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Swarm Optimization (swarm + optimization)
Kinds of Swarm Optimization Selected AbstractsMultimode Project Scheduling Based on Particle Swarm OptimizationCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 2 2006Hong Zhang This article introduces a methodology for solving the MRCPSP based on particle swarm optimization (PSO) that has not been utilized for this and other construction-related problems. The framework of the PSO-based methodology is developed. A particle representation formulation is proposed to represent the potential solution to the MRCPSP in terms of priority combination and mode combination for activities. Each particle-represented solution should be checked against the nonrenewable resource infeasibility and will be handled by adjusting the mode combination. The feasible particle-represented solution is transformed to a schedule through a serial generation scheme. Experimental analyses are presented to investigate the performance of the proposed methodology. [source] Integrative optimization by RBF network and particle swarm optimizationELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 12 2009Satoshi Kitayama Abstract This paper presents a method for the integrative optimization system. Recently, many methods for global optimization have been proposed. The objective of these methods is to find a global minimum of nonconvex function. However, large numbers of function evaluations are required, in general. We utilize the response surface method to approximate function space to reduce the function evaluations. The response surface method is constructed from sampling points. The RBF Network, which is one of the neural networks, is utilized to approximate the function space. Then Particle Swarm Optimization (PSO) is applied to the response surface. The proposed system consists of three parts: (Part 1) generation of the sampling points, (Part 2) construction of response surface by RBF Network, (Part 3) optimization by PSO. By iterating these three parts, it is expected that the approximate global minimum of nonconvex function can be obtained with a small number of function evaluations. Through numerical examples, the effectiveness and validity are examined. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(12): 31,42, 2009; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecj.10187 [source] A design for robust power system stabilizer by means of H, control and particle swarm optimization methodELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 8 2008Yoshifumi Zoka Abstract This paper proposes two types of PSS design methods that take into account robustness for comparably large power systems. The first one is a design method based on , control theory and the second one is a parameter determination method for a standard PSS by using Particle Swarm Optimization (PSO). In order to deal with large-scale systems, a reduced model is developed to get the target system which preserves major oscillation modes only. The major oscillation modes are selected by using the residue concept, and the PSS is designed based on the target system. In order to verify effectiveness, the proposed methods are compared with the other previously proposed method based on a Genetic Algorithm (GA) through many numerical simulations. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(8): 34,43, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10132 [source] An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfigurationEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 5 2010Taher Niknam Abstract A new formulation based on norm2 method for the multi objective distribution feeder reconfiguration (DFR) is introduced in order to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and to balance the loads on the feeders. In the proposed method, since the objective functions are not the same and commensurable, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. The status of the tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a multi objective and non-differentiable optimization problem so a hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called HPSO, is proposed to solve it. The feasibility of the HPSO algorithm and the proposed DFR is demonstrated and compared with the solutions obtained by other approaches and evolutionary methods such as genetic algorithm (GA), ACO and the original PSO, over different distribution test systems. Copyright © 2009 John Wiley & Sons, Ltd. [source] Integrated Optimization by Multi-Objective Particle Swarm OptimizationIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 1 2010Masaru Kawarabayashi Student Member Abstract In this letter, integrated optimization system, a new framework of practical optimization, is expanded to multi-objective optimization problem. This system is used in order to reduce the number of accesses to a simulator. On the basis of simulation results using some typical benchmark problems, it is shown that the proposed integrated optimization system enables to obtain relatively good Pareto solutions with drastic reduction in the number of function calls for evaluating the performance index values of systems. