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genetic algorithm approach for designing 2-D FIR digital filters with 2-D symmetric properties

SIGNAL PROCESSING 2004年第10期84卷 1883-1893页

作者： Tzeng, ST Kaoyuan Inst Technol Dept Elect Engn Kaohsiung 821 Taiwan

Besides the design of quadrantally symmetric linear-phase 2-D FIR digital filters, a wide variety of linear-phase 2-D filter designs are proposed in this paper. It will start from the discussion of the symmetric properties to disclose their applications for designing linear-phase 2-D FIR digital filters. It is shown that there are 16 types of cases to be considered according to the symmetry/antisymmetry of 2-D sequences in both directions and their filter lengths (even or odd). The corresponding types of magnitude responses are tabulated into a complete table if these 2-D sequences are used to realize 2-D FIR digital filters. Also, the definition of quadrantal-plane, half-plane, and full-plane filters are described along with several numerical examples designed by the proposed genetic algorithm approach. (C) 2004 Elsevier B.V. All rights reserved.

关键词： linear-phase 2-d FIR digital filters filter symmetry filter design genetic algorithm approach

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genetic algorithm approach to the modelling of polluted insulators

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 2002年第3期149卷 373-376页

作者： Gonos, IF Topalis, FV Stathopolos, IA Natl Tech Univ Athens Dept Elect & Comp Engn Athens 15780 Greece

The phenomenon of flashover in polluted insulators has not yet been described accurately through a mathematical model. The main difficulty lies in the definition of the constants of the arc, which is formed in The authors present a complex optimisation method based on genetic algorithms for the determination of the arc constants, using experimental results from artificially polluted insulators. First the well known model of Obenhaus for pollution flashover is used. This model results in a system of equations which cannot be solved with conventional arithmetic methods. The application of genetic algorithms enables the definition of the are constants, resulting also in the calculation of the critical conditions at the beginning of the pollution flashover mechanism. In this way a mathematical model is established, which simulates accurately the experimental results.

关键词： flashover insulator contamination mathematical model genetic algorithms arc constants artificially polluted insulators Dielectric breakdown and discharges polluted insulator flashover modelling arcs (electric) Power line supports, insulators and connectors Optimisation techniques Obenhaus model pollution flashover genetic algorithm approach

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Optimization of the physical properties of barium titanates using a genetic algorithm approach

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JOURNAL OF THE AUSTRALIAN CERAMIC SOCIETY 2017年第2期53卷 673-686页

作者： Al-dujaili, Mohammed A. Ahmed Al-hydary, Imad A. Disher Al Jabar, Ahmed J. Abed Univ Babylon Fac Mat Engn Dept Ceram Engn & Bldg Mat POB 4 Babylon Iraq

Barium titanates are widely used in the electronics industry because of their high dielectric constant and ferroelectric and piezoelectric properties. These properties are related to the physical properties of the material;thus, the optimization of these properties is crucially important. The aim of the current work is to control the values of the processing parameters to produce the optimum density, porosity, firing shrinkage, and green density of BaTiO3. A genetic algorithm was used to fulfill this aim. The modified pechini method was used to prepare barium titanate powders with five different particle size distributions. Eighty samples were prepared using different processing parameters including the pressing rate, pressing pressure, heating rate, sintering temperature, and soaking time. A genetic algorithm (GA) approach has been applied in order to obtain the optimum processing parameters. The results showed that the best value of the density that can be achieved is 6.02 g/cm(3), which is equal to the theoretical density of BaTiO3 using a pressing rate of 3 KN/S, a pressing pressure of around 370-385 Mpa, a sintering temperature of not less than 1400 degrees C, a soaking time of around 6-8 h, and a heating rate of 2.5 degrees C/min. The same upper and lower boundary conditions, used to obtain the optimum density, were also employed for the investigation of the porosity, firing shrinkage, and green density. The optimum achieved values were of geometric mean of 6.89%, 3.48 g/cm(3), and 17.01% for the porosity, green density, and firing shrinkage, respectively.

