Search results for: airfoil optimization
1556 Optimization Method Based MPPT for Wind Power Generators
Authors: Chun-Yao Lee , Yi-Xing Shen , Jung-Cheng Cheng , Chih-Wen Chang, Yi-Yin Li
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This paper proposes the method combining artificial neural network with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. With the measurements of wind speed, rotor speed of wind generator and output power, the artificial neural network can be trained and the wind speed can be estimated. The proposed control system in this paper provides a manner for searching the maximum output power of wind generator even under the conditions of varying wind speed and load impedance.
Keywords: maximum power point tracking, artificial neural network, particle swarm optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18111555 Optimization of Doubly Fed Induction Generator Equivalent Circuit Parameters by Direct Search Method
Authors: Mamidi Ramakrishna Rao
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Doubly-fed induction generator (DFIG) is currently the choice for many wind turbines. These generators, when connected to the grid through a converter, is subjected to varied power system conditions like voltage variation, frequency variation, short circuit fault conditions, etc. Further, many countries like Canada, Germany, UK, Scotland, etc. have distinct grid codes relating to wind turbines. Accordingly, following the network faults, wind turbines have to supply a definite reactive current. To satisfy the requirements including reactive current capability, an optimum electrical design becomes a mandate for DFIG to function. This paper intends to optimize the equivalent circuit parameters of an electrical design for satisfactory DFIG performance. Direct search method has been used for optimization of the parameters. The variables selected include electromagnetic core dimensions (diameters and stack length), slot dimensions, radial air gap between stator and rotor and winding copper cross section area. Optimization for 2 MW DFIG has been executed separately for three objective functions - maximum reactive power capability (Case I), maximum efficiency (Case II) and minimum weight (Case III). In the optimization analysis program, voltage variations (10%), power factor- leading and lagging (0.95), speeds for corresponding to slips (-0.3 to +0.3) have been considered. The optimum designs obtained for objective functions were compared. It can be concluded that direct search method of optimization helps in determining an optimum electrical design for each objective function like efficiency or reactive power capability or weight minimization.
Keywords: Direct search, DFIG, equivalent circuit parameters, optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8961554 Ant System with Acoustic Communication
Authors: S. Bougrine, S. Ouchraa, B. Ahiod, A. A. El Imrani
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Ant colony optimization is an ant algorithm framework that took inspiration from foraging behavior of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved.
Keywords: Acoustic Communication, Ant Colony Optimization, Local Search, Traveling Salesman Problem.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24211553 Selecting Materialized Views Using Two-Phase Optimization with Multiple View Processing Plan
Authors: Jiratta Phuboon-ob, Raweewan Auepanwiriyakul
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A data warehouse (DW) is a system which has value and role for decision-making by querying. Queries to DW are critical regarding to their complexity and length. They often access millions of tuples, and involve joins between relations and aggregations. Materialized views are able to provide the better performance for DW queries. However, these views have maintenance cost, so materialization of all views is not possible. An important challenge of DW environment is materialized view selection because we have to realize the trade-off between performance and view maintenance cost. Therefore, in this paper, we introduce a new approach aimed at solve this challenge based on Two-Phase Optimization (2PO), which is a combination of Simulated Annealing (SA) and Iterative Improvement (II), with the use of Multiple View Processing Plan (MVPP). Our experiments show that our method provides a further improvement in term of query processing cost and view maintenance cost.Keywords: Data warehouse, materialized views, view selectionproblem, two-phase optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16511552 UB-Tree Indexing for Semantic Query Optimization of Range Queries
Authors: S. Housseno, A. Simonet, M. Simonet