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Optimal Thermal Unit Commitment Integrated with Renewable Energy Sources Using Advanced Particle Swarm OptimizationIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 5 2009Shantanu Chakraborty Student member Abstract This paper presents a methodology for solving generation planning problem for thermal units integrated with wind and solar energy systems. The renewable energy sources are included in this model due to their low electricity cost and positive effect on environment. The generation planning problem also known by unit commitment problem is solved by a genetic algorithm operated improved binary particle swarm optimization (PSO) algorithm. Unlike trivial PSO, this algorithm runs the refinement process through the solutions within multiple populations. Some genetic algorithm operators such as crossover, elitism, and mutation are stochastically applied within the higher potential solutions to generate new solutions for next population. The PSO includes a new variable for updating velocity in accordance with population best along with conventional particle best and global best. The algorithm performs effectively in various sized thermal power system with equivalent solar and wind energy system and is able to produce high quality (minimized production cost) solutions. The solution model is also beneficial for reconstructed deregulated power system. The simulation results show the effectiveness of this algorithm by comparing the outcome with several established methods. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Particle Swarm Optimization with Diverse ParametersIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 4 2008Mari Takei Student Member Abstract This paper proposes a particle swarm optimization (PSO) with diverse parameters that achieve an appropriate balance between diversification and intensification during the search based on numerical stability analysis. Numerical experiments using seven typical benchmark problems with 100, 500, and 1000 dimensions validate the robustness and search capabilities of the proposed PSO. © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithmsMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 10 2007S. K. Goudos Abstract A multiobjective evolutionary technique is applied to design dielectric filters useful in microwave communications technology. The optimal geometry of the filters is derived by utilizing two different multiobjective optimization algorithms. The first one is the Nondominated Sorting Genetic Algorithm-II (NSGA-II), which is a popular multiobjective genetic algorithm. The second algorithm is based on multiobjective Particle Swarm Optimization with fitness sharing (MOPSO-fs). MOPSO-fs algorithm is a novel Pareto PSO algorithm that produces the Pareto front in a fast and efficient way. In the present work, MOPSO-fs is compared with NSGA-II to optimize the geometry of the filters under specific requirements concerning the frequency response of the filters. Several examples are studied to exhibit the efficiency of the multiobjective evolutionary optimizers and also the ability of the technique to derive optimal structures that can be used in practice. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2324,2329, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22755 [source] Quantitative Structure,Activity Relationship Studies for the Binding Affinities of Imidazobenzodiazepines for the ,6 Benzodiazepine Receptor Isoform Utilizing Optimized Blockwise Variable Combination by Particle Swarm Optimization for Partial Least Squares ModelingMOLECULAR INFORMATICS, Issue 1 2007Leqian Hu Abstract Binding affinities of a series of substituted imidazobenzodiazepines for the ,6 Benzodiazepine Receptor (BzR) isoform are investigated by the Optimized Blockwise Variable Combination (OBVC) by Particle Swarm Optimization (PSO) based on Partial Least Squares (PLS) modeling. The QSAR analysis result showed that MolRef, AlogP, MRCM**-3, Rotatable bonds (Rotlbonds), Hydrogen Bond Acceptors (Hbond acceptor), five Jurs descriptors, two Shadow indices descriptors and principal moment of inertia are the most important descriptors among all the investigated descriptors. One can change the molar refractivity, the polar interactions between molecules, the shape of the molecules, the principal moments of inertia about the principal axes of a molecule, the hydrophobic character of the molecule, the number of Rotlbonds and Hbond acceptors of the compounds to adjust the binding affinities of imidazobenzodiazepine for the ,6 BzR isoform. The Quantitative Structure,Activity Relationship (QSAR) analysis result was also compared with MLR, PLS, and hierarchical PLS algorithms. It has been demonstrated that OBVC by PSO for PLS modeling shows satisfactory performance in the QSAR analysis. [source] Research Article: pso@autodock: A Fast Flexible Molecular Docking Program Based on Swarm IntelligenceCHEMICAL BIOLOGY & DRUG DESIGN, Issue 6 2007Vigneshwaran Namasivayam On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program autodock, we present pso@autodock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 Å), which corresponds to only 10% of the computing time demanded by autodock. In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, autodock 3.05, and sodock, pso@autodock provides the smallest rmsd values for 12 in 37 protein,ligand complexes. The average rmsd value of 1.4 Å is significantly lower then those obtained with the other docking programs, which are all above 2.0 Å. Thus, pso@autodock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide,protein docking and virtual screening studies. [source] SODOCK: Swarm optimization for highly flexible protein,ligand dockingJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 2 2007Hung-Ming Chen Abstract Protein,ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein,ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 Å of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 Å. © 2006 Wiley Periodicals, Inc. J Comput Chem 28: 612,623, 2007 [source] Multimode Project Scheduling Based on Particle Swarm OptimizationCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 2 2006Hong Zhang This article introduces a methodology for solving the MRCPSP based on particle swarm optimization (PSO) that has not been utilized for this and other construction-related problems. The framework of the PSO-based methodology is developed. A particle representation formulation