengyong; Xue, Yu; Mao, Yanfen; Wang, Lei; Wu, Qidi
2017-05-04
Particle Swarm Optimization (PSO) is a popular algorithm which is widely investigated and well implemented in many areas. However, the canonical PSO does not perform well in population diversity maintenance so that usually leads to a premature convergence or local optima. To address this issue, we propose a variant of PSO named Grouping PSO with Personal- Best-Position (Pbest) Guidance (GPSO-PG) which maintains the population diversity by preserving the diversity of exemplars. On one hand, we adopt uniform random allocation strategy to assign particles into different groups and in each group the losers will learn from the winner. On the other hand, we employ personal historical best position of each particle in social learning rather than the current global best particle. In this way, the exemplars diversity increases and the effect from the global best particle is eliminated. We test the proposed algorithm to the benchmarks in CEC 2008 and CEC 2010, which concern the large scale optimization problems (LSOPs). By comparing several current peer algorithms, GPSO-PG exhibits a competitive performance to maintain population diversity and obtains a satisfactory performance to the problems.
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Alejandro Gonzalez
2014-01-01
Full Text Available Brain-machine interfaces (BMI rely on the accurate classification of event-related potentials (ERPs and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.
A swarm optimized neural network system for classification of microcalcification in mammograms.
Dheeba, J; Selvi, S Tamil
2012-10-01
Early detection of microcalcification clusters in breast tissue will significantly increase the survival rate of the patients. Radiologists use mammography for breast cancer diagnosis at early stage. It is a very challenging and difficult task for radiologists to correctly classify the abnormal regions in the breast tissue, because mammograms are noisy images. To improve the accuracy rate of detection of breast cancer, a novel intelligent computer aided classifier is used, which detects the presence of microcalcification clusters. In this paper, an innovative approach for detection of microcalcification in digital mammograms using Swarm Optimization Neural Network (SONN) is used. Prior to classification Laws texture features are extracted from the image to capture descriptive texture information. These features are used to extract texture energy measures from the Region of Interest (ROI) containing microcalcification (MC). A feedforward neural network is used for detection of abnormal regions in breast tissue is optimally designed using Particle Swarm Optimization algorithm. The proposed intelligent classifier is evaluated based on the MIAS database where 51 malignant, 63 benign and 208 normal images are utilized. The approach has also been tested on 216 real time clinical images having abnormalities which showed that the results are statistically significant. With the proposed methodology, the area under the ROC curve (A ( z )) reached 0.9761 for MIAS database and 0.9138 for real clinical images. The classification results prove that the proposed swarm optimally tuned neural network highly contribute to computer-aided diagnosis of breast cancer.
The Contribution of Particle Swarm Optimization to Three-Dimensional Slope Stability Analysis
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Roohollah Kalatehjari
2014-01-01
Full Text Available Over the last few years, particle swarm optimization (PSO has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D slope stability analysis. This paper applied PSO in three-dimensional (3D slope stability problem to determine the critical slip surface (CSS of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes.
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Gandhimathi Amirthalingam
2016-10-01
Full Text Available An effective fusion method for combining information from single modality system requires Multimodal biometric crypto system. Fuzzy vault has been widely used for providing security, but the disadvantage is that the biometric data are easily visible and chaff points generated randomly can be easily found, so that there is a chance for the data to be hacked by the attackers. In order to improve the security by hiding the secret key within the biometric data, a new chaff point based fuzzy vault is proposed. For the generation of the secret key in the fuzzy vault, grouped feature vectors are generated by combining the extracted shape and texture feature vectors with the new chaff point feature vectors. With the help of the locations of the extracted feature vector points, x and y co-ordinate chaff matrixes are generated. New chaff points can be made, by picking best locations from the feature vectors. The optimal locations are found out by using particle swarm optimization (PSO algorithm. In PSO, extracted feature locations are considered particles and from these locations, best location for generating the chaff feature point is selected based on the fitness value. The experimentation of the proposed work is done on Yale face and IIT Delhi ear databases and its performance are evaluated using the measures such as Jaccard coefficient (JC, Genuine Acceptance Rate (GAR, False Matching Rate (FMR, Dice Coefficient (DC and False Non Matching Rate (FNMR. The results of the implementation give better recognition of person by facilitating 90% recognition result.
Verma, Harish Kumar; Pal, Sandeep
2016-06-01
The main objective of an image enhancement is to improve eminence by maximizing the information content in the test image. Conventional contrast enhancement techniques either often fails to produce reasonable results for a broad variety of low-contrast and high contrast images, or cannot be automatically applied to different images, because they are parameters dependent. Hence this paper introduces a novel hybrid image enhancement approach by taking both the local and global information of an image. In the present work, sigmoid function is being modified on the basis of contrast of the images. The gray image enhancement problem is treated as nonlinear optimization problem with several constraints and solved by particle swarm optimization. The entropy and edge information is included in the objective function as quality measure of an image. The effectiveness of modified sigmoid function based enhancement over conventional methods namely linear contrast stretching, histogram equalization, and adaptive histogram equalization are better revealed by the enhanced images and further validated by statistical analysis of these images.
Cheung, Ngaam J; Shen, Hong-Bin
2014-11-01
The stable conformation of a molecule is greatly important to uncover the secret of its properties and functions. Generally, the conformation of a molecule will be the most stable when it is of the minimum potential energy. Accordingly, the determination of the conformation can be solved in the optimization framework. It is, however, not an easy task to achieve the only conformation with the lowest energy among all the potential ones because of the high complexity of the energy landscape and the exponential computation increasing with molecular size. In this paper, we develop a hierarchical and heterogeneous particle swarm optimizer (HHPSO) to deal with the problem in the minimization of the potential energy. The proposed method is evaluated over a scalable simplified molecular potential energy function with up to 200 degrees of freedom and a realistic energy function of pseudo-ethane molecule. The experimental results are compared with other six PSO variants and four genetic algorithms. The results show HHPSO is significantly better than the compared PSOs with p-value less than 0.01277 over molecular potential energy function. Copyright © 2014 Elsevier Inc. All rights reserved.
