A Novel Particle Swarm Optimization Algorithm for Global Optimization
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387
A Novel Particle Swarm Optimization Algorithm for Global Optimization.
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
A multiobjective memetic algorithm based on particle swarm optimization.
Liu, Dasheng; Tan, K C; Goh, C K; Ho, W K
2007-02-01
In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. The proposed features are examined to show their individual and combined effects in MO optimization. The comparative study shows the effectiveness of the proposed MA, which produces solution sets that are highly competitive in terms of convergence, diversity, and distribution. PMID:17278557
Acoustic Radiation Optimization Using the Particle Swarm Optimization Algorithm
NASA Astrophysics Data System (ADS)
Jeon, Jin-Young; Okuma, Masaaki
The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary population-based heuristic algorithm called the particle swarm optimization algorithm (PSOA). The particle swarm optimization algorithm is a parallel evolutionary computation technique proposed by Kennedy and Eberhart in 1995. This algorithm is based on the social behavior models for bird flocking, fish schooling and other models investigated by zoologists. Optimal structural design problems to reduce noise are highly nonlinear, so that most conventional methods are difficult to apply. The present paper investigates the applicability of PSOA to such problems. Optimal bending design of a vibrating plate using PSOA is performed in order to minimize noise radiation. PSOA can be effectively applied to such nonlinear acoustic radiation optimization.
A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations
NASA Technical Reports Server (NTRS)
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.
A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm
Ab Aziz, Nor Azlina; Mubin, Marizan; Mohamad, Mohd Saberi; Ab Aziz, Kamarulzaman
2014-01-01
In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well. PMID:25121109
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms
Garro, Beatriz A.; Vázquez, Roberto A.
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
Microwave-based medical diagnosis using particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
Modiri, Arezoo
This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level
NASA Astrophysics Data System (ADS)
Huang, Xiaobiao; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
Parallel global optimization with the particle swarm algorithm
Schutte, J. F.; Reinbolt, J. A.; Fregly, B. J.; Haftka, R. T.; George, A. D.
2007-01-01
SUMMARY Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. 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. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima—large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available. PMID:17891226
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
Multivariable optimization of liquid rocket engines using particle swarm algorithms
NASA Astrophysics Data System (ADS)
Jones, Daniel Ray
Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.
NASA Technical Reports Server (NTRS)
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.
Generalized Particle Swarm Algorithm for HCR Gearing Geometry Optimization
NASA Astrophysics Data System (ADS)
Kuzmanović, Siniša; Vereš, Miroslav; Rackov, Milan
2012-12-01
Particle swarm optimization algorithm based low cost magnetometer calibration
NASA Astrophysics Data System (ADS)
Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.
2011-12-01
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments
A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.
Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein
2016-01-01
Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945
A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm
Shamsi, Mousa; Sedaaghi, Mohammad Hossein
2016-01-01
Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945
NASA Astrophysics Data System (ADS)
Mao, Jiandong; Li, Jinxuan
2015-10-01
Particle size distribution is essential for describing direct and indirect radiation of aerosols. Because the relationship between the aerosol size distribution and optical thickness (AOT) is an ill-posed Fredholm integral equation of the first type, the traditional techniques for determining such size distributions, such as the Phillips-Twomey regularization method, are often ambiguous. Here, we use an approach based on an improved particle swarm optimization algorithm (IPSO) to retrieve aerosol size distribution. Using AOT data measured by a CE318 sun photometer in Yinchuan, we compared the aerosol size distributions retrieved using a simple genetic algorithm, a basic particle swarm optimization algorithm and the IPSO. Aerosol size distributions for different weather conditions were analyzed, including sunny, dusty and hazy conditions. Our results show that the IPSO-based inversion method retrieved aerosol size distributions under all weather conditions, showing great potential for similar size distribution inversions.
GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS
Rogers, Adam; Fiege, Jason D.
2011-02-01
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 {chi}{sup 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 {chi}{sup 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.
Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem
Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi
2013-01-01
Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429
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
Particle Swarm Optimization Toolbox
NASA Technical Reports Server (NTRS)
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
Dynamic topology multi force particle swarm optimization algorithm and its application
NASA Astrophysics Data System (ADS)
Chen, Dongning; Zhang, Ruixing; Yao, Chengyu; Zhao, Zheyu
2016-01-01
Particle swarm optimization (PSO) algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization (DTMFPSO) algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization (MFPSO) algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing (FE) topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as µPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.
Use of the particle swarm optimization algorithm for second order design of levelling networks
NASA Astrophysics Data System (ADS)
Yetkin, Mevlut; Inal, Cevat; Yigit, Cemal Ozer
2009-08-01
The weight problem in geodetic networks can be dealt with as an optimization procedure. This classic problem of geodetic network optimization is also known as second-order design. The basic principles of geodetic network optimization are reviewed. Then the particle swarm optimization (PSO) algorithm is applied to a geodetic levelling network in order to solve the second-order design problem. PSO, which is an iterative-stochastic search algorithm in swarm intelligence, emulates the collective behaviour of bird flocking, fish schooling or bee swarming, to converge probabilistically to the global optimum. Furthermore, it is a powerful method because it is easy to implement and computationally efficient. Second-order design of a geodetic levelling network using PSO yields a practically realizable solution. It is also suitable for non-linear matrix functions that are very often encountered in geodetic network optimization. The fundamentals of the method and a numeric example are given.
NASA Astrophysics Data System (ADS)
Shao, Gui-Fang; Wang, Ting-Na; Liu, Tun-Dong; Chen, Jun-Ren; Zheng, Ji-Wen; Wen, Yu-Hua
2015-01-01
Pt-Pd alloy nanoparticles, as potential catalyst candidates for new-energy resources such as fuel cells and lithium ion batteries owing to their excellent reactivity and selectivity, have aroused growing attention in the past years. Since structure determines physical and chemical properties of nanoparticles, the development of a reliable method for searching the stable structures of Pt-Pd alloy nanoparticles has become of increasing importance to exploring the origination of their properties. In this article, we have employed the particle swarm optimization algorithm to investigate the stable structures of alloy nanoparticles with fixed shape and atomic proportion. An improved discrete particle swarm optimization algorithm has been proposed and the corresponding scheme has been presented. Subsequently, the swap operator and swap sequence have been applied to reduce the probability of premature convergence to the local optima. Furthermore, the parameters of the exchange probability and the 'particle' size have also been considered in this article. Finally, tetrahexahedral Pt-Pd alloy nanoparticles has been used to test the effectiveness of the proposed method. The calculated results verify that the improved particle swarm optimization algorithm has superior convergence and stability compared with the traditional one.
NASA Astrophysics Data System (ADS)
Yoon, Kyung-Beom; Park, Won-Hee
2015-04-01
The convective heat transfer coefficient and surface emissivity before and after flame occurrence on a wood specimen surface and the flame heat flux were estimated using the repulsive particle swarm optimization algorithm and cone heater test results. The cone heater specified in the ISO 5660 standards was used, and six cone heater heat fluxes were tested. Preservative-treated Douglas fir 21 mm in thickness was used as the wood specimen in the tests. This study confirmed that the surface temperature of the specimen, which was calculated using the convective heat transfer coefficient, surface emissivity and flame heat flux on the wood specimen by a repulsive particle swarm optimization algorithm, was consistent with the measured temperature. Considering the measurement errors in the surface temperature of the specimen, the applicability of the optimization method considered in this study was evaluated.
Binary particle swarm optimization algorithm assisted to design of plasmonic nanospheres sensor
NASA Astrophysics Data System (ADS)
Kaboli, Milad; Akhlaghi, Majid; Shahmirzaee, Hossein
2016-04-01
In this study, a coherent perfect absorption (CPA)-type sensor based on plasmonic nanoparticles is proposed. It consists of a plasmonic nanospheres array on top of a quartz substrate. The refractive index changes above the sensor surface, which is due to the appearance of gas or the absorption of biomolecules, can be detected by measuring the resulting spectral shifts of the absorption coefficient. Since the CPA efficiency depends strongly on the number of plasmonic nanoparticles and the locations of nanoparticles, binary particle swarm optimization (BPSO) algorithm is used to design an optimized array of the plasmonic nanospheres. This optimized structure should be maximizing the absorption coefficient only in the one frequency. BPSO algorithm, a swarm of birds including a matrix with binary entries responsible for controlling nanospheres in the array, shows the presence with symbol of ('1') and the absence with ('0'). The sensor can be used for sensing both gas and low refractive index materials in an aqueous environment.
Optimal control of switched linear systems based on Migrant Particle Swarm Optimization algorithm
NASA Astrophysics Data System (ADS)
Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng
2009-10-01
The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.
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
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
Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm
NASA Astrophysics Data System (ADS)
De, Bishnu P.; Kar, Rajib; Mandal, Durbadal; Ghoshal, Sakti P.
2015-07-01
The inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds' flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.
Chang, Pei-Chann; Lin, Jyun-Jie; Liu, Chen-Hao
2012-09-01
In this research, a hybrid model is developed by integrating a case-based reasoning approach and a particle swarm optimization model for medical data classification. Two data sets from UCI Machine Learning Repository, i.e., Liver Disorders Data Set and Breast Cancer Wisconsin (Diagnosis), are employed for benchmark test. Initially a case-based reasoning method is applied to preprocess the data set thus a weight vector for each feature is derived. A particle swarm optimization model is then applied to construct a decision-making system for diseases identified. The PSO algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions and then reducing the number of clusters into two. The average forecasting accuracy for breast cancer of CBRPSO model is 97.4% and for liver disorders is 76.8%. The proposed case-based particle swarm optimization model is able to produce more accurate and comprehensible results for medical experts in medical diagnosis. PMID:21194784
NASA Astrophysics Data System (ADS)
Xu, Jiuping; Zeng, Ziqiang; Han, Bernard; Lei, Xiao
2013-07-01
This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic-pessimistic index. The iterative nature of the authors' model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors' optimization method, which is very effective as compared to the standard PSO algorithm.
Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng
2015-01-01
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. PMID:25603158
Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng
2015-01-01
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. PMID:25603158
NASA Astrophysics Data System (ADS)
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.
Jin, Junchen
2016-01-01
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. PMID:27436998
Wang, Jiaxi; Lin, Boliang; Jin, Junchen
2016-01-01
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. PMID:27436998
NASA Astrophysics Data System (ADS)
Yang, Yue; Wen, Jian; Chen, Xiaofei
2015-07-01
In this paper, we apply particle swarm optimization (PSO), an artificial intelligence technique, to velocity calibration in microseismic monitoring. We ran simulations with four 1-D layered velocity models and three different initial model ranges. The results using the basic PSO algorithm were reliable and accurate for simple models, but unsuccessful for complex models. We propose the staged shrinkage strategy (SSS) for the PSO algorithm. The SSS-PSO algorithm produced robust inversion results and had a fast convergence rate. We investigated the effects of PSO's velocity clamping factor in terms of the algorithm reliability and computational efficiency. The velocity clamping factor had little impact on the reliability and efficiency of basic PSO, whereas it had a large effect on the efficiency of SSS-PSO. Reassuringly, SSS-PSO exhibits marginal reliability fluctuations, which suggests that it can be confidently implemented.
Sung, Wen-Tsai; Chiang, Yen-Chun
2012-12-01
This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services. PMID:22492176
NASA Astrophysics Data System (ADS)
Wang, Deguang; Han, Baochang; Huang, Ming
Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928
Bayesian network structure learning based on the chaotic particle swarm optimization algorithm.
Zhang, Q; Li, Z; Zhou, C J; Wei, X P
2013-01-01
The Bayesian network (BN) is a knowledge representation form, which has been proven to be valuable in the gene regulatory network reconstruction because of its capability of capturing causal relationships between genes. Learning BN structures from a database is a nondeterministic polynomial time (NP)-hard problem that remains one of the most exciting challenges in machine learning. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, the classical K2 algorithm is the most successful. Nonetheless, the performance of the K2 algorithm is greatly affected by a prior ordering of input nodes. The proposed method in this paper is based on the chaotic particle swarm optimization (CPSO) and the K2 algorithm. Because the PSO algorithm completely entraps the local minimum in later evolutions, we combined the PSO algorithm with the chaos theory, which has the properties of ergodicity, randomness, and regularity. Experimental results show that the proposed method can improve the convergence rate of particles and identify networks more efficiently and accurately. PMID:24222226
Multiple R&D Projects Scheduling Optimization with Improved Particle Swarm Algorithm
Liu, Mengqi; Shan, Miyuan; Wu, Juan
2014-01-01
For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment. PMID:25032232
Huang, X N; Ren, H P
2016-01-01
Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation. PMID:27323043
Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm
NASA Astrophysics Data System (ADS)
Kia, Saeed; Sebt, Mohammad Hassan; Shahhosseini, Vahid
2015-03-01
Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR) to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane's elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar's weight, a 20% decrease in the concrete's weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.