is proposed to represent the potential solution to the MRCPSP in terms of priority combination and mode combination for activities. Each particle-represented solution should be checked against the nonrenewable resource infeasibility and will be handled by adjusting the mode combination. The feasible particle-represented solution is transformed to a schedule through a serial generation scheme. Experimental analyses are presented to investigate the performance of the proposed methodology. [source] Detecting particle swarm optimizationCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 4 2009Ying-Nan Zhang Abstract Here, we propose a detecting particle swarm optimization (DPSO). In DPSO, we define several detecting particles that are randomly selected from the population. The detecting particles use the newly proposed velocity formula to search the adjacent domains of a settled position in approximate spiral trajectories. In addition, we define the particles that use the canonical velocity updating formula as common particles. In each iteration, the common particles use the canonical velocity updating formula to update their velocities and positions, and then the detecting particles do search in approximate spiral trajectories created by the new velocity updating formula in order to find better solutions. As a whole, the detecting particles and common particles would do the high-performance search. DPSO implements the common particles' swarm search behavior and the detecting particles' individual search behavior, thereby trying to improve PSO's performance on swarm diversity, the ability of quick convergence and jumping out the local optimum. The experimental results from several benchmark functions demonstrate good performance of DPSO. Copyright © 2008 John Wiley & Sons, Ltd. [source] Active target particle swarm optimizationCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 1 2008Ying-Nan Zhang Abstract We propose an active target particle swarm optimization (APSO). APSO uses a new three-target velocity updating formula, i.e. the best previous position, the global best position and a new target position (called active target). In this study, we distinguish APSO from EPSO (extended PSO)/PSOPC (PSO with passive congregation) by the different methods of getting the active target. Note that here EPSO and PSOPC are the two existing methods for using three-target velocity updating formula, and getting the third (active) target from the obtained positions by the swarm. The proposed APSO gets the active (third) target using complex method, where the active target does not belong to the existing positions. We find that the APSO has the advantages of jumping out of the local optimum and keeping diversity; however, it also has the disadvantages of adding some extra computation expenses. The experimental results show the competitive performance of APSO when compared with PSO, EPSO, and PSOPC. Copyright © 2007 John Wiley & Sons, Ltd. [source] Particle swarm optimization of TMD by non-stationary base excitation during earthquakeEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 9 2008A. Y. T. Leung Abstract There are many traditional methods to find the optimum parameters of a tuned mass damper (TMD) subject to stationary base excitations. It is very difficult to obtain the optimum parameters of a TMD subject to non-stationary base excitations using these traditional optimization techniques. In this paper, by applying particle swarm optimization (PSO) algorithm as a novel evolutionary algorithm, the optimum parameters including the optimum mass ratio, damper damping and tuning frequency of the TMD system attached to a viscously damped single-degree-of-freedom main system subject to non-stationary excitation can be obtained when taking either the displacement or the acceleration mean square response, as well as their combination, as the cost function. For simplicity of presentation, the non-stationary excitation is modeled by an evolutionary stationary process in the paper. By means of three numerical examples for different types of non-stationary ground acceleration models, the results indicate that PSO can be used to find the optimum mass ratio, damper damping and tuning frequency of the non-stationary TMD system, and it is quite easy to be programmed for practical engineering applications. Copyright © 2008 John Wiley & Sons, Ltd. [source] Integrative optimization by RBF network and particle swarm optimizationELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 12 2009Satoshi Kitayama Abstract This paper presents a method for the integrative optimization system. Recently, many methods for global optimization have been proposed. The objective of these methods is to find a global minimum of nonconvex function. However, large numbers of function evaluations are required, in general. We utilize the response surface method to approximate function space to reduce the function evaluations. The response surface method is constructed from sampling points. The RBF Network, which is one of the neural networks, is utilized to approximate the function space. Then Particle Swarm Optimization (PSO) is applied to the response surface. The proposed system consists of three parts: (Part 1) generation of the sampling points, (Part 2) construction of response surface by RBF Network, (Part 3) optimization by PSO. By iterating these three parts, it is expected that the approximate global minimum of nonconvex function can be obtained with a small number of function evaluations. Through numerical examples, the effectiveness and validity are examined. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(12): 31,42, 2009; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecj.10187 [source] Optimal production cost of the power producers with linear ramp model using FDR PSO algorithmEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 2 2010M. Anitha Abstract In practice, the power producers have to be rescheduled for power generation beyond their power limits to meet vulnerable situations like credible contingency and increase in load conditions. During this process, the ramping cost is incurred if they violate their permissible elastic limits. In this paper, optimal production costs of the power producers are computed with ramping cost considering stepwise and piecewise linear ramp rate limits using fitness distance ratio particle swarm optimization (FDR PSO) algorithm. Transient stability constraints are also considered while formulating the optimal power flow (OPF) problem as additional rotor angle inequality constraints. The proposed algorithm is demonstrated on a practical 39 bus New England and 62 bus Indian Utility system with different case studies. Copyright © 2008 John Wiley & Sons, Ltd. [source] A hybrid swarm intelligence algorithm for the travelling salesman problemEXPERT SYSTEMS, Issue 3 2010I-Hong Kuo Abstract: We present a hybrid model named HRKPG that combines the random-key search method and an individual enhancement scheme to thoroughly exploit the global search ability of particle swarm optimization. With a genetic algorithm, we can expand the area of exploration of individuals in the solution space. With the individual enhancement scheme, we can enhance the particle swarm optimization and the genetic algorithm for the travelling salesman problem. The objective of the travelling salesman problem is to find the shortest route that starts from a city, visits every city once, and finally comes back to the start city. With the random-key search method, we can search the ability of the particle and chromosome. On the basis of the proposed hybrid scheme of HRKPG, we can improve solution quality quite a lot. Our experimental results show that the HRKPG model outperforms the particle swarm optimization and genetic algorithm in solution quality. [source] Optimal Thermal Unit Commitment Integrated with Renewable Energy Sources Using Advanced Particle Swarm OptimizationIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 5 2009Shantanu Chakraborty Student member Abstract This paper presents a methodology for solving generation planning problem for thermal units integrated with wind and solar energy systems. The renewable energy sources are included in this model due to their low electricity cost and positive effect on environment. The generation planning problem also known by unit commitment problem is solved by a genetic algorithm operated improved binary particle swarm optimization (PSO) algorithm. Unlike trivial PSO, this algorithm runs the refinement process through the solutions within multiple populations. Some genetic algorithm operators such as crossover, elitism, and mutation are stochastically applied within the higher potential solutions to generate new solutions for next population. The PSO includes a new variable for updating velocity in accordance with population best along with conventional particle best and global best. The algorithm performs effectively in various sized thermal power system with equivalent solar and wind energy system and is able to produce high quality (minimized production cost) solutions. The solution model is also beneficial for reconstructed deregulated power system. The simulation results show the effectiveness of this algorithm by comparing the outcome with several established methods. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] An Optimal Grey PID Control SystemIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 4 2009Guo-Dong Li Non-member Abstract The PID control algorithm has been widely applied in many industry control system to solve efficiently the control problems of manufacturing processes. However, PID control algorithm belong to "delay control", because it functions on basis of the actions occurred in the system. Thus the control accuracy in real-time is limited. In this paper, in order to realize the accurate control in real-time, the grey model based on grey system theory is first proposed to combine with PID control to establish the control system. We define the proposed control system as grey PID control system. Then the cubic spline function is integrated into the grey model. to enhance the control accuracy of system. To further improve the performance, the particle swarm optimization (PSO) algorithm is employed to optimize the parameters of PID algorithm. Finally, we validated the effectiveness of the proposed control system by computer simulation. © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Application and comparison of metaheuristic techniques to reactive power planning problemIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 6 2008Mehdi Eghbal Non-Member Abstract This paper presents the application and comparison of metaheuristic techniques to reactive power planning (RPP) problem which involves optimal allocation and combination of to-be-installed VAr sources to satisfy voltage constraints during normal and contingency states for multiple load levels. The main objective of the proposed RPP problem is to minimize the investment cost through balanced installation of SCs and SVCs while keeping a specified security level and minimizing the amount of load shedding. The problem is formulated as a large scale mixed integer nonlinear programming problem, which is a nonsmooth and nondifferentiable optimization problem using conventional optimization techniques and induces lots of local minima. Among the metaheuristic techniques, genetic algorithm (GA), particle swarm optimization (PSO) and evolutionary particle swarm optimization (EPSO) are applied to solve the RPP problem. To investigate the effectiveness of the metaheuristic techniques, the proposed approaches have been successfully tested on IEEE-14 buses, as well as IEEE-57 buses test system. The results obtained are compared and the effectiveness of each technique has been illustrated. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Particle Swarm Optimization with Diverse ParametersIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 4 2008Mari Takei Student Member Abstract This paper proposes a particle swarm optimization (PSO) with diverse parameters that achieve an appropriate balance between diversification and intensification during the search based on numerical stability analysis. Numerical experiments using seven typical benchmark problems with 100, 500, and 1000 dimensions validate the robustness and search capabilities of the proposed PSO. © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] An optimal spectrum-balancing algorithm for digital subscriber lines based on particle swarm optimizationINTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 9 2008Meiqin Tang Abstract This paper presents a new algorithm for optimal spectrum balancing in modern digital subscriber line (DSL) systems using particle swarm optimization (PSO). In DSL, crosstalk is one of the major performance bottlenecks, therefore various dynamic spectrum management algorithms have been proposed to reduce excess crosstalks among users by dynamically optimizing transmission power spectra. In fact, the objective function in the spectrum optimization problem is always nonconcave. PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm optimizes the weighted rate sum. These weights allow the system operator to place differing qualities of service or importance levels on each user, which makes it possible for the system to avoid the selfish-optimum. We can show that the proposed algorithm converges to the global optimal solutions. Simulation results demonstrate that our algorithm can guarantee fast convergence within a few iterations and solve the nonconvex optimization problems efficiently. Copyright © 2008 John Wiley & Sons, Ltd. [source] Portfolio management using value at risk: A comparison between genetic algorithms and particle swarm optimizationINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 7 2009V. A. F. Dallagnol In this paper, it is shown a comparison of the application of particle swarm optimization and genetic algorithms to portfolio management, in a constrained portfolio optimization problem where no short sales are allowed. The objective function to be minimized is the value at risk calculated using historical simulation where several strategies for handling the constraints of the problem were implemented. The results of the experiments performed show that, generally speaking, the methods are capable of consistently finding good solutions quite close to the best solution found in a reasonable amount of time. In addition, it is demonstrated statistically that the algorithms, on average, do not all consistently achieve the same best solution. PSO turned out to be faster than GA, both in terms of number of iterations and in terms of total running time. However, PSO appears to be much more sensitive to the initial position of the particles than GA. Tests were also made regarding the number of particles needed to solve the problem, and 50 particles/chromosomes seem to be enough for problems up to 20 assets. © 2009 Wiley Periodicals, Inc. [source] Synthesis of interdigital capacitors based on particle swarm optimization and artificial neural networksINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 4 2006Jehad Ababneh Abstract This article reports on the use of the particle swarm optimization (PSO) algorithm in the synthesis of the planar interdigital capacitor (IDC). The PSO algorithm is used to optimize the geometry parameters of the IDC in order to obtain a certain capacitance value. The capacitance value of the IDC is evaluated using an artificial neural network (ANN) model with the geometry parameters of the IDC as its inputs. Several design examples are presented that illustrate the use of the PSO algorithm, and the design goal in each example is easily achieved. Full-wave electromagnetic simulations are also performed for some of the studied IDC structures implemented using coplanar waveguide (CPW) technology. The simulation results are in good agreement with those obtained using the ANN/PSO algorithm. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006. [source] Application of local and global particle swarm optimization algorithms to optimal design and operation of irrigation pumping systems,IRRIGATION AND DRAINAGE, Issue 3 2009M. H. Afshar stations de pompage; conception et exploitation; optimisation par essaims particulaires locale et globale Abstract A particle swarm optimization (PSO) algorithm is used in this paper for optimal design and operation of irrigation pumping systems. An irrigation pumping systems design and management model is first introduced and subsequently solved with the newly introduced PSO algorithm. The solution of the model is carried out in two steps. In the first step an exhaustive enumeration is carried out to find all feasible sets of pump combinations able to cope with a given demand curve over the required period. The PSO algorithm is then called in to search for optimal operation of each set. Having solved the operation problem of all feasible sets, the total cost of operation and depreciation of initial investment is calculated for all the sets and the optimal set and the corresponding optimal operating policy is determined. The proposed model is applied to the design and operation of a real-world irrigation pumping system and the results are presented and compared with those of a genetic algorithm. Two global and local versions of the PSO algorithm are used and their efficiencies are compared to each other and that of