Tuan, Pham Viet; Koo, Insoo
2017-10-06
In this paper, we consider multiuser simultaneous wireless information and power transfer (SWIPT) for cognitive radio systems where a secondary transmitter (ST) with an antenna array provides information and energy to multiple single-antenna secondary receivers (SRs) equipped with a power splitting (PS) receiving scheme when multiple primary users (PUs) exist. The main objective of the paper is to maximize weighted sum harvested energy for SRs while satisfying their minimum required signal-to-interference-plus-noise ratio (SINR), the limited transmission power at the ST, and the interference threshold of each PU. For the perfect channel state information (CSI), the optimal beamforming vectors and PS ratios are achieved by the proposed PSO-SDR in which semidefinite relaxation (SDR) and particle swarm optimization (PSO) methods are jointly combined. We prove that SDR always has a rank-1 solution, and is indeed tight. For the imperfect CSI with bounded channel vector errors, the upper bound of weighted sum harvested energy (WSHE) is also obtained through the S-Procedure. Finally, simulation results demonstrate that the proposed PSO-SDR has fast convergence and better performance as compared to the other baseline schemes.
The Contribution of Particle Swarm Optimization to Three-Dimensional Slope Stability Analysis
A Rashid, Ahmad Safuan; Ali, Nazri
2014-01-01
Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes. PMID:24991652
Li, Duan; Xu, Lijun; Li, Xiaolu
2017-04-01
To measure the distances and properties of the objects within a laser footprint, a decomposition method for full-waveform light detection and ranging (LiDAR) echoes is proposed. In this method, firstly, wavelet decomposition is used to filter the noise and estimate the noise level in a full-waveform echo. Secondly, peak and inflection points of the filtered full-waveform echo are used to detect the echo components in the filtered full-waveform echo. Lastly, particle swarm optimization (PSO) is used to remove the noise-caused echo components and optimize the parameters of the most probable echo components. Simulation results show that the wavelet-decomposition-based filter is of the best improvement of SNR and decomposition success rates than Wiener and Gaussian smoothing filters. In addition, the noise level estimated using wavelet-decomposition-based filter is more accurate than those estimated using other two commonly used methods. Experiments were carried out to evaluate the proposed method that was compared with our previous method (called GS-LM for short). In experiments, a lab-build full-waveform LiDAR system was utilized to provide eight types of full-waveform echoes scattered from three objects at different distances. Experimental results show that the proposed method has higher success rates for decomposition of full-waveform echoes and more accurate parameters estimation for echo components than those of GS-LM. The proposed method based on wavelet decomposition and PSO is valid to decompose the more complicated full-waveform echoes for estimating the multi-level distances of the objects and measuring the properties of the objects in a laser footprint.
Directory of Open Access Journals (Sweden)
S. Asghar Gholamian
2012-12-01
Full Text Available Permanent magnet synchronous motors are efficient motors which have widespread applications in electric industry due to their noticeable features. One of the interesting applications of such motors is in underwater vehicles. In these cases, reaching to minimum volume and high torque of the motor are the major concern. Design optimization can enhance their merits considerably, thus reduce volume and improve performance of motors. In this paper, a new method for optimum design of a five-phase surface-mounted permanent magnet synchronous motor is presented to achieve minimum loss and magnet volume with an increased torque. A multi-objective optimization is performed in search for optimum dimensions of the motor and its permanent magnets using particle swarm optimization. The design optimization results in a motor with great improvement regarding the original motor. Finally, finite element analysis is utilized to validate the accuracy of the design.
DEFF Research Database (Denmark)
Ren, Jingzheng; Tan, Shiyu; Dong, Lichun
2010-01-01
the searching ability of basic particle swarm algorithm significantly. An example of utilizing the improved algorithm to solve the mathematical model was demonstrated; the result showed that it is efficient and convenient to optimize the reflux ratio for a distillation column by using the mathematical model......A mathematical model relating operation profits with reflux ratio of a stage distillation column was established. In order to optimize the reflux ratio by solving the nonlinear objective function, an improved particle swarm algorithm was developed and has been proved to be able to enhance...... and improved particle swarm algorithm....
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Mohammad Ali Ahmadi
2015-06-01
In this work, novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network (ANN linked to the particle swarm optimization (PSO tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions. It was found that there is very good match between the modeling output and the real data taken from the literature, so that a very low average absolute error percentage is attained (e.g., <0.82%. The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.
Wang, Ji; Zhang, Ru; Yan, Yuting; Dong, Xiaoqiang; Li, Jun Ming
2017-05-01
Hazardous gas leaks in the atmosphere can cause significant economic losses in addition to environmental hazards, such as fires and explosions. A three-stage hazardous gas leak source localization method was developed that uses movable and stationary gas concentration sensors. The method calculates a preliminary source inversion with a modified genetic algorithm (MGA) and has the potential to crossover with eliminated individuals from the population, following the selection of the best candidate. The method then determines a search zone using Markov Chain Monte Carlo (MCMC) sampling, utilizing a partial evaluation strategy. The leak source is then accurately localized using a modified guaranteed convergence particle swarm optimization algorithm with several bad-performing individuals, following selection of the most successful individual with dynamic updates. The first two stages are based on data collected by motionless sensors, and the last stage is based on data from movable robots with sensors. The measurement error adaptability and the effect of the leak source location were analyzed. The test results showed that this three-stage localization process can localize a leak source within 1.0 m of the source for different leak source locations, with measurement error standard deviation smaller than 2.0.