Fitting of interatomic potentials without forces: A parallel particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
González, Diego; Davis, Sergio
2014-12-01
We present a methodology for fitting interatomic potentials to ab initio data, using the particle swarm optimization (PSO) algorithm, needing only a set of positions and energies as input. The prediction error of energies associated with the fitted parameters can be close to 1 meV/atom or lower, for reference energies having a standard deviation of about 0.5 eV/atom. We tested our method by fitting a Sutton-Chen potential for copper from ab initio data, which is able to recover structural and dynamical properties, and obtain a better agreement of the predicted melting point versus the experimental value, as compared to the prediction of the standard Sutton-Chen parameters.
NASA Astrophysics Data System (ADS)
Rocha, M. C.; Saraiva, J. T.
2012-10-01
The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.
An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel
NASA Astrophysics Data System (ADS)
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.
Designing a mirrored Howland circuit with a particle swarm optimisation algorithm
NASA Astrophysics Data System (ADS)
Bertemes-Filho, Pedro; Negri, Lucas H.; Vincence, Volney C.
2016-06-01
Electrical impedance spectroscopy usually requires a wide bandwidth current source with high output impedance. Non-idealities of the operational amplifier (op-amp) degrade its performance. This work presents a particle swarm algorithm for extracting the main AC characteristics of the op-amp used to design a mirrored modified Howland current source circuit which satisfies both the output current and the impedance spectra required. User specifications were accommodated. Both resistive and biological loads were used in the simulations. The results showed that the algorithm can correctly identify the open-loop gain and the input and output resistance of the op-amp which best fit the performance requirements of the circuit. It was also shown that the higher the open-loop gain corner frequency the higher the output impedance of the circuit. The algorithm could be a powerful tool for developing a desirable current source for different bioimpedance medical and clinical applications, such as cancer tissue characterisation and tissue cell measurements.
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
Naebi, Mohammad; Saberi, Eshaghali; Risbaf Fakour, Sirous; Naebi, Ahmad; Hosseini Tabatabaei, Somayeh; Ansari Moghadam, Somayeh; Bozorgmehr, Elham; Davtalab Behnam, Nasim; Azimi, Hamidreza
2016-01-01
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%. PMID:27212947
Parameters optimisation of a vehicle suspension system using a particle swarm optimisation algorithm
NASA Astrophysics Data System (ADS)
Centeno Drehmer, Luis Roberto; Paucar Casas, Walter Jesus; Martins Gomes, Herbert
2015-04-01
The purpose of this paper is to determine the lumped suspension parameters that minimise a multi-objective function in a vehicle model under different standard PSD road profiles. This optimisation tries to meet the rms vertical acceleration weighted limits for human sensitivity curves from ISO 2631 [ISO-2631: guide for evaluation of human exposure to whole-body vibration. Europe; 1997] at the driver's seat, the road holding capability and the suspension working space. The vehicle is modelled in the frequency domain using eight degrees of freedom under a random road profile. The particle swarm optimisation and sequential quadratic programming algorithms are used to obtain the suspension optimal parameters in different road profile and vehicle velocity conditions. A sensitivity analysis is performed using the obtained results and, in Class G road profile, the seat damping has the major influence on the minimisation of the multi-objective function. The influence of vehicle parameters in vibration attenuation is analysed and it is concluded that the front suspension stiffness should be less stiff than the rear ones when the driver's seat relative position is located forward the centre of gravity of the car body. Graphs and tables for the behaviour of suspension parameters related to road classes, used algorithms and velocities are presented to illustrate the results. In Class A road profile it was possible to find optimal parameters within the boundaries of the design variables that resulted in acceptable values for the comfort, road holding and suspension working space.
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm.
Naebi, Mohammad; Saberi, Eshaghali; Risbaf Fakour, Sirous; Naebi, Ahmad; Hosseini Tabatabaei, Somayeh; Ansari Moghadam, Somayeh; Bozorgmehr, Elham; Davtalab Behnam, Nasim; Azimi, Hamidreza
2016-01-01
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%. PMID:27212947
NASA Astrophysics Data System (ADS)
Fındık, Oğuz; Babaoğlu, İsmail; Ülker, Erkan
2010-12-01
In this paper, a novel robust watermarking technique using particle swarm optimization and k-nearest neighbor algorithm is introduced to protect the intellectual property rights of color images in the spatial domain. In the embedding process, the color image is separated into non-overlapping blocks and each bit of the binary watermark is embedded into the individual blocks. Then, in order to extract the embedded watermark, features are obtained from watermark embedded blocks using the symmetric cross-shape kernel. These features are used to generate two centroids belonging to each binary (1 and 0) value of the watermark implementing particle swarm optimization. Subsequently, the embedded watermark is extracted by evaluating these centroids utilizing k-nearest neighbor algorithm. According to the test results, embedded watermark is extracted successfully even if the watermarked image is exposed to various image processing attacks.
Motion generation of peristaltic mobile robot with particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
Homma, Takahiro; Kamamichi, Norihiro
2015-03-01
In developments of robots, bio-mimetics is attracting attention, which is a technology for the design of the structure and function inspired from biological system. There are a lot of examples of bio-mimetics in robotics such as legged robots, flapping robots, insect-type robots, fish-type robots. In this study, we focus on the motion of earthworm and aim to develop a peristaltic mobile robot. The earthworm is a slender animal moving in soil. It has a segmented body, and each segment can be shorted and lengthened by muscular actions. It can move forward by traveling expanding motions of each segment backward. By mimicking the structure and motion of the earthworm, we can construct a robot with high locomotive performance against an irregular ground or a narrow space. In this paper, to investigate the motion analytically, a dynamical model is introduced, which consist of a series-connected multi-mass model. Simple periodic patterns which mimic the motions of earthworms are applied in an open-loop fashion, and the moving patterns are verified through numerical simulations. Furthermore, to generate efficient motion of the robot, a particle swarm optimization algorithm, one of the meta-heuristic optimization, is applied. The optimized results are investigated by comparing to simple periodic patterns.
NASA Astrophysics Data System (ADS)
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.
Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System
Olusanya, Micheal O.; Arasomwan, Martins A.; Adewumi, Aderemi O.
2015-01-01
This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment. PMID:25815046
Particle swarm optimization algorithm for optimizing assignment of blood in blood banking system.
Olusanya, Micheal O; Arasomwan, Martins A; Adewumi, Aderemi O
2015-01-01
This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment. PMID:25815046
Ramyachitra, D.; Sofia, M.; Manikandan, P.
2015-01-01
Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions. PMID:26484222
Ramyachitra, D; Sofia, M; Manikandan, P
2015-09-01
Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions. PMID:26484222
NASA Astrophysics Data System (ADS)
Zhao, Jianhu; Wang, Xiao; Zhang, Hongmei; Hu, Jun; Jian, Xiaomin
2016-06-01
To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Design Optimization of Pin Fin Geometry Using Particle Swarm Optimization Algorithm
Hamadneh, Nawaf; Khan, Waqar A.; Sathasivam, Saratha; Ong, Hong Choon
2013-01-01
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. PMID:23741525
Application of Particle Swarm Optimization Algorithm for Better Nano-Devices
NASA Astrophysics Data System (ADS)
Singh, Nameirakpam Basanta; Deb, Sanjoy; Mishra, Guru P.; Sarkar, Samir Kumar; Sarkar, Subir Kumar
Particle swarm optimization, an intelligent soft computing tool is employed to determine the optimized system parameters of GaAs quantum well for better high frequency performance under hot electron condition at room temperature. The energy loss through LO phonon and momentum loss through LO phonon, deformation acoustic phonon and ionized impurity (both background and remote) are incorporated in the present calculations. For a typical dc biasing field, it is possible to predict the optimum values of system parameters like lattice temperature, well width and two-dimensional carrier concentration for realizing a particular high frequency response characterised by well defined cut-off frequency. Such optimization will make feasible the fabrication of a variety of new quantum devices with desired characteristics.
Design optimization of pin fin geometry using particle swarm optimization algorithm.
Hamadneh, Nawaf; Khan, Waqar A; Sathasivam, Saratha; Ong, Hong Choon
2013-01-01
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. PMID:23741525
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
2016-07-01
This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design. PMID:27475078
NASA Astrophysics Data System (ADS)
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
2016-07-01
This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.
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. PMID:22738782
Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions
Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima
2013-01-01
The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm. PMID:23737718
Lin, Kuan-Cheng; Hsieh, Yi-Hsiu
2015-10-01
The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features. PMID:26289628
A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem
NASA Astrophysics Data System (ADS)
Rahimi-Vahed, A. R.; Mirghorbani, S. M.; Rabbani, M.
2007-12-01
Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.
NASA Astrophysics Data System (ADS)
Palma, Giuseppe; Bia, Pietro; Mescia, Luciano; Yano, Tetsuji; Nazabal, Virginie; Taguchi, Jun; Moréac, Alain; Prudenzano, Francesco
2014-07-01
A mid-IR amplifier consisting of a tapered chalcogenide fiber coupled to an Er-doped chalcogenide microsphere has been optimized via a particle swarm optimization (PSO) approach. More precisely, a dedicated three-dimensional numerical model, based on the coupled mode theory and solving the rate equations, has been integrated with the PSO procedure. The rate equations have included the main transitions among the erbium energy levels, the amplified spontaneous emission, and the most important secondary transitions pertaining to the ion-ion interactions. The PSO has allowed the optimal choice of the microsphere and fiber radius, taper angle, and fiber-microsphere gap in order to maximize the amplifier gain. The taper angle and the fiber-microsphere gap have been optimized to efficiently inject into the microsphere both the pump and the signal beams and to improve their spatial overlapping with the rare-earth-doped region. The employment of the PSO approach shows different attractive features, especially when many parameters have to be optimized. The numerical results demonstrate the effectiveness of the proposed approach for the design of amplifying systems. The PSO-based optimization approach has allowed the design of a microsphere-based amplifying system more efficient than a similar device designed by using a deterministic optimization method. In fact, the amplifier designed via the PSO exhibits a simulated gain G=33.7 dB, which is higher than the gain G=6.9 dB of the amplifier designed via the deterministic method.
2014-01-01
Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. PMID:24555567
NASA Astrophysics Data System (ADS)
Hu, Yifan; Ding, Yongsheng; Hao, Kuangrong; Ren, Lihong; Han, Hua
2014-03-01
The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
2016-01-01
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. PMID:26861348
NASA Astrophysics Data System (ADS)
Feng, Kunpeng; Zhou, Tong; Cui, Jiwen; Tan, Jiubin
2014-11-01
This paper presents a novel example-based super-resolution (SR) algorithm with improved k-means cluster. In this algorithm, genetic k-means (GKM) with hybrid particle swarm optimization (HPSO) is employed to improve the reconstruction of high-resolution (HR) images, and a pre-processing of classification in frequency is used to accelerate the procedure. Self-redundancy across different scales of a natural image is also utilized to build attached training set to expand example-based information. Meanwhile, a reconstruction algorithm based on hybrid supervise locally linear embedding (HSLLE) is proposed which uses training sets, high-resolution images and self-redundancy across different scales of a natural image. Experimental results show that patches are classified rapidly in training set processing session and the runtime of reconstruction is half of traditional algorithm at least in super-resolution session. And clustering and attached training set lead to a better recovery of low-resolution (LR) image.
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
2016-01-01
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. PMID:26861348
NASA Astrophysics Data System (ADS)
Abedi, Kambiz; Mirjalili, Seyed Mohammad
2015-03-01
Recently, majority of current research in the field of designing Phonic Crystal Waveguides (PCW) focus in extracting the relations between output slow light properties of PCW and structural parameters through a huge number of tedious non-systematic simulations in order to introduce better designs. This paper proposes a novel systematic approach which can be considered as a shortcut to alleviate the difficulties and human involvements in designing PCWs. In the proposed method, the problem of PCW design is first formulated as an optimization problem. Then, an optimizer is employed in order to automatically find the optimum design for the formulated PCWs. Meanwhile, different constraints are also considered during optimization with the purpose of applying physical limitations to the final optimum structure. As a case study, the structure of a Bragg-like Corrugation Slotted PCWs (BCSPCW) is optimized by using the proposed method. One of the most computationally powerful techniques in Computational Intelligence (CI) called Particle Swarm Optimization (PSO) is employed as an optimizer to automatically find the optimum structure for BCSPCW. The optimization process is done by considering five constraints to guarantee the feasibility of the final optimized structures and avoid band mixing. Numerical results demonstrate that the proposed method is able to find an optimum structure for BCSPCW with 172% and 100% substantial improvements in the bandwidth and Normalized Delay-Bandwidth Product (NDBP) respectively compared to the best current structure in the literature. Moreover, there is a time domain analysis at the end of the paper which verifies the performance of the optimized structure and proves that this structure has low distortion and attenuation simultaneously.