关键词： Physical properties Barium titanates Optimum density Porosity Firing shrinkage and green density of BaTiO3 genetic algorithm approach

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Determination of SIF for patched crack in aluminum plates by the combined finite element and genetic algorithm approach

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FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES 2008年第11期31卷 929-936页

作者： Ergun, E. Tasgetiren, S. Topcu, M. Pamukkale Univ Dept Mech Engn Denizli Turkey Afyon Kocatepe Univ Dept Mech Educ Afyon Turkey

In this paper, the finite element method is used to analyze the behaviour of repaired cracks in 2024-T3 aluminum with bonded patches made of unidirectional composite plates. The problem is considered in Mode I condition. First the K-I stress intensity factor (SIF) is calculated by the finite element method using displacement correlation technique. Different plate and patch thicknesses and crack lengths are considered in the analyses. Then an equation is determined by using genetic algorithms for the calculation of stress intensity factor without any further finite element analysis. The equation gives reasonably accurate results.

关键词： finite element analysis genetic algorithm approach patched crack stress intensity factors

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A hybrid discrete differential evolution - genetic algorithm approach with a new batch formation mechanism for parallel batch scheduling considering batch delivery

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第1-2期62卷 460-482页

作者： Kucukkoc, Ibrahim Keskin, Gulsen Aydin Karaoglan, Aslan Deniz Karadag, Sevgi Balikesir Univ Dept Ind Engn Cagis Campus TR-10145 Balikesir Turkiye HasTavuk Co Res & Dev Dept Susurluk Balikesir Turkiye

Scheduling is an important decision-making problem in production planning and the resulting decisions have a direct impact on reducing waste, including energy and idle capacity. Batch scheduling problems occur in various industries from automotive to food and energy. This paper introduces the parallel p-batch scheduling problem with batch delivery, content-dependent loading/unloading times and energy-aware objective function. The problem has been motivated by a real system used for freezing products in a food processing company. A mixed-integer linear programming model (MILP) has been developed and explained through a numerical example. As it is not practical to solve large-size instances via a mathematical model, the discrete differential evolution algorithm has been improved (iDDE) and hybridised with the genetic algorithm (GA). A release-oriented vector generation procedure and a heuristic batch formation mechanism have been developed to efficiently solve the problem. The performance of the proposed approach (iDDEGA) has been compared with CPLEX, iDDE and GA through a comprehensive computational study. A case study was conducted based on real data collected from the freezing process of the company, which also verified the practical use and advantages of the proposed methodology.

关键词： Parallel batch processing batch delivery freezing room hybrid discrete differential evolution genetic algorithm approach batch scheduling mixed-integer linear programming

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Steady-state analysis of self-excited induction generators using genetic algorithm approach under different operating modes

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 2013年第4期32卷 244-258页

作者： Joshi, Dheeraj Sandhu, K. S. Bansal, R. C. Natl Inst Technol Elect Engn Dept Kurukshetra Haryana India Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld Australia

The main focus of this paper is the steady-state analysis of self-excited induction generators (SEIGs). It employs the genetic algorithm approach (GAA) to estimate the steady-state performance of these machines. Further, theGAAis used for the solution of problems related to the operation of a number of SEIGs running in parallel. GA-based modelling is found to be effective to determine the generated voltage and frequency. Experimental results validate the proposed methodology.

关键词： steady-state analysis self-excited induction generators genetic algorithm approach parallel soperation

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A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator

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IFAC-PapersOnLine 2016年第12期49卷 1261-1266页

作者： West, C. Montazeri, A. Monk, S.D. Taylor, C.J. Engineering Department Lancaster University Gillow Avenue LancasterLA14YW United Kingdom

In this paper the problem of dynamic modeling and parameter estimation of a seven degree of freedom hydraulic manipulator is investigated. The numerical model is developed in Simulink using Sim Mechanic and Simscape toolboxes with unknown/uncertain parameters. The aim of this paper is to develop a mechanism that enables us to find a feasible set of parameters for the robot that is consistent with measurements of the input, output, and states of the system under noisy and unknown operating conditions. As the first step a genetic algorithm is developed to solve an output error system identification problem for a specific joint, i.e. joint 2, such that the parameters of the joint converge to the desired set of parameters within an acceptable accuracy. The results can be straightforwardly extended to all joints of the manipulator. © 2016

关键词： Parameter estimation Degrees of freedom (mechanics) genetic algorithms Identification (control systems) Manipulators Mathematical models Religious buildings Degree of freedom genetic algorithm approach Hydraulic manipulator Non linear model Operating condition Output error systems Parameter optimization Robotic manipulators

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Human resources optimization in hospital emergency using the genetic algorithm approach

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INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2021年第4期14卷 1441-1448页