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Semantic query optimization consists in restricting the search space in order to reduce the set of objects of interest for a query. This paper presents an indexing method based on UB-trees and a static analysis of the constraints associated to the views of the database and to any constraint expressed on attributes. The result of the static analysis is a partitioning of the object space into disjoint blocks. Through Space Filling Curve (SFC) techniques, each fragment (block) of the partition is assigned a unique identifier, enabling the efficient indexing of fragments by UB-trees. The search space corresponding to a range query is restricted to a subset of the blocks of the partition. This approach has been developed in the context of a KB-DBMS but it can be applied to any relational system.Keywords: Index, Range query, UB-tree, Space Filling Curve, Query optimization, Views, Database, Integrity Constraint, Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14911551 Multi-objective Optimisation of Composite Laminates under Heat and Moisture Effects using a Hybrid Neuro-GA Algorithm
Authors: M. R. Ghasemi, A. Ehsani
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In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.Keywords: Composite Laminates, GA, Multi-objectiveOptimisation, Neural Networks, RBFNN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16211550 Flow Modeling and Runner Design Optimization in Turgo Water Turbines
Authors: John S. Anagnostopoulos, Dimitrios E. Papantonis
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The incorporation of computational fluid dynamics in the design of modern hydraulic turbines appears to be necessary in order to improve their efficiency and cost-effectiveness beyond the traditional design practices. A numerical optimization methodology is developed and applied in the present work to a Turgo water turbine. The fluid is simulated by a Lagrangian mesh-free approach that can provide detailed information on the energy transfer and enhance the understanding of the complex, unsteady flow field, at very small computing cost. The runner blades are initially shaped according to hydrodynamics theory, and parameterized using Bezier polynomials and interpolation techniques. The use of a limited number of free design variables allows for various modifications of the standard blade shape, while stochastic optimization using evolutionary algorithms is implemented to find the best blade that maximizes the attainable hydraulic efficiency of the runner. The obtained optimal runner design achieves considerably higher efficiency than the standard one, and its numerically predicted performance is comparable to a real Turgo turbine, verifying the reliability and the prospects of the new methodology.Keywords: Turgo turbine, Lagrangian flow modeling, Surface parameterization, Design optimization, Evolutionary algorithms.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 40481549 Interactive Compromise Approach with Particle Swarm Optimization for Environmental/Economic Power Dispatch
Authors: Ming-Tang Tsai, Chih-Wei Yen
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In this paper, an Interactive Compromise Approach with Particle Swarm Optimization(ICA-PSO) is presented to solve the Economic Emission Dispatch(EED) problem. The cost function and emission function are modeled as the nonsmooth functions, respectively. The bi-objective including both the minimization of cost and emission is formulated in this paper. ICA-PSO is proposed to solve EED problem for finding a better compromise solution. The solution methodology can offer a global or near-global solution for decision-making requirements. The effectiveness and efficiency of ICA-PSO are demonstrated by a sample test system. Test results can be shown that the proposed method provide a practical and flexible framework for power dispatch.Keywords: Interactive Compromise Approach, Emission Control, Economic Dispatch, Particle Swarm Optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14391548 The Optimization of Decision Rules in Multimodal Decision-Level Fusion Scheme
Authors: Andrey V. Timofeev, Dmitry V. Egorov
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This paper introduces an original method of parametric optimization of the structure for multimodal decisionlevel fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.
Keywords: Сlassification accuracy, fusion solution, total error rate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19671547 Design of Optimal Proportional Integral Derivative Attitude Controller for an Uncoupled Flexible Satellite Using Particle Swarm Optimization
Authors: Martha C. Orazulume, Jibril D. Jiya
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Flexible satellites are equipped with various appendages which vibrate under the influence of any excitation and make the attitude of the satellite to be unstable. Therefore, the system must be able to adjust to balance the effect of these appendages in order to point accurately and satisfactorily which is one of the most important problems in satellite design. Proportional Integral Derivative (PID) Controller is simple to design and computationally efficient to implement which is used to stabilize the effect of these flexible appendages. However, manual turning of the PID is time consuming, waste energy and money. Particle Swarm Optimization (PSO) is used to tune the parameters of PID Controller. Simulation results obtained show that PSO tuned PID Controller is able to re-orient the spacecraft attitude as well as dampen the effect of mechanical resonance and yields better performance when compared with manually tuned PID Controller.