A comprehensive review of swarm optimization algorithms.
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
A Comprehensive Review of Swarm Optimization Algorithms
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
Chiang, Tzu-An; Che, Z. H.
2014-01-01
This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), VMax method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. PMID:24772026
NASA Astrophysics Data System (ADS)
Prabowo, K.; Sumaryada, T.; Kartono, A.
2016-01-01
Simulation of predictive modeling oral drug namely Compartment Absorption and Transit (CAT) using Particle Swarm Optimization (PSO) algorithm has been performed. This research will be carried out optimization of kinetic constant value oral drug use PSO algorithm to obtain the best global transport constant values for CAT equation that can predict drug concentration in plasma. The value of drug absorption rate constant for drug atenolol 25 mg is k10, k12, k21, k13 and k31 with each value is 0.8562, 0.3736, 0.2191, 0.4334 and 1.000 have been obtained thus raising the value of the coefficient of determination of a model CAT. From the experimental data plasma drug concentrations used are Atenolol, the coefficient of determination (R2) obtained from simulations atenolol 25 mg (PSO) was 81.72% and 99.46%. Better correlation between the dependent variable as the drug concentration and explanatory variables such as mass medication, plasma volume, and rate of absorption of the drug has increased in CAT models using PSO algorithm. Based on the results of CAT models fit charts can predict drug concentration in plasma.
Chiang, Tzu-An; Che, Z H; Cui, Zhihua
2014-01-01
This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. PMID:24772026
Selectively-informed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
2015-01-01
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their 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. PMID:25787315
Selectively-informed particle swarm optimization
Gao, Yang; Du, Wenbo; Yan, Gang
2015-01-01
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their 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. PMID:25787315
Selectively-informed particle swarm optimization
NASA Astrophysics Data System (ADS)
Gao, Yang; Du, Wenbo; Yan, Gang
2015-03-01
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their 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.
Incremental social learning in particle swarms.
de Oca, Marco A Montes; Stutzle, Thomas; Van den Enden, Ken; Dorigo, Marco
2011-04-01
Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure. We first derive analytically the probability density function induced by the proposed initialization rule applied to new particles. Then, we compare the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms (with and without local search) and a random restart local search algorithm. Finally, we measure the benefits of using incremental social learning on PSO algorithms by running IPSO and IPSOLS on problems with different fitness distance correlations. PMID:20875976
Wang, Wei; Chen, Xiyuan
2016-08-10
Modeling and compensation of temperature drift is an important method for improving the precision of fiber-optic gyroscopes (FOGs). In this paper, a new method of modeling and compensation for FOGs based on improved particle swarm optimization (PSO) and support vector machine (SVM) algorithms is proposed. The convergence speed and reliability of PSO are improved by introducing a dynamic inertia factor. The regression accuracy of SVM is improved by introducing a combined kernel function with four parameters and piecewise regression with fixed steps. The steps are as follows. First, the parameters of the combined kernel functions are optimized by the improved PSO algorithm. Second, the proposed kernel function of SVM is used to carry out piecewise regression, and the regression model is also obtained. Third, the temperature drift is compensated for by the regression data. The regression accuracy of the proposed method (in the case of mean square percentage error indicators) increased by 83.81% compared to the traditional SVM. PMID:27534465
2014-01-01
Background Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. Methods This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). Results The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. Conclusions This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion. PMID:24955105
NASA Astrophysics Data System (ADS)
Khehra, Baljit Singh; Pharwaha, Amar Partap Singh
2016-06-01
Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.
Multiswarm Particle Swarm Optimization with Transfer of the Best Particle
Wei, Xiao-peng; Zhang, Jian-xia; Zhou, Dong-sheng; Zhang, Qiang
2015-01-01
We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems. PMID:26345200
Mousavi, Seyed Mohsen; Niaki, S. T. A.; Bahreininejad, Ardeshir; Musa, Siti Nurmaya
2014-01-01
A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA. PMID:25093195
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. PMID:25709937
Analysis of image thresholding segmentation algorithms based on swarm intelligence
NASA Astrophysics Data System (ADS)
Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo
2013-03-01
Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.
Wang, Jie-Sheng; Han, Shuang
2015-01-01
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034
Lagrange Interpolation Learning Particle Swarm Optimization.
Kai, Zhang; Jinchun, Song; Ke, Ni; Song, Li
2016-01-01
In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles' diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle's historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO's comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence. PMID:27123982
Composite Particle Swarm Optimizer With Historical Memory for Function Optimization.
Li, Jie; Zhang, JunQi; Jiang, ChangJun; Zhou, MengChu
2015-10-01
Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms. PMID:26390177
NASA Astrophysics Data System (ADS)
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.
Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin
2007-01-30
Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks. PMID:17186488
Particle Swarm Optimization with Double Learning Patterns
Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian
2016-01-01
Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747
Particle Swarm Optimization with Double Learning Patterns.
Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian
2016-01-01
Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747
Lagrange Interpolation Learning Particle Swarm Optimization
2016-01-01
In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles’ diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle’s historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO’s comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence. PMID:27123982
Particle Swarm Transport in Fracture Networks
NASA Astrophysics Data System (ADS)
Pyrak-Nolte, L. J.; Mackin, T.; Boomsma, E.
2012-12-01
Colloidal particles of many types occur in fractures in the subsurface as a result of both natural and industrial processes (e.g., environmental influences, synthetic nano- & micro-particles from consumer products, chemical and mechanical erosion of geologic material, proppants used in gas and oil extraction, etc.). The degree of localization and speed of transport of such particles depends on the transport mechanisms, the chemical and physical properties of the particles and the surrounding rock, and the flow path geometry through the fracture. In this study, we investigated the transport of particle swarms through artificial fracture networks. A synthetic fracture network was created using an Objet Eden 350V 3D printer to build a network of fractures. Each fracture in the network had a rectangular cross-sectional area with a constant depth of 7 mm but with widths that ranged from 2 mm to 11 mm. The overall dimensions of the network were 132 mm by 166 mm. The fracture network had 7 ports that were used either as the inlet or outlet for fluid flow through the sample or for introducing a particle swarm. Water flow rates through the fracture were controlled with a syringe pump, and ranged from zero flow to 6 ml/min. Swarms were composed of a dilute suspension (2% by mass) of 3 μm fluorescent polystyrene beads in water. Swarms with volumes of 5, 10, 20, 30 and 60 μl were used and delivered into the network using a second syringe pump. The swarm behavior was imaged using an optical fluorescent imaging system illuminated by green (525 nm) LED arrays and captured by a CCD camera. For fracture networks with quiescent fluids, particle swarms fell under gravity and remained localized within the network. Large swarms (30-60 μl) were observed to bifurcate at shallower depths resulting in a broader dispersal of the particles than for smaller swarm volumes. For all swarm volumes studied, particle swarms tended to bifurcate at the intersection between fractures. These
Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin
2016-01-15
Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods. PMID:26592652
Yang, Qidong; Zuo, Hongchao; Li, Weidong
2016-01-01
Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large. PMID:26991786
Yang, Qidong; Zuo, Hongchao; Li, Weidong
2016-01-01
Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large. PMID:26991786
NASA Astrophysics Data System (ADS)
Wen, Xiulan; Zhao, Yibing; Wang, Dongxia; Zhu, Xiaochu; Xue, Xiaoqiang
2013-03-01
Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline(NURBS) for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system(DCS) and measurement coordinate system(MCS) for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization(QPSO) is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.
Swarm-based algorithm for phase unwrapping.
da Silva Maciel, Lucas; Albertazzi, Armando G
2014-08-20
A novel algorithm for phase unwrapping based on swarm intelligence is proposed. The algorithm was designed based on three main goals: maximum coverage of reliable information, focused effort for better efficiency, and reliable unwrapping. Experiments were performed, and a new agent was designed to follow a simple set of five rules in order to collectively achieve these goals. These rules consist of random walking for unwrapping and searching, ambiguity evaluation by comparing unwrapped regions, and a replication behavior responsible for the good distribution of agents throughout the image. The results were comparable with the results from established methods. The swarm-based algorithm was able to suppress ambiguities better than the flood-fill algorithm without relying on lengthy processing times. In addition, future developments such as parallel processing and better-quality evaluation present great potential for the proposed method. PMID:25321125
NASA Astrophysics Data System (ADS)
Ge, Xinmin; Wang, Hua; Fan, Yiren; Cao, Yingchang; Chen, Hua; Huang, Rui
2016-01-01
With more information than the conventional one dimensional (1D) longitudinal relaxation time (T1) and transversal relaxation time (T2) spectrums, a two dimensional (2D) T1-T2 spectrum in a low field nuclear magnetic resonance (NMR) is developed to discriminate the relaxation components of fluids such as water, oil and gas in porous rock. However, the accuracy and efficiency of the T1-T2 spectrum are limited by the existing inversion algorithms and data acquisition schemes. We introduce a joint method to inverse the T1-T2 spectrum, which combines iterative truncated singular value decomposition (TSVD) and a parallel particle swarm optimization (PSO) algorithm to get fast computational speed and stable solutions. We reorganize the first kind Fredholm integral equation of two kernels to a nonlinear optimization problem with non-negative constraints, and then solve the ill-conditioned problem by the iterative TSVD. Truncating positions of the two diagonal matrices are obtained by the Akaike information criterion (AIC). With the initial values obtained by TSVD, we use a PSO with parallel structure to get the global optimal solutions with a high computational speed. We use the synthetic data with different signal to noise ratio (SNR) to test the performance of the proposed method. The result shows that the new inversion algorithm can achieve favorable solutions for signals with SNR larger than 10, and the inversion precision increases with the decrease of the components of the porous rock.
NASA Astrophysics Data System (ADS)
Chen, Xia; Hu, Hong-li; Liu, Fei; Gao, Xiang Xiang
2011-10-01
The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality.
Transport of Particle Swarms Through Fractures
NASA Astrophysics Data System (ADS)
Boomsma, E.; Pyrak-Nolte, L. J.
2011-12-01
The transport of engineered micro- and nano-scale particles through fractured rock is often assumed to occur as dispersions or emulsions. Another potential transport mechanism is the release of particle swarms from natural or industrial processes where small liquid drops, containing thousands to millions of colloidal-size particles, are released over time from seepage or leaks. Swarms have higher velocities than any individual colloid because the interactions among the particles maintain the cohesiveness of the swarm as it falls under gravity. Thus particle swarms give rise to the possibility that engineered particles may be transported farther and faster in fractures than predicted by traditional dispersion models. In this study, the effect of fractures on colloidal swarm cohesiveness and evolution was studied as a swarm falls under gravity and interacts with fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with either (1) a uniform aperture or (2) a converging aperture followed by a uniform aperture (funnel-shaped). The samples consisted of two blocks that measured 100 x 100 x 50 mm. The separation between these blocks determined the aperture (0.5 mm to 50 mm). During experiments, a fracture was fully submerged in water and swarms were released into it. The swarms consisted of dilute suspensions of either 25 micron soda-lime glass beads (2% by mass) or 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. In the uniform aperture fracture, the speed of the swarm prior to bifurcation increased with aperture up to a maximum at a fracture width of approximately 10 mm. For apertures greater than ~15 mm, the velocity was essentially constant with fracture width (but less than at 10 mm). This peak suggests that two competing mechanisms affect swarm velocity in fractures. The wall provides both drag, which
Automatized Parameterization of DFTB Using Particle Swarm Optimization.
Chou, Chien-Pin; Nishimura, Yoshifumi; Fan, Chin-Chai; Mazur, Grzegorz; Irle, Stephan; Witek, Henryk A
2016-01-12
We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented. PMID:26587758
NASA Astrophysics Data System (ADS)
Mahmoodabadi, M. J.; Bagheri, A.; Nariman-zadeh, N.; Jamali, A.