Welding Diagnostics by Means of Particle Swarm Optimization and Feature Selection
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J. Mirapeix
2012-01-01
Full Text Available In a previous contribution, a welding diagnostics approach based on plasma optical spectroscopy was presented. It consisted of the employment of optimization algorithms and synthetic spectra to obtain the participation profiles of the species participating in the plasma. A modification of the model is discussed here: on the one hand the controlled random search algorithm has been substituted by a particle swarm optimization implementation. On the other hand a feature selection stage has been included to determine those spectral windows where the optimization process will take place. Both experimental and field tests will be shown to illustrate the performance of the solution that improves the results of the previous work.
Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.
Huang, Shuqiang; Tao, Ming
2017-01-22
Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.
Jevtić, Aleksandar; Gutiérrez, Alvaro
2011-01-01
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed bees algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a genetic algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
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Álvaro Gutiérrez
2011-11-01
Full Text Available Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA, previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
Wang, Xingmei; Hao, Wenqian; Li, Qiming
2017-12-18
This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.
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Muhammad Imran
2014-01-01
Full Text Available One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF coupled with support vector machine (SVM has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO. The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.
International Nuclear Information System (INIS)
Banerjee, Amit; Abu-Mahfouz, Issam
2014-01-01
The use of evolutionary algorithms has been popular in recent years for solving the inverse problem of identifying system parameters given the chaotic response of a dynamical system. The inverse problem is reformulated as a minimization problem and population-based optimizers such as evolutionary algorithms have been shown to be efficient solvers of the minimization problem. However, to the best of our knowledge, there has been no published work that evaluates the efficacy of using the two most popular evolutionary techniques – particle swarm optimization and differential evolution algorithm, on a wide range of parameter estimation problems. In this paper, the two methods along with their variants (for a total of seven algorithms) are applied to fifteen different parameter estimation problems of varying degrees of complexity. Estimation results are analyzed using nonparametric statistical methods to identify if an algorithm is statistically superior to others over the class of problems analyzed. Results based on parameter estimation quality suggest that there are significant differences between the algorithms with the newer, more sophisticated algorithms performing better than their canonical versions. More importantly, significant differences were also found among variants of the particle swarm optimizer and the best performing differential evolution algorithm
Wang, Ershen; Jia, Chaoying; Tong, Gang; Qu, Pingping; Lan, Xiaoyu; Pang, Tao
2018-03-01
The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.
Particle swarm genetic algorithm and its application
International Nuclear Information System (INIS)
Liu Chengxiang; Yan Changxiang; Wang Jianjun; Liu Zhenhai
2012-01-01
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)
High-resolution microwave diagnostics of architectural components by particle swarm optimization
Genovesi, Simone; Salerno, Emanuele; Monorchio, Agostino; Manara, Giuliano
2010-05-01
the discretization grid used by the forward solver. The algorithm we chose to optimize the objective is based on the particle swarm paradigm. Each feasible solution is coded as a location in a multidimensional space, explored by a number of "particles" each moving with a certain velocity, which is partly random and partly induced by the experience of both the particle itself and the "swarm" of all the other particles. In our case, the search is complicated by the mixed continuous-binary nature of our unknowns, but the swarm intelligence approach maintains the advantage of its intrinsic parallelism. The experimental results we obtained from both simulated and real measurements show that, for typical permittivity values and radiation wavelengths, the spatial resolution is highly improved by the line process. From real measurements in the range 1.7-2.6 GHz, we accurately reconstructed the permittivity values of our test phantom and located the discontinuities within the limits imposed by our discretization grid (with 1.5 mm cell thickness). At present, the applicability of our reconstruction method is still limited by the forward solver, which is based on a cascaded transmission-line model that assumes normal and plane-wave incidence. We are developing a new solver based on a closed-form Green's function in multilayered media, which should enable us to model appropriately both the microwave sensor and the illumination geometry, thus improving the accuracy of the computed reflection coefficients in the objective functional.
Subbulakshmi, C V; Deepa, S N
2015-01-01
Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.
Self-modeling curve resolution (SMCR) by particle swarm optimization (PSO).
Shinzawa, Hideyuki; Jiang, Jian-Hui; Iwahashi, Makio; Noda, Isao; Ozaki, Yukihiro
2007-07-09
Particle swarm optimization (PSO) combined with alternating least squares (ALS) is introduced to self-modeling curve resolution (SMCR) in this study for effective initial estimate. The proposed method aims to search concentration profiles or pure spectra which give the best resolution result by PSO. SMCR sometimes yields insufficient resolution results by getting trapped in a local minimum with poor initial estimates. The proposed method enables to reduce an undesirable effect of the local minimum in SMCR due to the advantages of PSO. Moreover, a new criterion based on global phase angle is also proposed for more effective performance of SMCR. It takes full advantage of data structure, that is to say, a sequential change with respect to a perturbation can be considered in SMCR with the criterion. To demonstrate its potential, SMCR by PSO is applied to concentration-dependent near-infrared (NIR) spectra of mixture solutions of oleic acid (OA) and ethanol. Its curve resolution performances are compared with SMCR with evolving factor analysis (EFA). The results show that SMCR by PSO yields significantly better curve resolution performances than those by EFA. It is revealed that SMCR by PSO is less sensitive to a local minimum in SMCR and it can be a new effective tool for curve resolution analysis.