Keywords: Attitude control, flexible satellite, particle swarm optimization, PID controller.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12651546 Using Swarm Intelligence for Improving Accuracy of Fuzzy Classifiers
Authors: Hassan M. Elragal
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This paper discusses a method for improving accuracy of fuzzy-rule-based classifiers using particle swarm optimization (PSO). Two different fuzzy classifiers are considered and optimized. The first classifier is based on Mamdani fuzzy inference system (M_PSO fuzzy classifier). The second classifier is based on Takagi- Sugeno fuzzy inference system (TS_PSO fuzzy classifier). The parameters of the proposed fuzzy classifiers including premise (antecedent) parameters, consequent parameters and structure of fuzzy rules are optimized using PSO. Experimental results show that higher classification accuracy can be obtained with a lower number of fuzzy rules by using the proposed PSO fuzzy classifiers. The performances of M_PSO and TS_PSO fuzzy classifiers are compared to other fuzzy based classifiersKeywords: Fuzzy classifier, Optimization of fuzzy systemparameters, Particle swarm optimization, Pattern classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23311545 Improved Multi–Objective Firefly Algorithms to Find Optimal Golomb Ruler Sequences for Optimal Golomb Ruler Channel Allocation
Authors: Shonak Bansal, Prince Jain, Arun Kumar Singh, Neena Gupta
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Recently nature–inspired algorithms have widespread use throughout the tough and time consuming multi–objective scientific and engineering design optimization problems. In this paper, we present extended forms of firefly algorithm to find optimal Golomb ruler (OGR) sequences. The OGRs have their one of the major application as unequally spaced channel–allocation algorithm in optical wavelength division multiplexing (WDM) systems in order to minimize the adverse four–wave mixing (FWM) crosstalk effect. The simulation results conclude that the proposed optimization algorithm has superior performance compared to the existing conventional computing and nature–inspired optimization algorithms to find OGRs in terms of ruler length, total optical channel bandwidth and computation time.Keywords: Channel allocation, conventional computing, four–wave mixing, nature–inspired algorithm, optimal Golomb ruler, Lévy flight distribution, optimization, improved multi–objective Firefly algorithms, Pareto optimal.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11411544 Optimal Capacitor Placement in Distribution Feeders
Authors: N. Rugthaicharoencheep, S. Auchariyamet
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Optimal capacitor allocation in distribution systems has been studied for a long times. It is an optimization problem which has an objective to define the optimal sizes and locations of capacitors to be installed. In this works, an overview of capacitor placement problem in distribution systems is briefly introduced. The objective functions and constraints of the problem are listed and the methodologies for solving the problem are summarized.Keywords: Capacitor Placement, Distribution Systems, Optimization Techniques
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24101543 Optimization of Transfer Pricing in a Recession with Reflection on Croatian Situation
Authors: Jasminka Radolović
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Countries in recession, among them Croatia, have lower tax revenues as a result of unfavorable economic situation, which is decrease of the economic activities and unemployment. The global tax base has decreased. In order to create larger state revenues, states use the institute of tax authorities. By controlling transfer pricing in the international companies and using certain techniques, tax authorities can create greater tax obligations for the companies in a short period of time.Keywords: Documentation, Methods, Tax Optimization, Transfer Pricing
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14821542 Reducing Variation of Dyeing Process in Textile Manufacturing Industry
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This study deals with a multi-criteria optimization problem which has been transformed into a single objective optimization problem using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Grey Relational Analyses (GRA) approach. Grey-RSM and Grey-ANN are hybrid techniques which can be used for solving multi-criteria optimization problem. There have been two main purposes of this research as follows. 1. To determine optimum and robust fiber dyeing process conditions by using RSM and ANN based on GRA, 2. To obtain the best suitable model by comparing models developed by different methodologies. The design variables for fiber dyeing process in textile are temperature, time, softener, anti-static, material quantity, pH, retarder, and dispergator. The quality characteristics to be evaluated are nominal color consistency of fiber, maximum strength of fiber, minimum color of dyeing solution. GRA-RSM with exact level value, GRA-RSM with interval level value and GRA-ANN models were compared based on GRA output value and MSE (Mean Square Error) performance measurement of outputs with each other. As a result, GRA-ANN with interval value model seems to be suitable reducing the variation of dyeing process for GRA output value of the model.Keywords: Artificial Neural Network, Grey Relational Analysis, Optimization, Response Surface Methodology