2012-10-01
Particle swarm optimization (PSO) is a randomized and population-based optimization method that was inspired by the flocking behaviour of birds and human social interactions. In this work, multi-objective PSO is modified in two stages. In the first stage, PSO is combined with convergence and divergence operators. Here, this method is named CDPSO. In the second stage, to produce a set of Pareto optimal solutions which has good convergence, diversity and distribution, two mechanisms are used. In the first mechanism, a new leader selection method is defined, which uses the periodic iteration and the concept of the particle's neighbour number. This method is named periodic multi-objective algorithm. In the second mechanism, an adaptive elimination method is employed to limit the number of non-dominated solutions in the archive, which has influences on computational time, convergence and diversity of solution. Single-objective results show that CDPSO performs very well on the complex test functions in terms of solution accuracy and convergence speed. Furthermore, some benchmark functions are used to evaluate the performance of periodic multi-objective CDPSO. This analysis demonstrates that the proposed algorithm operates better in three metrics through comparison with three well-known elitist multi-objective evolutionary algorithms. Finally, the algorithm is used for Pareto optimal design of a two-degree of freedom vehicle vibration model. The conflicting objective functions are sprung mass acceleration and relative displacement between sprung mass and tyre. The feasibility and efficiency of periodic multi-objective CDPSO are assessed in comparison with multi-objective modified NSGAII.
Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem
2014-01-01
Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859
Sieh Kiong, Tiong; Tariqul Islam, Mohammad; Ismail, Mahamod; Salem, Balasem
2014-01-01
Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859
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. PMID:25013845
NASA Astrophysics Data System (ADS)
Sarkar, Soham; Das, Swagatam
In recent years particle swarm optimization emerges as one of the most efficient global optimization tools. In this paper, a hybrid particle swarm with differential evolution operator, termed DEPSO, is applied for the synthesis of linear array geometry. Here, the minimum side lobe level and null control, both are obtained by optimizing the spacing between the array elements by this technique. Moreover, a statistical comparison is also provided to establish its performance against the results obtained by Genetic Algorithm (GA), classical Particle Swarm Optimization (PSO), Tabu Search Algorithm (TSA), Differential Evolution (DE) and Memetic Algorithm (MA).
A Novel Particle Swarm Optimization Approach for Grid Job Scheduling
NASA Astrophysics Data System (ADS)
Izakian, Hesam; Tork Ladani, Behrouz; Zamanifar, Kamran; Abraham, Ajith
This paper represents a Particle Swarm Optimization (PSO) algorithm, for grid job scheduling. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. In this paper we used a PSO approach for grid job scheduling. The scheduler aims at minimizing makespan and flowtime simultaneously. Experimental studies show that the proposed novel approach is more efficient than the PSO approach reported in the literature.
A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system
NASA Astrophysics Data System (ADS)
Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun
2014-11-01
In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.
Zhao, Xiujuan; Xu, Wei; Ma, Yunjia; Hu, Fuyu
2015-01-01
The correct location of earthquake emergency shelters and their allocation to residents can effectively reduce the number of casualties by providing safe havens and efficient evacuation routes during the chaotic period of the unfolding disaster. However, diverse and strict constraints and the discrete feasible domain of the required models make the problem of shelter location and allocation more difficult. A number of models have been developed to solve this problem, but there are still large differences between the models and the actual situation because the characteristics of the evacuees and the construction costs of the shelters have been excessively simplified. We report here the development of a multi-objective model for the allocation of residents to earthquake shelters by considering these factors using the Chaoyang district, Beijing, China as a case study. The two objectives of this model were to minimize the total weighted evacuation time from residential areas to a specified shelter and to minimize the total area of all the shelters. The two constraints were the shelter capacity and the service radius. Three scenarios were considered to estimate the number of people who would need to be evacuated. The particle swarm optimization algorithm was first modified by applying the von Neumann structure in former loops and global structure in later loops, and then used to solve this problem. The results show that increasing the shelter area can result in a large decrease in the total weighted evacuation time from scheme 1 to scheme 9 in scenario A, from scheme 1 to scheme 9 in scenario B, from scheme 1 to scheme 19 in scenario C. If the funding were not a limitation, then the final schemes of each scenario are the best solutions, otherwise the earlier schemes are more reasonable. The modified model proved to be useful for the optimization of shelter allocation, and the result can be used as a scientific reference for planning shelters in the Chaoyang district
Adaptive Flocking of Robot Swarms: Algorithms and Properties
NASA Astrophysics Data System (ADS)
Lee, Geunho; Chong, Nak Young
This paper presents a distributed approach for adaptive flocking of swarms of mobile robots that enables to navigate autonomously in complex environments populated with obstacles. Based on the observation of the swimming behavior of a school of fish, we propose an integrated algorithm that allows a swarm of robots to navigate in a coordinated manner, split into multiple swarms, or merge with other swarms according to the environment conditions. We prove the convergence of the proposed algorithm using Lyapunov stability theory. We also verify the effectiveness of the algorithm through extensive simulations, where a swarm of robots repeats the process of splitting and merging while passing around multiple stationary and moving obstacles. The simulation results show that the proposed algorithm is scalable, and robust to variations in the sensing capability of individual robots.
Particle Swarm Based Collective Searching Model for Adaptive Environment
Cui, Xiaohui; Patton, Robert M; Potok, Thomas E; Treadwell, Jim N
2007-01-01
This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the collective search behavior of self-organized groups in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social group adaptation for the dynamic environment and to provide insight and understanding of social group knowledge discovering and strategic searching. A new adaptive environment model, which dynamically reacts to the group collective searching behaviors, is proposed in this research. The simulations in the research indicate that effective communication between groups is not the necessary requirement for whole self-organized groups to achieve the efficient collective searching behavior in the adaptive environment.
Particle Swarm Based Collective Searching Model for Adaptive Environment
Cui, Xiaohui; Patton, Robert M; Potok, Thomas E; Treadwell, Jim N
2008-01-01
This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the collective search behavior of self-organized groups in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social group adaptation for the dynamic environment and to provide insight and understanding of social group knowledge discovering and strategic searching. A new adaptive environment model, which dynamically reacts to the group collective searching behaviors, is proposed in this research. The simulations in the research indicate that effective communication between groups is not the necessary requirement for whole self-organized groups to achieve the efficient collective searching behavior in the adaptive environment.
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
NASA Astrophysics Data System (ADS)
Basu, Mousumi
2015-07-01
Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.
Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang
2012-08-01
Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can
Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang
2012-01-01
Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can
Particle swarm optimization for the clustering of wireless sensors
NASA Astrophysics Data System (ADS)
Tillett, Jason C.; Rao, Raghuveer M.; Sahin, Ferat; Rao, T. M.
2003-07-01
Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network.
Roundness error assessment based on particle swarm optimization
NASA Astrophysics Data System (ADS)
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.
Particle swarm optimization with recombination and dynamic linkage discovery.
Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung
2007-12-01
In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system. PMID:18179066
Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders
Lim, Kian Sheng; Buyamin, Salinda; Ahmad, Anita; Shapiai, Mohd Ibrahim; Naim, Faradila; Mubin, Marizan; Kim, Dong Hwa
2014-01-01
The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms. PMID:24883386
Unit Commitment by Adaptive Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Saber, Ahmed Yousuf; Senjyu, Tomonobu; Miyagi, Tsukasa; Urasaki, Naomitsu; Funabashi, Toshihisa
This paper presents an Adaptive Particle Swarm Optimization (APSO) for Unit Commitment (UC) problem. APSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable size and load demand, adaptive criteria are applied on PSO parameters and the global best particle (knowledge) based on the diversity of fitness. In this proposed method, PSO parameters are automatically adjusted using Gaussian modification. To increase the knowledge, the global best particle is updated instead of a fixed one in each generation. To avoid the method to be frozen, idle particles are reset. The real velocity is digitized (0/1) by a logistic function for binary UC. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method.
Bio Inspired Swarm Algorithm for Tumor Detection in Digital Mammogram
NASA Astrophysics Data System (ADS)
Dheeba, J.; Selvi, Tamil
Microcalcification clusters in mammograms is the significant early sign of breast cancer. Individual clusters are difficult to detect and hence an automatic computer aided mechanism will help the radiologist in detecting the microcalcification clusters in an easy and efficient way. This paper presents a new classification approach for detection of microcalcification in digital mammogram using particle swarm optimization algorithm (PSO) based clustering technique. Fuzzy C-means clustering technique, well defined for clustering data sets are used in combination with the PSO. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model. This method is quite simple and valid, and it can avoid the minimum local value. The proposed classification approach is applied to a database of 322 dense mammographic images, originating from the MIAS database. Results shows that the proposed PSO-FCM approach gives better detection performance compared to conventional approaches.
A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis.
Raja, Chandrasekaran; Gangatharan, Narayanan
2015-08-01
Glaucoma is among the most common causes of permanent blindness in human. Because the initial symptoms are not evident, mass screening would assist early diagnosis in the vast population. Such mass screening requires an automated diagnosis technique. Our proposed automation consists of pre-processing, optimal wavelet transformation, feature extraction, and classification modules. The hyper analytic wavelet transformation (HWT) based statistical features are extracted from fundus images. Because HWT preserves phase information, it is appropriate for feature extraction. The features are then classified by a Support Vector Machine (SVM) with a radial basis function (RBF) kernel. The filter coefficients of the wavelet transformation process and the SVM-RB width parameter are simultaneously tailored to best-fit the diagnosis by the hybrid Particle Swarm algorithm. To overcome premature convergence, a Group Search Optimizer (GSO) random searching (ranging) and area scanning behavior (around the optima) are embedded within the Particle Swarm Optimization (PSO) framework. We also embed a novel potential-area scanning as a preventive mechanism against premature convergence, rather than diagnosis and cure. This embedding does not compromise the generality and utility of PSO. In two 10-fold cross-validated test runs, the diagnostic accuracy of the proposed hybrid PSO exceeded that of conventional PSO. Furthermore, the hybrid PSO maintained the ability to explore even at later iterations, ensuring maturity in fitness. PMID:26093787
Thermal design of an electric motor using Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Jandaud, P.-O.; Harmand, S.; Fakes, M.
2012-11-01
In this paper, flow inside an electric machine called starter-alternator is studied parametrically with CFD in order to be used by a thermal lumped model coupled to an optimization algorithm using Particle Swarm Optimization (PSO). In a first case, the geometrical parameters are symmetric allowing us to model only one side of the machine. The optimized thermal results are not conclusive. In a second case, all the parameters are independent. In this case, the flow is strongly influenced by the dissymmetry. Optimization results are this time a clear improvement compared to the original machine.
Algorithmic requirements for swarm intelligence in differently coupled collective systems.
Stradner, Jürgen; Thenius, Ronald; Zahadat, Payam; Hamann, Heiko; Crailsheim, Karl; Schmickl, Thomas
2013-05-01
Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments. PMID:23805030
Algorithmic requirements for swarm intelligence in differently coupled collective systems
Stradner, Jürgen; Thenius, Ronald; Zahadat, Payam; Hamann, Heiko; Crailsheim, Karl; Schmickl, Thomas
2013-01-01
Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments. PMID:23805030
Bifurcating Particle Swarms in Smooth-Walled Fractures
NASA Astrophysics Data System (ADS)
Pyrak-Nolte, L. J.; Sun, H.
2010-12-01
Particle swarms can occur naturally or from industrial processes where small liquid drops containing thousands to millions of micron-size to colloidal-size particles are released over time from seepage or leaks into fractured rock. The behavior of these particle swarms as they fall under gravity are affected by particle interactions as well as interactions with the walls of the fractures. In this paper, we present experimental results on the effect of fractures on the cohesiveness of the swarm and the formation of bifurcation structures as they fall under gravity and interact with the fracture walls. A transparent cubic sample (100 mm x 100 mm x 100 mm) containing a synthetic fracture with uniform aperture distributions was optically imaged to quantify the effect of confinement within fractures on particle swarm formation, swarm velocity, and swarm geometry. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass). The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. Experiments were performed using swarms that ranged in size from 5 µl to 60 µl. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. As a swarm falls in an open-tank of water, it forms a torroidal shape that is stable as long as no ambient or background currents exist in the water tank. When a swarm is released into a fracture with an aperture less than 5 mm, the swarm forms the torroidal shape but it is distorted because of the presence of the walls. The
NASA Astrophysics Data System (ADS)
Huang, Haibin; Zhuang, Yufei
2015-08-01
This paper proposes a method that plans energy-optimal trajectories for multi-satellite formation reconfiguration in deep space environment. A novel co-evolutionary particle swarm optimization algorithm is stated to solve the nonlinear programming problem, so that the computational complexity of calculating the gradient information could be avoided. One swarm represents one satellite, and through communication with other swarms during the evolution, collisions between satellites can be avoided. In addition, a dynamic depth first search algorithm is proposed to solve the redundant search problem of a co-evolutionary particle swarm optimization method, with which the computation time can be shorten a lot. In order to make the actual trajectories optimal and collision-free with disturbance, a re-planning strategy is deduced for formation reconfiguration maneuver.