作者： Apornak, Arash Raissi, Sadigh Keramati, Abbas Khalili-Damghani, Kaveh Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran Univ Tehran Sch Ind Engn Fac Engn Tehran Iran Ryerson Univ Ted Roger Sch Management Informat Technol Management Dept Toronto ON Canada

Human resource (HR) planning acts as an evaluating process staffing requirement for current and short-time future activities. This can be a complicated decision making process in an organization. It also requires responding to issues and taking action to close the gap between its current and future needs. The current research aimed to follow an evolutionary algorithm for such problem using the genetic algorithm (GA). During current research data gathered from a period of 36 months in a big hospital with 108 direct staffs of health care affairs. Here the fuzzy Delphi was applied to identify the influencing factors on the optimization of human resources. In order to minimize annual direct HR cost as well maximizing affaires, a composition fitness established. The proposed algorithm identified the number of specialist, general practitioners and nurses in three shifts after 500 of generations. The proposed plan promotes the fitness function about 36% and includes identifying the skills and abilities to encounter potential demand for emergency affairs.

关键词： Optimization human resources hospital emergency genetic algorithm approach fuzzy Delphi

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Parametric approach and genetic algorithm for multi objective linear programming with imprecise parameters

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OPSEARCH 2010年第1期47卷 73-92页

作者： Chakraborty, M. Ray, Ananya Indian Sch Mines Dept Appl Math Dhanbad 826004 Bihar India

Many real world decision making problems are multi objective in nature. However, in some cases the model parameters are imprecise in nature. Such type of problems cannot be solved using classical techniques. These modelling complications can be handled with the help of the concept developed in the theory of fuzzy sets. For the imprecise parameters the model users are normally able to give realistic intervals. Using parametric approach the fuzzy multi objective model may be reduced to multi objective linear programming (MOLP) with crisp parameters. genetic algorithm (GA) is a powerful technique to solve multi objective decision making problems. A set of non-dominated pareto optimal solutions may be obtained with this approach. In this paper the MOLP with imprecise parameter has been considered and solved using parametric approach and GA. To illustrate the procedure a numerical example has been solved. A case study has been done for the allocation of coal and its by-products from a mine establishment to different consumption sites. The transportation cost, availability and the demands are defined by a realistic interval. The problem is solved by GA approach and efficient numerical solution has been found.

关键词： Multi objective Linear programming genetic algorithm approach Parametric programming

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Neural network based on a genetic algorithm for power system loading margin estimation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第11期6卷 1153-1163页

作者： Razmi, H. Teshnehlab, M. Shayanfar, H. A. Islamic Azad Univ Sci & Res Branch Dept Elect Engn Tehran Iran KN Toosi Univ Technol Fac Elect & Comp Engn Ind Control Ctr Excellence Tehran Iran Iran Univ Sci & Technol Ctr Excellence Power Syst Automat & Operat Dept Elect Engn Tehran Iran

In this study, a new methodology based on the predictor-corrector method is introduced to trace the total system equilibrium of the power system model. This methodology considers the automatic voltage regulator voltage limit of all generation units and computes the loading margin associated with both saddle node bifurcation and saddle limit induced bifurcation points. Furthermore, a neural network based on the genetic algorithm approach is presented for fast voltage stability analysis. The neural network can establish a mapping between the operating conditions and the power system loading margin obtained by the above simultaneous equilibrium tracing technique. In order to improve the neural network efficiency, the genetic algorithm is used to generate the optimal feature subset and the neural network parameters at the same time. The method is examined on the New England 39-bus system and a mean absolute estimation error of 0.57% and a response time <0.1 s are obtained. By comparing the results obtained through different methods, it is concluded that the genetic algorithm-neural network approach provides a compromise between accuracy and speed of calculation. Hence, for operational purposes, for which having a fast response is vital, using the genetic algorithm-neural network approach is recommended.

关键词： Neural computing techniques simultaneous equilibrium tracing technique predictor-corrector method Other numerical methods bifurcation neural network efficiency Optimisation techniques neural nets power system model predictor-corrector methods saddle limit induced bifurcation points Power system control genetic algorithms fast voltage stability analysis total system equilibrium power system loading margin estimation power system state estimation generation units New England 39-bus system power system stability genetic algorithm approach Power engineering computing power system simulation mean absolute estimation error saddle node bifurcation automatic voltage regulator

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