A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation
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Prasertsak Charoen
2017-04-01
Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.
Pashaei, Elnaz; Pashaei, Elham; Aydin, Nizamettin
2018-04-14
In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets. Copyright © 2018 Elsevier Inc. All rights reserved.
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C. V. Subbulakshmi
2015-01-01
Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.
Weerathunga, Thilina Shihan
2017-08-01
Gravitational waves are a fundamental prediction of Einstein's General Theory of Relativity. The first experimental proof of their existence was provided by the Nobel Prize winning discovery by Taylor and Hulse of orbital decay in a binary pulsar system. The first detection of gravitational waves incident on earth from an astrophysical source was announced in 2016 by the LIGO Scientific Collaboration, launching the new era of gravitational wave (GW) astronomy. The signal detected was from the merger of two black holes, which is an example of sources called Compact Binary Coalescences (CBCs). Data analysis strategies used in the search for CBC signals are derivatives of the Maximum-Likelihood (ML) method. The ML method applied to data from a network of geographically distributed GW detectors--called fully coherent network analysis--is currently the best approach for estimating source location and GW polarization waveforms. However, in the case of CBCs, especially for lower mass systems (O(1M solar masses)) such as double neutron star binaries, fully coherent network analysis is computationally expensive. The ML method requires locating the global maximum of the likelihood function over a nine dimensional parameter space, where the computation of the likelihood at each point requires correlations involving O(104) to O(106) samples between the data and the corresponding candidate signal waveform template. Approximations, such as semi-coherent coincidence searches, are currently used to circumvent the computational barrier but incur a concomitant loss in sensitivity. We explored the effectiveness of Particle Swarm Optimization (PSO), a well-known algorithm in the field of swarm intelligence, in addressing the fully coherent network analysis problem. As an example, we used a four-detector network consisting of the two LIGO detectors at Hanford and Livingston, Virgo and Kagra, all having initial LIGO noise power spectral densities, and show that PSO can locate the global
M. Dalavi, Amol; J. Pawar, Padmakar; P. Singh, Tejinder
2016-01-01
Tool travel and tool switch scheduling are two major issues in hole-making operations. It is necessary to find the optimal sequence of operations to reduce the total processing cost of hole-making operations. In this work therefore, an attempt is made to use both a recently developed particle swarm optimisation algorithm and a shuffled frog leaping algorithm demonstrating in this way an example of plastic injection mould. The exact value of the minimum total processing cost is obtained by con...
Lee, X. N.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.; Shazzuan, S.
2017-09-01
Plastic injection moulding is a popular manufacturing method not only it is reliable, but also efficient and cost saving. It able to produce plastic part with detailed features and complex geometry. However, defects in injection moulding process degrades the quality and aesthetic of the injection moulded product. The most common defect occur in the process is warpage. Inappropriate process parameter setting of injection moulding machine is one of the reason that leads to the occurrence of warpage. The aims of this study were to improve the quality of injection moulded part by investigating the optimal parameters in minimizing warpage using Response Surface Methodology (RSM) and Glowworm Swarm Optimization (GSO). Subsequent to this, the most significant parameter was identified and recommended parameters setting was compared with the optimized parameter setting using RSM and GSO. In this research, the mobile phone case was selected as case study. The mould temperature, melt temperature, packing pressure, packing time and cooling time were selected as variables whereas warpage in y-direction was selected as responses in this research. The simulation was carried out by using Autodesk Moldflow Insight 2012. In addition, the RSM was performed by using Design Expert 7.0 whereas the GSO was utilized by using MATLAB. The warpage in y direction recommended by RSM were reduced by 70 %. The warpages recommended by GSO were decreased by 61 % in y direction. The resulting warpages under optimal parameter setting by RSM and GSO were validated by simulation in AMI 2012. RSM performed better than GSO in solving warpage issue.
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Sen Zhang
2018-02-01
Full Text Available Gas utilization ratio (GUR is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs. Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN and the particle swarm algorithm (PSO to predict the GUR. The particle swarm algorithm (PSO is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.
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Ying-Yi Hong
2016-01-01
Full Text Available This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1′′ constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.
Zhang, Sen; Jiang, Haihe; Yin, Yixin; Xiao, Wendong; Zhao, Baoyong
2018-02-20
Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.
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Vanaja Gokul
2012-01-01
Full Text Available In distributed systems real time optimizations need to be performed dynamically for better utilization of the network resources. Real time optimizations can be performed effectively by using Cross Layer Optimization (CLO within the network operating system. This paper presents the performance evaluation of Cross Layer Optimization (CLO in comparison with the traditional approach of Single-Layer Optimization (SLO. In the parallel implementation of the approaches the experimental study carried out indicates that the CLO results in a significant improvement in network utilization when compared to SLO. A variant of the Particle Swarm Optimization technique that utilizes Digital Pheromones (PSODP for better performance has been used here. A significantly higher speed up in performance was observed from the parallel implementation of CLO that used PSODP on a cluster of nodes.
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
2012-09-01
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.
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Hao Yin
2014-01-01
Full Text Available For SLA-aware service composition problem (SSC, an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.
Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.
2018-03-01
Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
their ability for generalization, self-organization and self-learning (Fausett 1994). Amongst the neural networks, the .... solution to an optimization problem. The concept is derived from the motion of a flock of birds ..... Proceedings of IEEE International Conference on Neural Networks and. Brain, Beijing, China, pp. 1693– ...
APPLICATION OF A PARTICLE SWARM OPTIMIZATION IN AN ...
African Journals Online (AJOL)
3], and it can be used to solve many complex optimization problems, which are nonlinear, non-differentiable and multi-modal. The most prominent merit of PSO is its fast convergence speed. In addition, PSO algorithm can be realized simply for ...