Particle Swarm Inspired Underwater Sensor Self-Deployment
Du, Huazheng; Xia, Na; Zheng, Rong
2014-01-01
Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation. PMID:25195852
Particle swarm inspired underwater sensor self-deployment.
Du, Huazheng; Xia, Na; Zheng, Rong
2014-01-01
Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation. PMID:25195852
Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo
2012-01-01
Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods. PMID:21905841
Particle Swarm and Ant Colony Approaches in Multiobjective Optimization
NASA Astrophysics Data System (ADS)
Rao, S. S.
2010-10-01
The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.
Binary Particle Swarm Optimization based Biclustering of Web Usage Data
NASA Astrophysics Data System (ADS)
Rathipriya, R.; Thangavel, K.; Bagyamani, J.
2011-07-01
Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms.
What is Particle Swarm optimization? Application to hydrogeophysics (Invited)
NASA Astrophysics Data System (ADS)
Fernández Martïnez, J.; García Gonzalo, E.; Mukerji, T.
2009-12-01
Inverse problems are generally ill-posed. This yields lack of uniqueness and/or numerical instabilities. These features cause local optimization methods without prior information to provide unpredictable results, not being able to discriminate among the multiple models consistent with the end criteria. Stochastic approaches to inverse problems consist in shifting attention to the probability of existence of certain interesting subsurface structures instead of "looking for a unique model". Some well-known stochastic methods include genetic algorithms and simulated annealing. A more recent method, Particle Swarm Optimization, is a global optimization technique that has been successfully applied to solve inverse problems in many engineering fields, although its use in geosciences is still limited. Like all stochastic methods, PSO requires reasonably fast forward modeling. The basic idea behind PSO is that each model searches the model space according to its misfit history and the misfit of the other models of the swarm. PSO algorithm can be physically interpreted as a damped spring-mass system. This physical analogy was used to define a whole family of PSO optimizers and to establish criteria, based on the stability of particle swarm trajectories, to tune the PSO parameters: inertia, local and global accelerations. In this contribution we show application to different low-cost hydrogeophysical inverse problems: 1) a salt water intrusion problem using Vertical Electrical Soundings, 2) the inversion of Spontaneous Potential data for groundwater modeling, 3) the identification of Cole-Cole parameters for Induced Polarization data. We show that with this stochastic approach we are able to answer questions related to risk analysis, such as what is the depth of the salt intrusion with a certain probability, or giving probabilistic bounds for the water table depth. Moreover, these measures of uncertainty are obtained with small computational cost and time, allowing us a very
Particle swarm optimization of ascent trajectories of multistage launch vehicles
NASA Astrophysics Data System (ADS)
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
Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method
NASA Astrophysics Data System (ADS)
Sun, Jun; Zhao, Ji; Wu, Xiaojun; Fang, Wei; Cai, Yujie; Xu, Wenbo
2010-06-01
Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.
NASA Astrophysics Data System (ADS)
Arya, Rajesh; Purey, Pradeep
2016-06-01
MW-generation rescheduling is being considered for voltage stability improvement under stressed operating condition. At times it can avoid voltage collapse. This paper describes an algorithm for determination of optimum MW-generation participation pattern for static voltage stability margin enhancement. The optimum search direction has been obtained by employing modified bare born particle swarm optimization technique. Optimum search direction is based on maximization of distance to point of collapse in generation space. Developed algorithm has been implemented on a standard 25 bus test system. Results obtained have been compared with those obtained using standard particle swarm optimization.
NASA Astrophysics Data System (ADS)
Arya, Rajesh; Purey, Pradeep
2015-06-01
MW-generation rescheduling is being considered for voltage stability improvement under stressed operating condition. At times it can avoid voltage collapse. This paper describes an algorithm for determination of optimum MW-generation participation pattern for static voltage stability margin enhancement. The optimum search direction has been obtained by employing modified bare born particle swarm optimization technique. Optimum search direction is based on maximization of distance to point of collapse in generation space. Developed algorithm has been implemented on a standard 25 bus test system. Results obtained have been compared with those obtained using standard particle swarm optimization.
Particle Swarm Optimization Approach in a Consignment Inventory System
NASA Astrophysics Data System (ADS)
Sharifyazdi, Mehdi; Jafari, Azizollah; Molamohamadi, Zohreh; Rezaeiahari, Mandana; Arshizadeh, Rahman
2009-09-01
Consignment Inventory (CI) is a kind of inventory which is in the possession of the customer, but is still owned by the supplier. This creates a condition of shared risk whereby the supplier risks the capital investment associated with the inventory while the customer risks dedicating retail space to the product. This paper considers both the vendor's and the retailers' costs in an integrated model. The vendor here is a warehouse which stores one type of product and supplies it at the same wholesale price to multiple retailers who then sell the product in independent markets at retail prices. Our main aim is to design a CI system which generates minimum costs for the two parties. Here a Particle Swarm Optimization (PSO) algorithm is developed to calculate the proper values. Finally a sensitivity analysis is performed to examine the effects of each parameter on decision variables. Also PSO performance is compared with genetic algorithm.
Electronic enclosure design using distributed particle swarm optimization
NASA Astrophysics Data System (ADS)
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.
Quantum-Behaved Particle Swarm Optimization with Chaotic Search
NASA Astrophysics Data System (ADS)
Yang, Kaiqiao; Nomura, Hirosato
The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.
Transport of Particle Swarms Through Variable Aperture Fractures
NASA Astrophysics Data System (ADS)
Boomsma, E.; Pyrak-Nolte, L. J.
2012-12-01
Particle transport through fractured rock is a key concern with the increased use of micro- and nano-size particles in consumer products as well as from other activities in the sub- and near surface (e.g. mining, industrial waste, hydraulic fracturing, etc.). While particle transport is often studied as the transport of emulsions or dispersions, particles may also enter the subsurface from leaks or seepage that lead to particle swarms. Swarms are drop-like collections of millions of colloidal-sized particles that exhibit a number of unique characteristics when compared to dispersions and emulsions. Any contaminant or engineered particle that forms a swarm can be transported farther, faster, and more cohesively in fractures than would be expected from a traditional dispersion model. In this study, the effects of several variable aperture fractures on colloidal swarm cohesiveness and evolution were studied as a swarm fell under gravity and interacted with the fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with (1) a uniform aperture, (2) a converging region followed by a uniform region (funnel shaped), (3) a uniform region followed by a diverging region (inverted funnel), and (4) a cast of a an induced fracture from a carbonate rock. All of the samples consisted of two blocks that measured 100 x 100 x 50 mm. The minimum separation between these blocks determined the nominal aperture (0.5 mm to 20 mm). During experiments a fracture was fully submerged in water and swarms were released into it. The swarms consisted of a dilute suspension of 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. The variation in fracture aperture controlled swarm behavior. Diverging apertures caused a sudden loss of confinement that resulted in a rapid change in the swarm's shape as well as a sharp increase in its velocity
NASA Astrophysics Data System (ADS)
Ghosh, Pradipta; Zafar, Hamim
Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This paper describes the synthesis method of linear array geometry with minimum side lobe level and null control by the Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search (DMSPSO) which optimizes the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. The results of the DMSPSO algorithm have been shown to meet or beat the results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA),General Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way. Three design examples are presented that illustrate the use of the DMSPSO algorithm, and the optimization goal in each example is easily achieved.
Particle Swarm Transport through Immiscible Fluid Layers in a Fracture
NASA Astrophysics Data System (ADS)
Teasdale, N. D.; Boomsma, E.; Pyrak-Nolte, L. J.
2011-12-01
Immiscible fluids occur either naturally (e.g. oil & water) or from anthropogenic processes (e.g. liquid CO2 & water) in the subsurface and complicate the transport of natural or engineered micro- or nano-scale particles. In this study, we examined the effect of immiscible fluids on the formation and evolution of particle swarms in a fracture. A particle swarm is a collection of colloidal-size particles in a dilute suspension that exhibits cohesive behavior. Swarms fall under gravity with a velocity that is greater than the settling velocity of a single particle. Thus a particle swarm of colloidal contaminants can potentially travel farther and faster in a fracture than expected for a dispersion or emulsion of colloidal particles. We investigated the formation, evolution, and break-up of colloidal swarms under gravity in a uniform aperture fracture as hydrophobic/hydrophyllic particle swarms move across an oil-water interface. A uniform aperture fracture was fabricated from two transparent acrylic rectangular prisms (100 mm x 50 mm x 100 mm) that are separated by 1, 2.5, 5, 10 or 50 mm. The fracture was placed, vertically, inside a glass tank containing a layer of pure silicone oil (polydimethylsiloxane) on distilled water. Along the length of the fracture, 30 mm was filled with oil and 70 mm with water. Experiments were conducted using silicone oils with viscosities of 5, 10, 100, or 1000 cSt. Particle swarms (5 μl) were comprised of a 1% concentration (by mass) of 25 micron glass beads (hydrophilic) suspended in a water drop, or a 1% concentration (by mass) of 3 micron polystyrene fluorescent beads (hydrophobic) suspended in a water drop. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera and by green (525 nm) LED arrays for illumination. Swarms were spherical and remained coherent as they fell through the oil because of the immiscibility of oil and water. However, as a swarm approached the oil-water interface, it
Particle Swarms in Fractures: Open Versus Partially Closed Systems
NASA Astrophysics Data System (ADS)
Boomsma, E.; Pyrak-Nolte, L. J.
2014-12-01
In the field, fractures may be isolated or connected to fluid reservoirs anywhere along the perimeter of a fracture. These boundaries affect fluid circulation, flow paths and communication with external reservoirs. The transport of drop like collections of colloidal-sized particles (particle swarms) in open and partially closed systems was studied. A uniform aperture synthetic fracture was constructed using two blocks (100 x 100 x 50 mm) of transparent acrylic placed parallel to each other. The fracture was fully submerged a tank filled with 100cSt silicone oil. Fracture apertures were varied from 5-80 mm. Partially closed systems were created by sealing the sides of the fracture with plastic film. The four boundary conditions study were: (Case 1) open, (Case 2) closed on the sides, (Case 3) closed on the bottom, and (Case 4) closed on both the sides and bottom of the fracture. A 15 μL dilute suspension of soda-lime glass particles in oil (2% by mass) were released into the fracture. Particle swarms were illuminated using a green (525 nm) LED array and imaged with a CCD camera. The presence of the additional boundaries modified the speed of the particle swarms (see figure). In Case 1, enhanced swarm transport was observed for a range of apertures, traveling faster than either very small or very large apertures. In Case 2, swarm velocities were enhanced over a larger range of fracture apertures than in any of the other cases. Case 3 shifted the enhanced transport regime to lower apertures and also reduced swarm speed when compared to Case 2. Finally, Case 4 eliminated the enhanced transport regime entirely. Communication between the fluid in the fracture and an external fluid reservoir resulted in enhanced swarm transport in Cases 1-3. The non-rigid nature of a swarm enables drag from the fracture walls to modify the swarm geometry. The particles composing a swarm reorganize in response to the fracture, elongating the swarm and maintaining its density. Unlike a
Isolated particle swarm optimization with particle migration and global best adoption
NASA Astrophysics Data System (ADS)
Tsai, Hsing-Chih; Tyan, Yaw-Yauan; Wu, Yun-Wu; Lin, Yong-Huang
2012-12-01
Isolated particle swarm optimization (IPSO) segregates particles into several sub-swarms in order to improve the ability of the global optimization. In this study, particle migration and global best adoption (gbest adoption) are used to improve IPSO. Particle migration allows particles to travel among sub-swarms, based on the fitness of the sub-swarms. The use of gbest adoption allows sub-swarms to peep at the gbest proportionally or probably after a certain number of iterations, i.e. gbest replacing, and gbest sharing, respectively. Three well-known benchmark functions are utilized to determine the parameter settings of the IPSO. Then, 13 benchmark functions are used to study the performance of the designed IPSO. Computational experience demonstrates that the designed IPSO is superior to the original version of particle swarm optimization (PSO) in terms of the accuracy and stability of the results, when isolation phenomenon, particle migration and gbest sharing are involved.
Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization
NASA Technical Reports Server (NTRS)
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.
