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PSO Algorithm (pso + algorithm)
Selected AbstractsOptimal 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] 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] 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] | ||||||||||