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Huan Zhang
2017-01-01
Full Text Available For the problem of multiaircraft cooperative suppression interference array (MACSIA against the enemy air defense radar network in electronic warfare mission planning, firstly, the concept of route planning security zone is proposed and the solution to get the minimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security zone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function, and the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization algorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to maintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation, and the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained and verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle swarm optimization algorithm for solving the problem of MACSIA is feasible and effective.
Directory of Open Access Journals (Sweden)
Hanning Chen
2014-01-01
Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.
An adaptive quantum-behaved particle swarm optimization approach for license plate segmentation
Zhu, Rong; Zhuo, Junfei
2009-10-01
Automatic license plate segmentation plays an essential role in intelligent transportation systems. But it can be a challenging task when segmenting the vehicle images with poor quality in real-world applications. For segmenting license plates out of the vehicle images efficiently, a novel two-stage segmentation strategy that contains a rough localization stage and a fine segmentation stage is proposed in this paper. Firstly, during the rough localization stage, the texture characteristic of Chinese license plates is utilized to get candidate rectangle regions. The license plate region is then identified and extracted from these regions based on projection property and geometric information. In the fine segmentation stage, two enhancement algorithms are applied to improve image quality and reduce segmentation error. And then an adaptive threshold-based segmentation approach based on quantum-behaved particle swarm optimization is presented to deal with the threshold selection of distinguishing the constitution codes from the background in the obtained license plate region. The experiments of segmenting the vehicle images are illustrated to show that the proposed method can achieve an ideal segmentation result with less computational cost.
Thakral, Priyanka; Bakhshi, A. K.
2014-02-01
A proficient metaheuristic approach viz., particle swarm optimization coupled with negative factor counting technique and inverse iteration method has been employed for designing novel binary and ternary copolymers based on thiophene, pyrrole and furan skeletons. A comparative study of the electronic structures and conduction properties of neutral heterocyclic copolymers and their benzene substituted analogues is inferred using the band structure results derived from ab-initio Hartree-Fock crystal orbital calculations. The band gap value decreases as a result of substitution on the polymer backbone due to increased quinoid contributions which is expected to enhance the intrinsic conductivity of the resulting copolymers. In general, it has been found that HOMO energies have a more decisive influence than LUMO energies on the relative fraction of constituents of the respective low band gap copolymers. The trends in the electronic properties of the respective copolymers are also verified and discussed with the help of density of states. These results can help streamline scrupulous synthetic efforts providing a potent route for molecular engineering of sustainable and efficient electronic materials.
Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu
2016-08-15
Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.
Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model
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Lokesh Selvaraj
2014-01-01
Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.
Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization
Niu, Liyong; Zhang, Di
2015-01-01
Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly. PMID:26236770
Applying Particle Swarm Optimization for Solving Team Orienteering Problem with Time Windows
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The Jin Ai
2014-01-01
Full Text Available The Team Orienteering Problem With Time Windows (TOPTW is a transportation problem case that have a set of vertices with a score, service time, and the time windows, start and final at a depot location. A number of paths are constructed to maximize the total collected score by the vertices which is visited. Each vertice can be visited only once and the visit can only start during the time window of vertices. This paper proposes a Particle Swarm Optimization algorithm for solving the TOPTW, by defining a specific particle for representing the solution of TOPTW within the PSO algorithm and two alternatives, called PSO_TOPTW1 and PSO_TOPTW2, for translating the particle position to form the routes of the path. The performance of the proposed PSO algorithm is evaluated through some benchmark data problem available in the literature. The computational results show that the proposed PSO is able to produce sufficiently good TOPTW solutions that are comparable with corresponding solutions from other existing methods for solving the TOPTW.
Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence
Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd
2018-04-01
Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.
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Xiao Yang
2017-11-01
Full Text Available The dynamic characteristics of power batteries directly affect the performance of electric vehicles, and the mathematical model is the basis for the design of a battery management system (BMS.Based on the electrode-averaged model of a lithium-ion battery, in view of the solid phase lithium-ion diffusion equation, the electrochemical model is simplified through the finite difference method. By analyzing the characteristics of the model and the type of parameters, the solid state diffusion kinetics are separated, and then the cascade parameter identifications are implemented with Particle Swarm Optimization. Eventually, the validity of the electrochemical model and the accuracy of model parameters are verified through 0.2–2 C multi-rates battery discharge tests of cell and road simulation tests of a micro pure electric vehicle under New European Driving Cycle (NEDC conditions. The results show that the estimated parameters can guarantee the output accuracy. In the test of cell, the voltage deviation of discharge is generally less than 0.1 V except the end; in road simulation test, the output is close to the actual value at low speed with the error around ±0.03 V, and at high speed around ±0.08 V.
A Binary Cat Swarm Optimization Algorithm for the Non-Unicost Set Covering Problem
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Broderick Crawford
2015-01-01
Full Text Available The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library.
Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments
Energy Technology Data Exchange (ETDEWEB)
Kurt Derr; Milos Manic
2009-05-01
Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.