Particle Swarm Social Model for Group Social Learning in Adaptive Environment
Cui, Xiaohui; Potok, Thomas E; Treadwell, Jim N; Patton, Robert M; Pullum, Laura L
2008-01-01
This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social learning for a dynamic environment. The research provides a platform for understanding and insights into knowledge discovery and strategic search in human self-organized social groups, such as insurgents or online communities.
NASA Astrophysics Data System (ADS)
Zheng, Genrang; Lin, ZhengChun
The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.
Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing
Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud
2015-01-01
This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309
Influence of Color Noise on Processing of Optical Signals by Swarm Intellect Algorithm
NASA Astrophysics Data System (ADS)
Galimullin, D. Z.; Sibgatullin, M. E.; Kamalova, D. I.; Salakhov, M. Kh.
2015-09-01
An approach based on stochastic particle swarm optimization was applied for the mathematical processing of spectral profiles with noise. To test the algorithm's stability with respect to noise, we used the noises with different values of the Hurst index which characterizes noise component from the point of view of prevalence of low-frequency or high-frequency component in it. The Hurst index varied from 0.1 to 0.9. An influence of color noise with level from 1 to 10% on processing of optical signals by particle swarm optimization algorithm was analyzed. This method is shown to be stable with respect to noise with the level of 10% if it's the Hurst index does not exceed the value of 0.5.
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
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
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
NASA Astrophysics Data System (ADS)
Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-01
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO-ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO-ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO-ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO-ACO is a very powerful tool for parameter estimation with high accuracy and low deviations.
Augmented Lagrangian Particle Swarm Optimization in Mechanism Design
NASA Astrophysics Data System (ADS)
Sedlaczek, Kai; Eberhard, Peter
The problem of optimizing nonlinear multibody systems is in general nonlinear and nonconvex. This is especially true for the dimensional synthesis process of rigid body mechanisms, where often only local solutions might be found with gradient-based optimization methods. An attractive alternative for solving such multimodal optimization problems is the Particle Swarm Optimization (PSO) algorithm. This stochastic solution technique allows a derivative-free search for a global solution without the need for any initial design. In this work, we present an extension to the basic PSO algorithm in order to solve the problem of dimensional synthesis with nonlinear equality and inequality constraints. It utilizes the Augmented Lagrange Multiplier Method in combination with an advanced non-stationary penalty function approach that does not rely on excessively large penalty factors for sufficiently accurate solutions. Although the PSO method is even able to solve nonsmooth and discrete problems, this augmented algorithm can additionally calculate accurate Lagrange multiplier estimates for differentiable formulations, which are helpful in the analysis process of the optimization results. We demonstrate this method and show its very promising applicability to the constrained dimensional synthesis process of rigid body mechanisms.
Pixelated source optimization for optical lithography via particle swarm optimization
NASA Astrophysics Data System (ADS)
Wang, Lei; Li, Sikun; Wang, Xiangzhao; Yan, Guanyong; Yang, Chaoxing
2016-01-01
Source optimization is one of the key techniques for achieving higher resolution without increasing the complexity of mask design. An efficient source optimization approach is proposed on the basis of particle swarm optimization. The pixelated sources are encoded into particles, which are evaluated by using the pattern error as the fitness function. Afterward, the optimization is implemented by updating the velocities and positions of these particles. This approach is demonstrated using three mask patterns, including a periodic array of contact holes, a vertical line/space design, and a complicated pattern. The pattern errors are reduced by 69.6%, 51.5%, and 40.3%, respectively. Compared with the source optimization approach via genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. Compared with the source optimization approach via gradient descent method, the proposed approach does not need the calculation of gradients, and it has a strong adaptation to various lithographic models, fitness functions, and resist models. The robustness of the proposed approach to initial sources is also verified.
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. PMID:25978493
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. PMID:25978493
NASA Astrophysics Data System (ADS)
Ma, Yanfang; Xu, Jiuping
2015-06-01
This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.
Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Perc, Matjaž
2011-01-01
We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction. PMID:21760906
Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization
Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee
2014-01-01
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. PMID:25285268
Particle swarm optimization with scale-free interactions.
Liu, Chen; Du, Wen-Bo; Wang, Wen-Xu
2014-01-01
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. PMID:24859007
Particle Swarm Optimization with Scale-Free Interactions
Liu, Chen; Du, Wen-Bo; Wang, Wen-Xu
2014-01-01
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. PMID:24859007
A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization
Bhattacharya, Sayantani; Konar, Amit; Das, Swagatam; Han, Sang Yong
2009-01-01
The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy. PMID:22303158
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
Han, Gaining; Fu, Weiping; Wang, Wen
2016-01-01
In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm
Han, Gaining; Fu, Weiping; Wang, Wen
2016-01-01
In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881
Parameter Identification of Chaotic Systems by a Novel Dual Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Jiang, Yunxiang; Lau, Francis C. M.; Wang, Shiyuan; Tse, Chi K.
In this paper, we propose a dual particle swarm optimization (PSO) algorithm for parameter identification of chaotic systems. We also consider altering the search range of individual particles adaptively according to their objective function value. We consider both noiseless and noisy channels between the original system and the estimation system. Finally, we verify the effectiveness of the proposed dual PSO method by estimating the parameters of the Lorenz system using two different data acquisition schemes. Simulation results show that the proposed method always outperforms the traditional PSO algorithm.
Optimal design of plate-fin heat exchangers by particle swarm optimization
NASA Astrophysics Data System (ADS)
Yousefi, M.; Darus, A. N.
2011-12-01
This study explores the application of Particle Swarm Optimization (PSO) for optimization of a cross-flow plate fin heat exchanger. Minimization total annual cost is the target of optimization. Seven design parameters, namely, heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length and number of hot side layers are selected as optimization variables. A case study from the literature proves the effectiveness of the proposed algorithm in case of achieving more accurate results.
A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment
Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao
2014-01-01
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855
A hybrid search algorithm for swarm robots searching in an unknown environment.
Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao
2014-01-01
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855
A novel bee swarm optimization algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush
2010-10-01
The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.
NASA Astrophysics Data System (ADS)
Fan, Shu-Kai S.; Chang, Ju-Ming; Chuang, Yu-Chiang
2015-06-01
Most real-world optimization problems involve the optimization task of more than a single objective function and, therefore, require a great amount of computational effort as the solution procedure is designed to anchor multiple compromised optimal solutions. Abundant multi-objective evolutionary algorithms (MOEAs) for multi-objective optimization have appeared in the literature over the past two decades. In this article, a new proposal by means of particle swarm optimization is addressed for solving multi-objective optimization problems. The proposed algorithm is constructed based on the concept of Pareto dominance, taking both the diversified search and empirical movement strategies into account. The proposed particle swarm MOEA with these two strategies is thus dubbed the empirical-movement diversified-search multi-objective particle swarm optimizer (EMDS-MOPSO). Its performance is assessed in terms of a suite of standard benchmark functions taken from the literature and compared to other four state-of-the-art MOEAs. The computational results demonstrate that the proposed algorithm shows great promise in solving multi-objective optimization problems.
Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization
NASA Technical Reports Server (NTRS)
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.
NASA Astrophysics Data System (ADS)
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
An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems.
Timmis, J; Ismail, A R; Bjerknes, J D; Winfield, A F T
2016-08-01
Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots. PMID:27178784
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
Support vector machine based on adaptive acceleration particle swarm optimization.
Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali
2014-01-01
Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584
Detecting compact galactic binaries using a hybrid swarm-based algorithm
NASA Astrophysics Data System (ADS)
Bouffanais, Yann; Porter, Edward K.
2016-03-01
Compact binaries in our galaxy are expected to be one of the main sources of gravitational waves for the future eLISA mission. During the mission lifetime, many thousands of galactic binaries should be individually resolved. However, the identification of the sources and the extraction of the signal parameters in a noisy environment are real challenges for data analysis. So far, stochastic searches have proven to be the most successful for this problem. In this work, we present the first application of a swarm-based algorithm combining Particle Swarm Optimization and Differential Evolution. These algorithms have been shown to converge faster to global solutions on complicated likelihood surfaces than other stochastic methods. We first demonstrate the effectiveness of the algorithm for the case of a single binary in a 1-mHz search bandwidth. This interesting problem gave the algorithm plenty of opportunity to fail, as it can be easier to find a strong noise peak rather than the signal itself. After a successful detection of a fictitious low-frequency source, as well as the verification binary RXJ 0806.3 +1527 , we then applied the algorithm to the detection of multiple binaries, over different search bandwidths, in the cases of low and mild source confusion. In all cases, we show that we can successfully identify the sources and recover the true parameters within a 99% credible interval.
NASA Astrophysics Data System (ADS)
Kar, Subhajit; Sharma, Kaushik Das
2010-10-01
System identification is a ubiquitous necessity for successful applications in various fields. The area of system identification can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. Particle Swarm Optimization (PSO) is a stochastic, population-based optimization algorithm and many variants of PSO have been developed since, including constrained, multi objective, and discrete or combinatorial versions and applications have been developed using PSO in many fields. The basic PSO algorithm implicitly utilizes a fully connected neighborhood topology. However, local neighborhood models have also been proposed for PSO long ago, where each particle has access to the information corresponding to its immediate neighbors, according to a certain swarm topology. In this local neighborhood model of PSO, particles have information only of their own and their nearest neighbors' bests, rather than that of the entire population of the swarm. In the present work basic PSO method and two of its local neighborhood variants are utilized for determining the optimal parameters of a dc motor. The result obtain from the simulation study demonstrate the usefulness of the proposed methodology.
Application of particle swarm techniques in sensor network configuration
NASA Astrophysics Data System (ADS)
Tillett, Jason; Yang, Shanchieh J.; Rao, Raghuveer; Sahin, Ferat
2005-05-01
A decentralized version of particle swarm optimization called the distributed particle swarm optimization (DPSO) approach is formulated and applied to the generation of sensor network configurations or topologies so that the deleterious effects of hidden nodes and asymmetric links on the performance of wireless sensor networks are minimized. Three different topology generation schemes, COMPOW, Cone-Based and the DPSO--based schemes are examined using ns-2. Simulations are executed by varying the node density and traffic rates. Results contrasting heterogeneous vs. homogeneous power reveal that an important metric for a sensor network topology may involve consideration of hidden nodes and asymmetric links, and demonstrate the effect of spatial reuse on the potency of topology generators.
Particle Swarm Imaging (PSIM) - Innovative Gamma-Ray Assay - 13497
Parvin, Daniel; Clarke, Sean; Humes, Sarah J.
2013-07-01
Particle Swarm Imaging is an innovative technique used to perform quantitative gamma-ray assay. The innovation overcomes some of the difficulties associated with the accurate measurement and declaration of measurement uncertainties of radionuclide inventories within waste items when the distribution of activity is unknown. Implementation requires minimal equipment, with field measurements and results obtained using only a single electrically cooled HRGS gamma-ray detector. Examples of its application in the field are given in this paper. (authors)
Particle swarm optimization for complex nonlinear optimization problems
NASA Astrophysics Data System (ADS)
Alexandridis, Alex; Famelis, Ioannis Th.; Tsitouras, Charalambos
2016-06-01
This work presents the application of a technique belonging to evolutionary computation, namely particle swarm optimization (PSO), to complex nonlinear optimization problems. To be more specific, a PSO optimizer is setup and applied to the derivation of Runge-Kutta pairs for the numerical solution of initial value problems. The effect of critical PSO operational parameters on the performance of the proposed scheme is thoroughly investigated.
SwarmPS: rapid, semi-automated single particle selection software.
Woolford, David; Ericksson, Geoffery; Rothnagel, Rosalba; Muller, David; Landsberg, Michael J; Pantelic, Radosav S; McDowall, Alasdair; Pailthorpe, Bernard; Young, Paul R; Hankamer, Ben; Banks, Jasmine
2007-01-01
Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that approximately 10(4)-10(5) particle projections are required to attain a 3A resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present Swarm(PS), a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (approximately 10(2)), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. Swarm(PS) is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites. PMID:16774837
NASA Astrophysics Data System (ADS)
Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang
2014-11-01
In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.
Application of particle swarm optimization to interpret Rayleigh wave dispersion curves
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments
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.
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
Yan, Danping; Lu, Yongzhong; Levy, David
2015-01-01
Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators. PMID:26039090
Biochemical systems identification by a random drift particle swarm optimization approach
2014-01-01
Background Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study. PMID:25078435
Cui, Xiaohui; Potok, Thomas E
2007-01-01
A swarm based social adaptive model is proposed to model multiple insurgent groups?strategy searching in a dynamic changed environment. This report presents a pilot study on using the particle swarm modeling, a widely used non-linear optimal tool, to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamic environment and to provide insight and understanding of insurgent group strategic adaptation.