a genetic algorithm (GA) model. The results indicate that the proposed model in conjunction with the PSO algorithm is a versatile management model for the design and operation of real-world irrigation pumping systems. Copyright © 2008 John Wiley & Sons, Ltd. Un algorithme d'optimisation par essaims particulaires (PSO en anglais) est employé dans cet article pour la conception et l'exploitation optimale des systèmes d'irrigation avec pompages. Un modèle de conception et de gestion du système est d'abord présenté et ensuite résolu avec le nouvel algorithme PSO. La solution du modèle est effectuée dans deux étapes. Dans la première étape une énumération exhaustive est effectuée pour trouver toutes les combinaisons possibles de pompes capables de répondre à une courbe de demande donnée pendant la période souhaitée. L'algorithme d'optimisation par essaims particulaires est alors utilisé pour rechercher la gestion optimale de chaque ensemble. Ayant résolu le problème de gestion de toutes les combinaisons possibles, le coût d'exploitation et d'amortissement de l'investissement initial est calculé pour chacune et la combinaison optimale et sa stratégie de gestion optimale est déterminée. Le modèle proposé est appliqué à la conception et l'exploitation d'un système irrigué réel et les résultats sont présentés et comparés à ceux d'un algorithme génétique. Deux versions globales et locales de l'algorithme PSO sont employées et leurs efficacités sont comparées entre eux et avec celles d'un modèle à algorithme génétique. Les résultats indiquent que le modèle proposé associé à l'algorithme d'optimisation par essaims particulaires est un modèle souple pour la conception et l'exploitation systèmes irrigués réels avec pompage. Copyright © 2008 John Wiley & Sons, Ltd. [source] PeckCryst: a program for structure determination from powder diffraction data using a particle swarm optimization algorithmJOURNAL OF APPLIED CRYSTALLOGRAPHY, Issue 6 2009Zhen Jie Feng PeckCryst has been developed for the solution of molecular crystal structures from powder diffraction data using a particle swarm optimization (PSO) algorithm. In order to speed up the calculation process, a modified Bragg R factor is used as the evaluation function for the PSO algorithm. The effectiveness of the program has been tested by solving four known structures from powder diffraction data. A Python script is also provided for convenient repetitive running of PeckCryst. The distributed PeckCryst program is freely available from the authors upon request, and runs on Linux and Windows (32- and 64-bit) platforms. [source] Multiple ligand simultaneous docking: Orchestrated dancing of ligands in binding sites of proteinJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 10 2010Huameng Li Abstract Present docking methodologies simulate only one single ligand at a time during docking process. In reality, the molecular recognition process always involves multiple molecular species. Typical protein,ligand interactions are, for example, substrate and cofactor in catalytic cycle; metal ion coordination together with ligand(s); and ligand binding with water molecules. To simulate the real molecular binding processes, we propose a novel multiple ligand simultaneous docking (MLSD) strategy, which can deal with all the above processes, vastly improving docking sampling and binding free energy scoring. The work also compares two search strategies: Lamarckian genetic algorithm and particle swarm optimization, which have respective advantages depending on the specific systems. The methodology proves robust through systematic testing against several diverse model systems: E. coli purine nucleoside phosphorylase (PNP) complex with two substrates, SHP2NSH2 complex with two peptides and Bcl-xL complex with ABT-737 fragments. In all cases, the final correct docking poses and relative binding free energies were obtained. In PNP case, the simulations also capture the binding intermediates and reveal the binding dynamics during the recognition processes, which are consistent with the proposed enzymatic mechanism. In the other two cases, conventional single-ligand docking fails due to energetic and dynamic coupling among ligands, whereas MLSD results in the correct binding modes. These three cases also represent potential applications in the areas of exploring enzymatic mechanism, interpreting noisy X-ray crystallographic maps, and aiding fragment-based drug design, respectively. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [source] SODOCK: Swarm optimization for highly flexible protein,ligand dockingJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 2 2007Hung-Ming Chen Abstract Protein,ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein,ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 Å of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 Å. © 2006 Wiley Periodicals, Inc. J Comput Chem 28: 612,623, 2007 [source] New and accurate synthesis formulas for open supported coplanar waveguidesMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 2 2010S. Kaya Abstract In this article, new and accurate synthesis formulas to compute the physical dimensions of open supported coplanar waveguides (OS-CPWs) are presented. The synthesis formulas are obtained with the use of differential evolution (DE) and particle swarm optimization (PSO) algorithms. They are useful for the computer-aided design of OS-CPWs. The average percentage errors of the synthesis formulas obtained by using DE and PSO algorithms are computed to be 1.26% and 1.67%, respectively, for 4560 OS-CPW samples having different electrical parameters and physical dimensions, as compared with the results of quasi-static analysis. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 262,269, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24908 [source] | |||||||||||||||||||