International Nuclear Information System (INIS)
Li Yongjie; Yao Dezhong; Yao, Jonathan; Chen Wufan
2005-01-01
Automatic beam angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated
Buyuk, Ersin; Karaman, Abdullah
2017-04-01
We estimated transmissivity and storage coefficient values from the single well water-level measurements positioned ahead of the mining face by using particle swarm optimization (PSO) technique. The water-level response to the advancing mining face contains an semi-analytical function that is not suitable for conventional inversion shemes because the partial derivative is difficult to calculate . Morever, the logaritmic behaviour of the model create difficulty for obtaining an initial model that may lead to a stable convergence. The PSO appears to obtain a reliable solution that produce a reasonable fit between water-level data and model function response. Optimization methods have been used to find optimum conditions consisting either minimum or maximum of a given objective function with regard to some criteria. Unlike PSO, traditional non-linear optimization methods have been used for many hydrogeologic and geophysical engineering problems. These methods indicate some difficulties such as dependencies to initial model, evolution of the partial derivatives that is required while linearizing the model and trapping at local optimum. Recently, Particle swarm optimization (PSO) became the focus of modern global optimization method that is inspired from the social behaviour of birds of swarms, and appears to be a reliable and powerful algorithms for complex engineering applications. PSO that is not dependent on an initial model, and non-derivative stochastic process appears to be capable of searching all possible solutions in the model space either around local or global optimum points.
Performance Review of Harmony Search, Differential Evolution and Particle Swarm Optimization
Mohan Pandey, Hari
2017-08-01
Metaheuristic algorithms are effective in the design of an intelligent system. These algorithms are widely applied to solve complex optimization problems, including image processing, big data analytics, language processing, pattern recognition and others. This paper presents a performance comparison of three meta-heuristic algorithms, namely Harmony Search, Differential Evolution, and Particle Swarm Optimization. These algorithms are originated altogether from different fields of meta-heuristics yet share a common objective. The standard benchmark functions are used for the simulation. Statistical tests are conducted to derive a conclusion on the performance. The key motivation to conduct this research is to categorize the computational capabilities, which might be useful to the researchers.
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Ying-Yi Hong
2014-01-01
Full Text Available Particle swarm optimization (PSO has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1 appending the mean search to the original approach and (2 pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method.
Particle swarm optimization for determining shortest distance to voltage collapse
Energy Technology Data Exchange (ETDEWEB)
Arya, L.D.; Choube, S.C. [Electrical Engineering Department, S.G.S.I.T.S. Indore, MP 452 003 (India); Shrivastava, M. [Electrical Engineering Department, Government Engineering College Ujjain, MP 456 010 (India); Kothari, D.P. [Centre for Energy Studies, Indian Institute of Technology, Delhi (India)
2007-12-15
This paper describes an algorithm for computing shortest distance to voltage collapse or determination of CSNBP using PSO technique. A direction along CSNBP gives conservative results from voltage security view point. This information is useful to the operator to steer the system away from this point by taking corrective actions. The distance to a closest bifurcation is a minimum of the loadability given a slack bus or participation factors for increasing generation as the load increases. CSNBP determination has been formulated as an optimization problem to be used in PSO technique. PSO is a new evolutionary algorithm (EA) which is population based inspired by the social behavior of animals such as fish schooling and birds flocking. It can handle optimization problems with any complexity since mechanization is simple with few parameters to be tuned. The developed algorithm has been implemented on two standard test systems. (author)
Yan, Bailu; Zhao, Zheng; Zhou, Yingcheng; Yuan, Wenyan; Li, Jian; Wu, Jun; Cheng, Daojian
2017-10-01
Swarm intelligence optimization algorithms are mainstream algorithms for solving complex optimization problems. Among these algorithms, the particle swarm optimization (PSO) algorithm has the advantages of fast computation speed and few parameters. However, PSO is prone to premature convergence. To solve this problem, we develop a new PSO algorithm (RPSOLF) by combining the characteristics of random learning mechanism and Levy flight. The RPSOLF algorithm increases the diversity of the population by learning from random particles and random walks in Levy flight. On the one hand, we carry out a large number of numerical experiments on benchmark test functions, and compare these results with the PSO algorithm with Levy flight (PSOLF) algorithm and other PSO variants in previous reports. The results show that the optimal solution can be found faster and more efficiently by the RPSOLF algorithm. On the other hand, the RPSOLF algorithm can also be applied to optimize the Lennard-Jones clusters, and the results indicate that the algorithm obtains the optimal structure (2-60 atoms) with an extraordinary high efficiency. In summary, RPSOLF algorithm proposed in our paper is proved to be an extremely effective tool for global optimization.
Li, Dongsheng; Yang, Wei; Zhang, Wenyao
2017-05-01
Stress corrosion is the major failure type of bridge cable damage. The acoustic emission (AE) technique was applied to monitor the stress corrosion process of steel wires used in bridge cable structures. The damage evolution of stress corrosion in bridge cables was obtained according to the AE characteristic parameter figure. A particle swarm optimization cluster method was developed to determine the relationship between the AE signal and stress corrosion mechanisms. Results indicate that the main AE sources of stress corrosion in bridge cables included four types: passive film breakdown and detachment of the corrosion product, crack initiation, crack extension, and cable fracture. By analyzing different types of clustering data, the mean value of each damage pattern's AE characteristic parameters was determined. Different corrosion damage source AE waveforms and the peak frequency were extracted. AE particle swarm optimization cluster analysis based on principal component analysis was also proposed. This method can completely distinguish the four types of damage sources and simplifies the determination of the evolution process of corrosion damage and broken wire signals. Copyright © 2017. Published by Elsevier B.V.
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Zhilong Wang
2014-01-01
Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.