Early Mission Design of Transfers to Halo Orbits via Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Abraham, Andrew J.; Spencer, David B.; Hart, Terry J.
2016-03-01
Particle Swarm Optimization (PSO) is used to prune the search space of a low-thrust trajectory transfer from a high-altitude, Earth orbit to a Lagrange point orbit in the Earth-Moon system. Unlike a gradient based approach, this evolutionary PSO algorithm is capable of avoiding undesirable local minima. The PSO method is extended to a "local" version and uses a two dimensional search space that is capable of reducing the computation run-time by an order of magnitude when compared with published work. A technique for choosing appropriate PSO parameters is demonstrated and an example of an optimized trajectory is discussed.
Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun
2016-01-01
Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656
Yau, Her-Terng; Hung, Tzu-Hsiang; Hsieh, Chia-Chun
2012-01-01
This study used the complex dynamic characteristics of chaotic systems and Bluetooth to explore the topic of wireless chaotic communication secrecy and develop a communication security system. The PID controller for chaos synchronization control was applied, and the optimum parameters of this PID controller were obtained using a Particle Swarm Optimization (PSO) algorithm. Bluetooth was used to realize wireless transmissions, and a chaotic wireless communication security system was developed in the design concept of a chaotic communication security system. The experimental results show that this scheme can be used successfully in image encryption. PMID:22969355
Early Mission Design of Transfers to Halo Orbits via Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Abraham, Andrew J.; Spencer, David B.; Hart, Terry J.
2016-06-01
Particle Swarm Optimization (PSO) is used to prune the search space of a low-thrust trajectory transfer from a high-altitude, Earth orbit to a Lagrange point orbit in the Earth-Moon system. Unlike a gradient based approach, this evolutionary PSO algorithm is capable of avoiding undesirable local minima. The PSO method is extended to a "local" version and uses a two dimensional search space that is capable of reducing the computation run-time by an order of magnitude when compared with published work. A technique for choosing appropriate PSO parameters is demonstrated and an example of an optimized trajectory is discussed.
Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun
2016-01-01
Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656
Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine
Yu, Zhang; Yang, Xiaomei
2013-01-01
By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency. PMID:24294135
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
Han, Wenhua; Xu, Jun; Wang, Ping; Tian, Guiyun
2014-01-01
In this paper, efficient managing particle swarm optimization (EMPSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signal. In the proposed EMPSO, in order to strengthen exchange of information among particles, particle pair model was built. For more efficient searching when facing different landscapes of problems, velocity updating scheme including three velocity updating models was also proposed. In addition, for more chances to search optimum solution out, automatic particle selection for re-initialization was implemented. The optimization results of six benchmark functions show EMPSO performs well when optimizing 100-D problems. The defect simulation results demonstrate that the inversing technique based on EMPSO outperforms the one based on self-learning particle swarm optimizer (SLPSO), and the estimated profiles are still close to the desired profiles with the presence of low noise in MFL signal. The results estimated from real MFL signal by EMPSO-based inversing technique also indicate that the algorithm is capable of providing an accurate solution of the defect profile with real signal. Both the simulation results and experiment results show the computing time of the EMPSO-based inversing technique is reduced by 20%-30% than that of the SLPSO-based inversing technique. PMID:24926693
Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach
Yan, Danping; Lu, Yongzhong; Levy, David
2015-01-01
Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles’ local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators. PMID:26039090
Multi-terminal pipe routing by Steiner minimal tree and particle swarm optimisation
NASA Astrophysics Data System (ADS)
Liu, Qiang; Wang, Chengen
2012-08-01
Computer-aided design of pipe routing is of fundamental importance for complex equipments' developments. In this article, non-rectilinear branch pipe routing with multiple terminals that can be formulated as a Euclidean Steiner Minimal Tree with Obstacles (ESMTO) problem is studied in the context of an aeroengine-integrated design engineering. Unlike the traditional methods that connect pipe terminals sequentially, this article presents a new branch pipe routing algorithm based on the Steiner tree theory. The article begins with a new algorithm for solving the ESMTO problem by using particle swarm optimisation (PSO), and then extends the method to the surface cases by using geodesics to meet the requirements of routing non-rectilinear pipes on the surfaces of aeroengines. Subsequently, the adaptive region strategy and the basic visibility graph method are adopted to increase the computation efficiency. Numeral computations show that the proposed routing algorithm can find satisfactory routing layouts while running in polynomial time.
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. PMID:25204003
Extended particle swarm optimisation method for folding protein on triangular lattice.
Guo, Yuzhen; Wu, Zikai; Wang, Ying; Wang, Yong
2016-02-01
In this study, the authors studied the protein structure prediction problem by the two-dimensional hydrophobic-polar model on triangular lattice. Particularly the non-compact conformation was modelled to fold the amino acid sequence into a relatively larger triangular lattice, which is more biologically realistic and significant than the compact conformation. Then protein structure prediction problem was abstracted to match amino acids to lattice points. Mathematically, the problem was formulated as an integer programming and they transformed the biological problem into an optimisation problem. To solve this problem, classical particle swarm optimisation algorithm was extended by the single point adjustment strategy. Compared with square lattice, conformations on triangular lattice are more flexible in several benchmark examples. They further compared the authors' algorithm with hybrid of hill climbing and genetic algorithm. The results showed that their method was more effective in finding solution with lower energy and less running time. PMID:26816397
Solving initial and boundary value problems using learning automata particle swarm optimization
NASA Astrophysics Data System (ADS)
Nemati, Kourosh; Mariyam Shamsuddin, Siti; Darus, Maslina
2015-05-01
In this article, the particle swarm optimization (PSO) algorithm is modified to use the learning automata (LA) technique for solving initial and boundary value problems. A constrained problem is converted into an unconstrained problem using a penalty method to define an appropriate fitness function, which is optimized using the LA-PSO method. This method analyses a large number of candidate solutions of the unconstrained problem with the LA-PSO algorithm to minimize an error measure, which quantifies how well a candidate solution satisfies the governing ordinary differential equations (ODEs) or partial differential equations (PDEs) and the boundary conditions. This approach is very capable of solving linear and nonlinear ODEs, systems of ordinary differential equations, and linear and nonlinear PDEs. The computational efficiency and accuracy of the PSO algorithm combined with the LA technique for solving initial and boundary value problems were improved. Numerical results demonstrate the high accuracy and efficiency of the proposed method.
On the application of Particle Swarm Optimization strategies on Scholte-wave inversion
NASA Astrophysics Data System (ADS)
Wilken, D.; Rabbel, W.
2012-07-01
We investigate different aspects concerning the application of swarm intelligence optimization to the inversion of Scholte-wave phase-slowness frequency (p-f) spectra with respect to shear wave velocity structure. Besides human influence due to the dependence on a priori information for starting models and interpretation of p-f spectra as well as noise, the model resolution of the inversion problem is strongly influenced by the multimodality of the misfit function. We thus tested the efficiency of global, stochastic optimization approaches with focus on swarm intelligence methods that can explore the multiple minima of the misfit function. A comparison among different PSO schemes by applying them to synthetic Scholte-wave spectra led to a hybrid of Particle Swarm Optimization and Downhill Simplex providing the best resolution of inverted shear wave velocity depth models. The results showed a very low spread of best fitting solutions of 7 per cent in shear wave velocity and an average of 9 per cent for noisy synthetic data and a very good fit to the true synthetic model used for computation of the input data. To classify this method we also compared the probability of finding a good fit in synthetic spectra inversion with that of Evolutionary Algorithm, Simulated Annealing, Neighbourhood Algorithm and Artificial Bee Colony algorithm. Again the hybrid optimization scheme showed its predominance. The usage of stochastic algorithms furthermore allowed a new way of misfit definition in terms of dispersion curve slowness residuals making the inversion scheme independent on Scholte-wave mode identification and allowing joint inversion of fundamental mode and higher mode information. Finally we used the hybrid optimization scheme and the misfit calculation for the inversion of 2-D shear wave velocity profiles from two locations in the North- and Baltic Sea. The models show acceptable resolution and a very good structural correlation to high resolution reflection seismic
NASA Astrophysics Data System (ADS)
Fan, Shu-Kai S.; Chang, Ju-Ming
2010-05-01
This article presents a novel parallel multi-swarm optimization (PMSO) algorithm with the aim of enhancing the search ability of standard single-swarm PSOs for global optimization of very large-scale multimodal functions. Different from the existing multi-swarm structures, the multiple swarms work in parallel, and the search space is partitioned evenly and dynamically assigned in a weighted manner via the roulette wheel selection (RWS) mechanism. This parallel, distributed framework of the PMSO algorithm is developed based on a master-slave paradigm, which is implemented on a cluster of PCs using message passing interface (MPI) for information interchange among swarms. The PMSO algorithm handles multiple swarms simultaneously and each swarm performs PSO operations of its own independently. In particular, one swarm is designated for global search and the others are for local search. The first part of the experimental comparison is made among the PMSO, standard PSO, and two state-of-the-art algorithms (CTSS and CLPSO) in terms of various un-rotated and rotated benchmark functions taken from the literature. In the second part, the proposed multi-swarm algorithm is tested on large-scale multimodal benchmark functions up to 300 dimensions. The results of the PMSO algorithm show great promise in solving high-dimensional problems.
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
Luo, Xiongbiao E-mail: Ying.Wan@student.uts.edu.au; Wan, Ying E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian
2015-04-15
Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was
Solving constrained optimization problems with hybrid particle swarm optimization
NASA Astrophysics Data System (ADS)
Zahara, Erwie; Hu, Chia-Hsin
2008-11-01
Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.
Order-2 Stability Analysis of Particle Swarm Optimization.
Liu, Qunfeng
2015-01-01
Several stability analyses and stable regions of particle swarm optimization (PSO) have been proposed before. The assumption of stagnation and different definitions of stability are adopted in these analyses. In this paper, the order-2 stability of PSO is analyzed based on a weak stagnation assumption. A new definition of stability is proposed and an order-2 stable region is obtained. Several existing stable analyses for canonical PSO are compared, especially their definitions of stability and the corresponding stable regions. It is shown that the classical stagnation assumption is too strict and not necessary. Moreover, among all these definitions of stability, it is shown that our definition requires the weakest conditions, and additional conditions bring no benefit. Finally, numerical experiments are reported to show that the obtained stable region is meaningful. A new parameter combination of PSO is also shown to be good, even better than some known best parameter combinations. PMID:24738856
A Triangle Mesh Standardization Method Based on Particle Swarm Optimization
Duan, Liming; Bai, Yang; Wang, Haoyu; Shao, Hui; Zhong, Siyang
2016-01-01
To enhance the triangle quality of a reconstructed triangle mesh, a novel triangle mesh standardization method based on particle swarm optimization (PSO) is proposed. First, each vertex of the mesh and its first order vertices are fitted to a cubic curve surface by using least square method. Additionally, based on the condition that the local fitted surface is the searching region of PSO and the best average quality of the local triangles is the goal, the vertex position of the mesh is regulated. Finally, the threshold of the normal angle between the original vertex and regulated vertex is used to determine whether the vertex needs to be adjusted to preserve the detailed features of the mesh. Compared with existing methods, experimental results show that the proposed method can effectively improve the triangle quality of the mesh while preserving the geometric features and details of the original mesh. PMID:27509129
NASA Astrophysics Data System (ADS)
Lazzús, J. A.; López-Caraballo, C. H.; Rojas, P.; Salfate, I.; Rivera, M.; Palma-Chilla, L.
2016-05-01
In this study, an artificial neural network was optimized with particle swarm algorithm and trained to predict the geomagmetic DST index one hour ahead using the past values of DST and auroral electrojet indices. The results show that the proposed neural network model can be properly trained for predicting of DST(t + 1) with acceptable accuracy, and that the geomagnetic indices used have influential effects on the good training and predicting capabilities of the chosen network.
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features
Amudha, P.; Karthik, S.; Sivakumari, S.
2015-01-01
Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.
Amudha, P; Karthik, S; Sivakumari, S
2015-01-01
Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625
hydroPSO: A Versatile Particle Swarm Optimisation R Package for Calibration of Environmental Models
NASA Astrophysics Data System (ADS)
Zambrano-Bigiarini, M.; Rojas, R.