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35451541 Hybrid Algorithm for Hammerstein System Identification Using Genetic Algorithm and Particle Swarm Optimization
Authors: Tomohiro Hachino, Kenji Shimoda, Hitoshi Takata
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This paper presents a method of model selection and identification of Hammerstein systems by hybridization of the genetic algorithm (GA) and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. On the other hand, the adjusting parameters of the ACF model structure are properly selected by the hybrid algorithm of the GA and PSO, where the Akaike information criterion is utilized as the evaluation value function. Simulation results are shown to demonstrate the effectiveness of the proposed hybrid algorithm.Keywords: Hammerstein system, identification, automatic choosing function model, genetic algorithm, particle swarm optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14881540 Advanced Hybrid Particle Swarm Optimization for Congestion and Power Loss Reduction in Distribution Networks with High Distributed Generation Penetration through Network Reconfiguration
Authors: C. Iraklis, G. Evmiridis, A. Iraklis
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Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.
Keywords: Congestion, distribution networks, loss reduction, particle swarm optimization, smart grid.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7301539 Optimization of PEM Fuel Cell Biphasic Model
Authors: Boubekeur Dokkar, Nasreddine Chennouf, Noureddine Settou, Belkhir Negrou, Abdesslam Benmhidi
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The optimal operation of proton exchange membrane fuel cell (PEMFC) requires good water management which is presented under two forms vapor and liquid. Moreover, fuel cells have to reach higher output require integration of some accessories which need electrical power. In order to analyze fuel cells operation and different species transport phenomena a biphasic mathematical model is presented by governing equations set. The numerical solution of these conservation equations is calculated by Matlab program. A multi-criteria optimization with weighting between two opposite objectives is used to determine the compromise solutions between maximum output and minimal stack size. The obtained results are in good agreement with available literature data.
Keywords: Biphasic model, PEM fuel cell, optimization, simulation, specie transport.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20211538 Analysis and Application of in Indirect MinimumJerk Method for Higher order Differential Equation in Dynamics Optimization Systems
Authors: V. Tawiwat, T. Amornthep, P. Pnop
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Both the minimum energy consumption and smoothness, which is quantified as a function of jerk, are generally needed in many dynamic systems such as the automobile and the pick-and-place robot manipulator that handles fragile equipments. Nevertheless, many researchers come up with either solely concerning on the minimum energy consumption or minimum jerk trajectory. This research paper considers the indirect minimum Jerk method for higher order differential equation in dynamics optimization proposes a simple yet very interesting indirect jerks approaches in designing the time-dependent system yielding an alternative optimal solution. Extremal solutions for the cost functions of indirect jerks are found using the dynamic optimization methods together with the numerical approximation. This case considers the linear equation of a simple system, for instance, mass, spring and damping. The simple system uses two mass connected together by springs. The boundary initial is defined the fix end time and end point. The higher differential order is solved by Galerkin-s methods weight residual. As the result, the 6th higher differential order shows the faster solving time.Keywords: Optimization, Dynamic, Linear Systems, Jerks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13251537 A Hybrid Radial-Based Neuro-GA Multiobjective Design of Laminated Composite Plates under Moisture and Thermal Actions
Authors: Mohammad Reza Ghasemi, Ali Ehsani