International Nuclear Information System (INIS)
Chen, Syuan-Yi; Hung, Yi-Hsuan; Wu, Chien-Hsun; Huang, Siang-Ting
2015-01-01
Highlights: • Online sub-optimal energy management using IPSO. • A second-order HEV model with 5 major segments was built. • IPSO with equivalent-fuel fitness function using 5 particles. • Engine, rule-based control, PSO, IPSO and ECMS are compared. • Max. 31+% fuel economy and 56+% energy consumption improved. - Abstract: This study developed an online suboptimal energy management system by using improved particle swarm optimization (IPSO) for engine/motor hybrid electric vehicles. The vehicle was modeled on the basis of second-order dynamics, and featured five major segments: a battery, a spark ignition engine, a lithium battery, transmission and vehicle dynamics, and a driver model. To manage the power distribution of dual power sources, the IPSO was equipped with three inputs (rotational speed, battery state-of-charge, and demanded torque) and one output (power split ratio). Five steps were developed for IPSO: (1) initialization; (2) determination of the fitness function; (3) selection and memorization; (4) modification of position and velocity; and (5) a stopping rule. Equivalent fuel consumption by the engine and motor was used as the fitness function with five particles, and the IPSO-based vehicle control unit was completed and integrated with the vehicle simulator. To quantify the energy improvement of IPSO, a four-mode rule-based control (system ready, motor only, engine only, and hybrid modes) was designed according to the engine efficiency and rotational speed. A three-loop Equivalent Consumption Minimization Strategy (ECMS) was coded as the best case. The simulation results revealed that IPSO searches the optimal solution more efficiently than conventional PSO does. In two standard driving cycles, ECE and FTP, the improvements in the equivalent fuel consumption and energy consumption compared to baseline were (24.25%, 45.27%) and (31.85%, 56.41%), respectively, for the IPSO. The CO 2 emission for all five cases (pure engine, rule-based, PSO
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ZHAO Jianhu
2016-08-01
Full Text Available Due to the problem of the existing image segmentation methods applied in side scan sonar (SSS image often suffered from low efficiency or low accuracy, this paper proposed a novel SSS image thresholding segmentation method based on neutrosophic set (NS and quantum-behaved particle swarm optimization (QPSO algorithm. Firstly, the image gray co-occurrence matrix is constructed in NS domain, the fine texture of SSS image is expressed, and this can improve the accuracy of SSS image segmentation. Then, based on the two-dimensional maximum entropy theory, the optimal two-dimensional segmentation threshold vector is quickly and accurately obtained by QPSO algorithm, and this can improve the efficiency and accuracy of SSS image segmentation. Finally, the accurate and high efficient target segmentation of SSS image with high noises is realized. The effectiveness of the algorithm is verified by segmenting SSS image containing different targets.
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Li Mao
2016-01-01
Full Text Available Artificial bee colony (ABC algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
2016-01-01
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. PMID:27428971
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
2016-07-14
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.
Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization
Jamaluddin; Siringoringo, Rimbun
2017-12-01
Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%
Bejagam, Karteek K; Singh, Samrendra; Deshmukh, Sanket A
2018-05-05
New Lennard-Jones parameters have been developed to describe the interactions between atomistic model of graphene, represented by REBO potential, and five commonly used all-atom water models, namely SPC, SPC/E, SPC/Fw, SPC/Fd, and TIP3P/Fs by employing particle swarm optimization (PSO) method. These new parameters were optimized to reproduce the macroscopic contact angle of water on a graphene sheet. The calculated line tension was in the order of 10 -11 J/m for the droplets of all water models. Our molecular dynamics simulations indicate the preferential orientation of water molecules near graphene-water interface with one OH bond pointing toward the graphene surface. Detailed analysis of simulation trajectories reveals the presence of water molecules with ≤∼1, ∼2, and ∼4 hydrogen bonds at the surface of air-water interface, graphene-water interface, and bulk region of the water droplet, respectively. Presence of water molecules with ≤∼1 and ∼2 hydrogen bonds suggest the existence of water clusters of different sizes at these interfaces. The trends observed in the libration, bending, and stretching bands of the vibrational spectra are closely associated with these structural features of water. The inhomogeneity in hydrogen bond network of water at the air-water and graphene-water interface is manifested by broadening of the peaks in the libration band for water present at these interfaces. The stretching band for the molecules in water droplet shows a blue shift as compared to the pure bulk water, which conjecture the presence of weaker hydrogen bond network in a droplet. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
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Xiaomin Xu
2015-01-01
Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.
Li, X.; Li, S. W.
2012-07-01
In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO), is introduced into close range photogrammetric data processing. PSO can be applied to obtain the approximate values of exterior orientation elements under the condition that multi-intersection photography and a small portable plane control frame are used. PSO, put forward by an American social psychologist J. Kennedy and an electrical engineer R.C. Eberhart, is a stochastic global optimization method based on swarm intelligence, which was inspired by social behavior of bird flocking or fish schooling. The strategy of obtaining the approximate values of exterior orientation elements using PSO is as follows: in terms of image coordinate observed values and space coordinates of few control points, the equations of calculating the image coordinate residual errors can be given. The sum of absolute value of each image coordinate is minimized to be the objective function. The difference between image coordinate observed value and the image coordinate computed through collinear condition equation is defined as the image coordinate residual error. Firstly a gross area of exterior orientation elements is given, and then the adjustment of other parameters is made to get the particles fly in the gross area. After iterative computation for certain times, the satisfied approximate values of exterior orientation elements are obtained. By doing so, the procedures like positioning and measuring space control points in close range photogrammetry can be avoided. Obviously, this method can improve the surveying efficiency greatly and at the same time can decrease the surveying cost. And during such a process, only one small portable control frame with a couple of control points is employed, and there are no strict requirements for the space distribution of control points. In order to verify the effectiveness of this algorithm, two experiments are
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X. Li
2012-07-01
Full Text Available In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO, is introduced into close range photogrammetric data processing. PSO can be applied to obtain the approximate values of exterior orientation elements under the condition that multi-intersection photography and a small portable plane control frame are used. PSO, put forward by an American social psychologist J. Kennedy and an electrical engineer R.C. Eberhart, is a stochastic global optimization method based on swarm intelligence, which was inspired by social behavior of bird flocking or fish schooling. The strategy of obtaining the approximate values of exterior orientation elements using PSO is as follows: in terms of image coordinate observed values and space coordinates of few control points, the equations of calculating the image coordinate residual errors can be given. The sum of absolute value of each image coordinate is minimized to be the objective function. The difference between image coordinate observed value and the image coordinate computed through collinear condition equation is defined as the image coordinate residual error. Firstly a gross area of exterior orientation elements is given, and then the adjustment of other parameters is made to get the particles fly in the gross area. After iterative computation for certain times, the satisfied approximate values of exterior orientation elements are obtained. By doing so, the procedures like positioning and measuring space control points in close range photogrammetry can be avoided. Obviously, this method can improve the surveying efficiency greatly and at the same time can decrease the surveying cost. And during such a process, only one small portable control frame with a couple of control points is employed, and there are no strict requirements for the space distribution of control points. In order to verify the effectiveness of this algorithm
Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization.