2012-04-01
Particle Swarm Optimisation (PSO) is a recent and powerful population-based stochastic optimisation technique inspired by social behaviour of bird flocking, which shares similarities with other evolutionary techniques such as Genetic Algorithms (GA). In PSO, however, each individual of the population, known as particle in PSO terminology, adjusts its flying trajectory on the multi-dimensional search-space according to its own experience (best-known personal position) and the one of its neighbours in the swarm (best-known local position). PSO has recently received a surge of attention given its flexibility, ease of programming, low memory and CPU requirements, and efficiency. Despite these advantages, PSO may still get trapped into sub-optimal solutions, suffer from swarm explosion or premature convergence. Thus, the development of enhancements to the "canonical" PSO is an active area of research. To date, several modifications to the canonical PSO have been proposed in the literature, resulting into a large and dispersed collection of codes and algorithms which might well be used for similar if not identical purposes. In this work we present hydroPSO, a platform-independent R package implementing several enhancements to the canonical PSO that we consider of utmost importance to bring this technique to the attention of a broader community of scientists and practitioners. hydroPSO is model-independent, allowing the user to interface any model code with the calibration engine without having to invest considerable effort in customizing PSO to a new calibration problem. Some of the controlling options to fine-tune hydroPSO are: four alternative topologies, several types of inertia weight, time-variant acceleration coefficients, time-variant maximum velocity, regrouping of particles when premature convergence is detected, different types of boundary conditions and many others. Additionally, hydroPSO implements recent PSO variants such as: Improved Particle Swarm
Thermal and athermal three-dimensional swarms of self-propelled particles.
Nguyen, Nguyen H P; Jankowski, Eric; Glotzer, Sharon C
2012-07-01
Swarms of self-propelled particles exhibit complex behavior that can arise from simple models, with large changes in swarm behavior resulting from small changes in model parameters. We investigate the steady-state swarms formed by self-propelled Morse particles in three dimensions using molecular dynamics simulations optimized for graphics processing units. We find a variety of swarms of different overall shape assemble spontaneously and that for certain Morse potential parameters at most two competing structures are observed. We report a rich "phase diagram" of athermal swarm structures observed across a broad range of interaction parameters. Unlike the structures formed in equilibrium self-assembly, we find that the probability of forming a self-propelled swarm can be biased by the choice of initial conditions. We investigate how thermal noise influences swarm formation and demonstrate ways it can be exploited to reconfigure one swarm into another. Our findings validate and extend previous observations of self-propelled Morse swarms and highlight open questions for predictive theories of nonequilibrium self-assembly. PMID:23005397
Mutation-Based Artificial Fish Swarm Algorithm for Bound Constrained Global Optimization
NASA Astrophysics Data System (ADS)
Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2011-09-01
The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods.
NASA Astrophysics Data System (ADS)
Dutta, Rajdeep; Ganguli, Ranjan; Mani, V.
2011-10-01
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.
Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block.
Kora, Padmavathi; Kalva, Sri Ramakrishna
2015-01-01
Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging-Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg-Marquardt Neural Network classifier. PMID:26361582
NASA Astrophysics Data System (ADS)
Pekşen, Ertan; Yas, Türker; Kıyak, Alper
2014-09-01
We examine the one-dimensional direct current method in anisotropic earth formation. We derive an analytic expression of a simple, two-layered anisotropic earth model. Further, we also consider a horizontally layered anisotropic earth response with respect to the digital filter method, which yields a quasi-analytic solution over anisotropic media. These analytic and quasi-analytic solutions are useful tests for numerical codes. A two-dimensional finite difference earth model in anisotropic media is presented in order to generate a synthetic data set for a simple one-dimensional earth. Further, we propose a particle swarm optimization method for estimating the model parameters of a layered anisotropic earth model such as horizontal and vertical resistivities, and thickness. The particle swarm optimization is a naturally inspired meta-heuristic algorithm. The proposed method finds model parameters quite successfully based on synthetic and field data. However, adding 5 % Gaussian noise to the synthetic data increases the ambiguity of the value of the model parameters. For this reason, the results should be controlled by a number of statistical tests. In this study, we use probability density function within 95 % confidence interval, parameter variation of each iteration and frequency distribution of the model parameters to reduce the ambiguity. The result is promising and the proposed method can be used for evaluating one-dimensional direct current data in anisotropic media.
Chen, Qiang; Chen, Yunhao; Jiang, Weiguo
2016-01-01
In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285
The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm
NASA Astrophysics Data System (ADS)
Zhao, Xinying; Hu, Yihua; Gu, Youlin; Li, Le
2015-10-01
Microorganism aggregated particle swarm, which is quite an important composition of complex media environment, can be developed as a new kind of infrared functional materials. Current researches mainly focus on the optical properties of single microorganism particle. As for the swarm, especially the microorganism aggregated particle swarm, a more accurate simulation model should be proposed to calculate its extinction effect. At the same time, certain parameters deserve to be discussed, which helps to better develop the microorganism aggregated particle swarm as a new kind of infrared functional materials. In this paper, take Aspergillus Niger spore as an example. On the one hand, a new calculation model is established. Firstly, the cluster-cluster aggregation (CCA) model is used to simulate the structure of Aspergillus Niger spore aggregated particle. Secondly, the single scattering extinction parameters for Aspergillus Niger spore aggregated particle are calculated by using the discrete dipole approximation (DDA) method. Thirdly, the transmittance of Aspergillus Niger spore aggregated particle swarm is simulated by using Monte Carlo method. On the other hand, based on the model proposed above, what influences can wavelength causes has been studied, including the spectral distribution of scattering intensity of Aspergillus Niger spore aggregated particle and the infrared spectral transmittance of the aggregated particle swarm within the range of 8～14μm incident infrared wavelengths. Numerical results indicate that the scattering intensity of Aspergillus Niger spore aggregated particle reduces with the increase of incident wavelengths at each scattering angle. Scattering energy mainly concentrates on the scattering angle between 0～40°, forward scattering has an obvious effect. In addition, the infrared transmittance of Aspergillus Niger spore aggregated particle swarm goes up with the increase of incident wavelengths. However, some turning points of the trend
Log-linear model based behavior selection method for artificial fish swarm algorithm.
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
Discrete particle swarm optimization for identifying community structures in signed social networks.
Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng
2014-10-01
Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. PMID:24856248
NASA Astrophysics Data System (ADS)
Wang, Xinsheng; Wang, Chenxu; Yu, Mingyan
2016-07-01
In this paper, we propose a generalised sub-block structure preservation interconnect model order reduction (MOR) technique based on the swarm intelligence method, that is, particle swarm optimisation (PSO). The swarm intelligence-based structure preservation MOR can be used for a standard model as a criterion for different structure preservation interconnect MOR methods. In the proposed technique, the PSO method is used for predicting the unknown elements of structure-preserving reduced-order modelling of interconnect circuits. The prediction is based on minimising the difference of transform function between the original full-order and desired reduced-order systems maintaining the full-order structure in the reduced-order model. The proposed swarm-intelligence-based structure-preserving MOR method is compared with published work on structure preservation MOR SPRIM techniques. Simulation and synthesis results verify the accuracy and validity of the new structure-preserving MOR technique.
A particle swarm optimization variant with an inner variable learning strategy.
Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin
2014-01-01
Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge. PMID:24587746
Chou, Sheng-Kai; Jiau, Ming-Kai; Huang, Shih-Chia
2016-08-01
The growing ubiquity of vehicles has led to increased concerns about environmental issues. These concerns can be mitigated by implementing an effective carpool service. In an intelligent carpool system, an automated service process assists carpool participants in determining routes and matches. It is a discrete optimization problem that involves a system-wide condition as well as participants' expectations. In this paper, we solve the carpool service problem (CSP) to provide satisfactory ride matches. To this end, we developed a particle swarm carpool algorithm based on stochastic set-based particle swarm optimization (PSO). Our method introduces stochastic coding to augment traditional particles, and uses three terminologies to represent a particle: 1) particle position; 2) particle view; and 3) particle velocity. In this way, the set-based PSO (S-PSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOs-S-PSO and binary PSO (BPSO)-and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a real-world metropolis. We observed that the S-PSO outperformed the BPSO and the GA thoroughly. Moreover, our method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP. PMID:26890944
NASA Astrophysics Data System (ADS)
Verma, Harish Kumar; Jain, Cheshta
2015-07-01
In this article, a hybrid algorithm of particle swarm optimization (PSO) with statistical parameter (HSPSO) is proposed. Basic PSO for shifted multimodal problems have low searching precision due to falling into a number of local minima. The proposed approach uses statistical characteristics to update the velocity of the particle to avoid local minima and help particles to search global optimum with improved convergence. The performance of the newly developed algorithm is verified using various standard multimodal, multivariable, shifted hybrid composition benchmark problems. Further, the comparative analysis of HSPSO with variants of PSO is tested to control frequency of hybrid renewable energy system which comprises solar system, wind system, diesel generator, aqua electrolyzer and ultra capacitor. A significant improvement in convergence characteristic of HSPSO algorithm over other variants of PSO is observed in solving benchmark optimization and renewable hybrid system problems.
NASA Astrophysics Data System (ADS)
Saada, Mohamed M.; Arafa, Mustafa H.; Nassef, Ashraf O.
2013-06-01
The use of vibration-based techniques in damage identification has recently received considerable attention in many engineering disciplines. While various damage indicators have been proposed in the literature, those relying only on changes in the natural frequencies are quite appealing since these quantities can conveniently be acquired. Nevertheless, the use of natural frequencies in damage identification is faced with many obstacles, including insensitivity and non-uniqueness issues. The aim of this article is to develop a viable damage identification scheme based only on changes in the natural frequencies and to attempt to overcome the challenges typically encountered. The proposed methodology relies on building a finite element model (FEM) of the structure under investigation. An improved particle swarm optimization algorithm is proposed to facilitate updating the FEM in accordance with experimentally determined natural frequencies in order to predict the damage location and extent. The method is tested on beam structures and was shown to be an effective tool for damage identification.
Infrared face recognition based on binary particle swarm optimization and SVM-wrapper model
NASA Astrophysics Data System (ADS)
Xie, Zhihua; Liu, Guodong
2015-10-01
Infrared facial imaging, being light- independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. Robust feature selection and representation is a key issue for infrared face recognition research. This paper proposes a novel infrared face recognition method based on local binary pattern (LBP). LBP can improve the robust of infrared face recognition under different environment situations. How to make full use of the discriminant ability in LBP patterns is an important problem. A search algorithm combination binary particle swarm with SVM is used to find out the best discriminative subset in LBP features. Experimental results show that the proposed method outperforms traditional LBP based infrared face recognition methods. It can significantly improve the recognition performance of infrared face recognition.
Energy and operation management of a microgrid using particle swarm optimization
NASA Astrophysics Data System (ADS)
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.
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. PMID:24273145
NASA Astrophysics Data System (ADS)
Chang, Yau-Zen; Hou, Jung-Fu; Tsao, Yi Hsiang; Lee, Shih-Tseng
2011-09-01
This paper proposes a scheme for finding the correspondence between uniformly spaced locations on the images of human face captured from different viewpoints at the same instant. The correspondence is dedicated for 3D reconstruction to be used in the registration procedure for neurosurgery where the exposure to projectors must be seriously restricted. The approach utilizes structured light to enhance patterns on the images and is initialized with the scale-invariant feature transform (SIFT). Successive locations are found according to spatial order using a parallel version of the particle swarm optimization algorithm. Furthermore, false locations are singled out for correction by searching for outliers from fitted curves. Case studies show that the scheme is able to correctly generate 456 evenly spaced 3D coordinate points in 23 seconds from a single shot of projected human face using a PC with 2.66 GHz Intel Q9400 CPU and 4GB RAM.
Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.
Hsu, Wei-Yen
2013-12-01
In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications. PMID:24156669
Joint inversion of TEM and DC in roadway advanced detection based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Cheng, Jiulong; Li, Fei; Peng, Suping; Sun, Xiaoyun; Zheng, Jing; Jia, Jizhe
2015-12-01
Transient electromagnetic method (TEM)and direct current method (DC)are two key widely applied methods for practical roadway detection, but both have their limitations. To take the advantage of each method, a synchronous nonlinear joint inversion method is proposed based on TEM and DC by using particle swarm optimization (PSO)algorithm. Firstly, a model with double low resistance anomaly and interference is constructed to test the performance of the method. Then the independent inversion and joint inversion are calculated by using the model built above. It is demonstrated that the joint inversion helped in improving the interpretation of the data to get better results. It is because that the suppression of interference and separation of the resistivity anomalies ahead and the back of the roadway working face using the proposed method. Finally, the proposed method was successfully used in a coalmine in Huainan coalfield in east China to demonstrate its practical usefulness.