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In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.Keywords: Composite Laminates, GA, Multi-objectiveOptimization, Neural Networks, RBFNN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14551536 Optimization of the Transfer Molding Process by Implementation of Online Monitoring Techniques for Electronic Packages
Authors: Burcu Kaya, Jan-Martin Kaiser, Karl-Friedrich Becker, Tanja Braun, Klaus-Dieter Lang
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Quality of the molded packages is strongly influenced by the process parameters of the transfer molding. To achieve a better package quality and a stable transfer molding process, it is necessary to understand the influence of the process parameters on the package quality. This work aims to comprehend the relationship between the process parameters, and to identify the optimum process parameters for the transfer molding process in order to achieve less voids and wire sweep. To achieve this, a DoE is executed for process optimization and a regression analysis is carried out. A systematic approach is represented to generate models which enable an estimation of the number of voids and wire sweep. Validation experiments are conducted to verify the model and the results are presented.Keywords: Epoxy molding compounds, optimization, regression analysis, transfer molding process, voids, wire sweep.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15151535 Canonical PSO based Nanorobot Control for Blood Vessel Repair
Authors: Pinfa Boonrong, Boonserm Kaewkamnerdpong
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As nanotechnology advances, the use of nanotechnology for medical purposes in the field of nanomedicine seems more promising; the rise of nanorobots for medical diagnostics and treatments could be arriving in the near future. This study proposes a swarm intelligence based control mechanism for swarm nanorobots that operate as artificial platelets to search for wounds. The canonical particle swarm optimization algorithm is employed in this study. A simulation in the circulatory system is constructed and used for demonstrating the movement of nanorobots with essential characteristics to examine the performance of proposed control mechanism. The effects of three nanorobot capabilities including their perception range, maximum velocity and respond time are investigated. The results show that canonical particle swarm optimization can be used to control the early version nanorobots with simple behaviors and actions.
Keywords: Artificial platelets, canonical particle swarm optimization, nanomedicine, nanorobot, swarm intelligence.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26721534 The Effect of Increment in Simulation Samples on a Combined Selection Procedure
Authors: Mohammad H. Almomani, Rosmanjawati Abdul Rahman
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Statistical selection procedures are used to select the best simulated system from a finite set of alternatives. In this paper, we present a procedure that can be used to select the best system when the number of alternatives is large. The proposed procedure consists a combination between Ranking and Selection, and Ordinal Optimization procedures. In order to improve the performance of Ordinal Optimization, Optimal Computing Budget Allocation technique is used to determine the best simulation lengths for all simulation systems and to reduce the total computation time. We also argue the effect of increment in simulation samples for the combined procedure. The results of numerical illustration show clearly the effect of increment in simulation samples on the proposed combination of selection procedure.Keywords: Indifference-Zone, Optimal Computing Budget Allocation, Ordinal Optimization, Ranking and Selection, Subset Selection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12281533 Particle Swarm Optimization Based PID Power System Stabilizer for a Synchronous Machine
Authors: Gowrishankar Kasilingam
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This paper proposes a swarm intelligence method that yields optimal Proportional-Integral-Derivative (PID) Controller parameters of a power system stabilizer (PSS) in a single machine infinite bus system. The proposed method utilizes the Particle Swarm Optimization (PSO) algorithm approach to generate the optimal tuning parameters. The paper is modeled in the MATLAB Simulink Environment to analyze the performance of a synchronous machine under several load conditions. At the same operating point, the PID-PSS parameters are also tuned by Ziegler-Nichols method. The dynamic performance of proposed controller is compared with the conventional Ziegler-Nichols method of PID tuning controller to demonstrate its advantage. The analysis reveals the effectiveness of the proposed PSO based PID controller.