Modiri, Arezoo; Gu, Xuejun; Hagan, Aaron M; Sawant, Amit
2017-05-01
Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm-a popular RT optimization technique is also implemented and used. The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO. RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.
Yeh, Wei-Chang
2013-04-01
A new soft computing method called the parameter-free simplified swarm optimization (SSO)-based artificial neural network (ANN), or improved SSO for short, is proposed to adjust the weights in ANNs. The method is a modification of the SSO, and seeks to overcome some of the drawbacks of SSO. In the experiments, the iSSO is compared with five other famous soft computing methods, including the backpropagation algorithm, the genetic algorithm, the particle swarm optimization (PSO) algorithm, cooperative random learning PSO, and the SSO, and its performance is tested on five famous time-series benchmark data to adjust the weights of two ANN models (multilayer perceptron and single multiplicative neuron model). The experimental results demonstrate that iSSO is robust and more efficient than the other five algorithms.
Iswari, T.; Asih, A. M. S.
2018-04-01
In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.
Ervin, Katherine; Shipman, Steven
2017-06-01
While rotational spectra can be rapidly collected, their analysis (especially for complex systems) is seldom straightforward, leading to a bottleneck. The AUTOFIT program was designed to serve that need by quickly matching rotational constants to spectra with little user input and supervision. This program can potentially be improved by incorporating an optimization algorithm in the search for a solution. The Particle Swarm Optimization Algorithm (PSO) was chosen for implementation. PSO is part of a family of optimization algorithms called heuristic algorithms, which seek approximate best answers. This is ideal for rotational spectra, where an exact match will not be found without incorporating distortion constants, etc., which would otherwise greatly increase the size of the search space. PSO was tested for robustness against five standard fitness functions and then applied to a custom fitness function created for rotational spectra. This talk will explain the Particle Swarm Optimization algorithm and how it works, describe how Autofit was modified to use PSO, discuss the fitness function developed to work with spectroscopic data, and show our current results. Seifert, N.A., Finneran, I.A., Perez, C., Zaleski, D.P., Neill, J.L., Steber, A.L., Suenram, R.D., Lesarri, A., Shipman, S.T., Pate, B.H., J. Mol. Spec. 312, 13-21 (2015)
Identification of fast-steering mirror based on chicken swarm optimization algorithm
Ren, Wei; Deng, Chao; Zhang, Chao; Mao, Yao
2017-06-01
According to the transfer function identification method of fast steering mirror exists problems which estimate the initial value is complicated in the process of using, put forward using chicken swarm algorithm to simplify the identification operation, reducing the workload of identification. chicken swarm algorithm is a meta heuristic intelligent population algorithm, which shows global convergence is efficient in the identification experiment, and the convergence speed is fast. The convergence precision is also high. Especially there are many parameters are needed to identificate in the transfer function without considering the parameters estimation problem. Therefore, compared with the traditional identification methods, the proposed approach is more convenient, and greatly achieves the intelligent design of fast steering mirror control system in enginerring application, shorten time of controller designed.
Su, Hongsheng
2017-12-18
Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.
Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang
2018-04-01
Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.
Directory of Open Access Journals (Sweden)
Yu Huang
Full Text Available Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.
International Nuclear Information System (INIS)
Mhamdi, B.; Grayaa, K.; Aguili, T.
2011-01-01
In this paper, a microwave imaging technique for reconstructing the shape of two-dimensional perfectly conducting scatterers by means of a stochastic optimization approach is investigated. Based on the boundary condition and the measured scattered field derived by transverse magnetic illuminations, a set of nonlinear integral equations is obtained and the imaging problem is reformulated in to an optimization problem. A hybrid approximation algorithm, called PSO-SA, is developed in this work to solve the scattering inverse problem. In the hybrid algorithm, particle swarm optimization (PSO) combines global search and local search for finding the optimal results assignment with reasonable time and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of SA. Reconstruction results are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.
Directory of Open Access Journals (Sweden)
Lei Si
2016-06-01
Full Text Available In order to accurately evaluate the health condition of a shearer, a hybrid prediction method was proposed based on the integration of a fuzzy neural network (FNN and improved particle swarm optimization (IPSO. The parameters of FNN were optimized by the use of PSO, which was coupled with a premature judgment and mutation mechanism to increase the convergence speed and enhance the generalization ability. The key technologies are elaborated and the flowchart of the proposed approach was designed. Furthermore, an experiment example was carried out and the comparison results indicated that the proposed approach was feasible and outperforms others. Finally, a field application example in coal mining face was demonstrated to specify the effect of the proposed system.