Keywords: Particle Swarm Optimization, PID Controller, Power System Stabilizer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30231532 A New Solution for Natural Convection of Darcian Fluid about a Vertical Full Cone Embedded in Porous Media Prescribed Wall Temperature by using a Hybrid Neural Network-Particle Swarm Optimization Method
Authors: M.A.Behrang, M. Ghalambaz, E. Assareh, A.R. Noghrehabadi
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Fluid flow and heat transfer of vertical full cone embedded in porous media is studied in this paper. Nonlinear differential equation arising from similarity solution of inverted cone (subjected to wall temperature boundary conditions) embedded in porous medium is solved using a hybrid neural network- particle swarm optimization method. To aim this purpose, a trial solution of the differential equation is defined as sum of two parts. The first part satisfies the initial/ boundary conditions and does contain an adjustable parameter and the second part which is constructed so as not to affect the initial/boundary conditions and involves adjustable parameters (the weights and biases) for a multi-layer perceptron neural network. Particle swarm optimization (PSO) is applied to find adjustable parameters of trial solution (in first and second part). The obtained solution in comparison with the numerical ones represents a remarkable accuracy.Keywords: Porous Media, Ordinary Differential Equations (ODE), Particle Swarm Optimization (PSO), Neural Network (NN).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17061531 Correlation-based Feature Selection using Ant Colony Optimization
Authors: M. Sadeghzadeh, M. Teshnehlab
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Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.
Keywords: Ant colony optimization, Classification, Datamining, Feature selection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24111530 Method for Determining the Probing Points for Efficient Measurement of Freeform Surface
Authors: Yi Xu, Zexiang Li
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In inspection and workpiece localization, sampling point data is an important issue. Since the devices for sampling only sample discrete points, not the completely surface, sampling size and location of the points will be taken into consideration. In this paper a method is presented for determining the sampled points size and location for achieving efficient sampling. Firstly, uncertainty analysis of the localization parameters is investigated. A localization uncertainty model is developed to predict the uncertainty of the localization process. Using this model the minimum size of the sampled points is predicted. Secondly, based on the algebra theory an eigenvalue-optimal optimization is proposed. Then a freeform surface is used in the simulation. The proposed optimization is implemented. The simulation result shows its effectivity.
Keywords: eigenvalue-optimal optimization, freeform surface inspection, sampling size and location, sampled points.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12201529 Improved Artificial Immune System Algorithm with Local Search
Authors: Ramin Javadzadeh., Zahra Afsahi, MohammadReza Meybodi
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The Artificial immune systems algorithms are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Artificial Immune System Algorithm is introduced for the first time to overcome its problems of artificial immune system. That use of the small size of a local search around the memory antibodies is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the standard artificial immune system algorithmsKeywords: Artificial immune system, Cellular Automata, Cellular learning automata, Cellular learning automata, , Local search, Optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18781528 Multi-objective Optimization of Vehicle Passive Suspension with a Two-Terminal Mass Using Chebyshev Goal Programming
Authors: Chuan Li, Ming Liang, Qibing Yu
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To improve the dynamics response of the vehicle passive suspension, a two-terminal mass is suggested to connect in parallel with the suspension strut. Three performance criteria, tire grip, ride comfort and suspension deflection, are taken into consideration to optimize the suspension parameters. However, the three criteria are conflicting and non-commensurable. For this reason, the Chebyshev goal programming method is applied to find the best tradeoff among the three objectives. A simulation case is presented to describe the multi-objective optimization procedure. For comparison, the Chebyshev method is also employed to optimize the design of a conventional passive suspension. The effectiveness of the proposed design method has been clearly demonstrated by the result. It is also shown that the suspension with a two-terminal mass in parallel has better performance in terms of the three objectives.Keywords: Vehicle, passive suspension, two-terminal mass, optimization, Chebyshev goal programming
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17491527 A Deterministic Dynamic Programming Approach for Optimization Problem with Quadratic Objective Function and Linear Constraints
Authors: S. Kavitha, Nirmala P. Ratchagar
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This paper presents the novel deterministic dynamic programming approach for solving optimization problem with quadratic objective function with linear equality and inequality constraints. The proposed method employs backward recursion in which computations proceeds from last stage to first stage in a multi-stage decision problem. A generalized recursive equation which gives the exact solution of an optimization problem is derived in this paper. The method is purely analytical and avoids the usage of initial solution. The feasibility of the proposed method is demonstrated with a practical example. The numerical results show that the proposed method provides global optimum solution with negligible computation time.
Keywords: Backward recursion, Dynamic programming, Multi-stage decision problem, Quadratic objective function.
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