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real-coded genetic algorithm with Differential Evolution Operator for Terahertz Quasi-Optical Power Divider/Combiner Design

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2017年第10期32卷 888-894页

作者： Zhang, Fan Song, Kaijun Fan, Yong Univ Elect Sci & Technol China Sch Elect Engn EHF Key Lab Sci Chengdu 611731 Sichuan Peoples R China

This paper describes a modified real-coded genetic algorithm with a differential evolution operator (RGA-DE) for a shaped reflector of terahertz (THz) quasi-optical power divider/combiner. The real-coded genetic algorithm (RGA) is more convenient to define the reflector surfaces as compared to other coding methods. Interpolation method is applied to smooth the sophisticated surfaces. A better searching ability is obtained in a small number of individuals by using the differential evolution operator instead of the conventional crossover operator. So a bionics algorithm combining real-coded genetic algorithm and differential evolution (DE) algorithm has been presented to design the THz quasi-optical multiple-way holographic power-combining circuits. The RGA-DE has been utilized to optimize the coupling coefficient kappa of the field distribution to get close to 1. Two real-world examples operating at 380 GHz and 450 GHz respectively are described. The computed results are verified by using the commercial software FEKO Suite.

关键词： Differential evolution operator quasi-optical power divider/combiner real-coded genetic algorithm terahertz technology

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real-coded genetic algorithm for machining condition optimization

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第9-10期38卷 884-895页

作者： Kim, Sung Soo Kim, Il-Hwan Mani, V. Kim, Hyung Jun Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India Kangwon Natl Univ Dept Ind Engn Chunchon 200701 South Korea Kangwon Natl Univ Dept Elect & Telecommun Engn Chunchon 200701 South Korea

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

关键词： machining conditions multi-pass turning optimization real-coded genetic algorithm single-pass turning

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real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral-derivative controller in automatic voltage regulator system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2009年第7期3卷 641-649页

作者： Devaraj, D. Selvabala, B. Kalasalingam Univ Power Syst Automat Lab Krishnankoil 626190 Tamil Nadu India

Optimal tuning of proportional-integral-derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.

关键词： automatic voltage regulator system Sugeno fuzzy system optimal tuning Power convertors and power supplies to apparatus Power electronics, supply and supervisory circuits voltage regulators Control of electric power systems genetic algorithms three-term control fuzzy logic proportional-integral-derivative controller fuzzy logic approach PID controller Optimisation techniques Formal logic real-coded genetic algorithm Voltage control

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real-coded genetic algorithm for system identification and controller tuning

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APPLIED MATHEMATICAL MODELLING 2009年第8期33卷 3392-3401页

作者： Valarmathi, K. Devarai, D. Radhakrishnan, T. K. Arulmigu Kalasalingam Coll Engn Krishnankoil 626190 Tamil Nadu India Natl Inst Technol Dept Chem Engn Tiruchirappalli 620015 Tamil Nadu India

This paper presents an application of real-coded genetic algorithm (RGA) for system identification and controller tuning in process plants. The genetic algorithm is applied sequentially for system identification and controller tuning. First CA is applied to identify the changes in system parameters. Once the process parameters are identified, the optimal controller parameters are identified using GA. In the proposed genetic algorithm, the optimization variables are represented as floating point numbers. Also. cross over and mutation operators that can directly deal with the floating point numbers are used. The proposed approach has been applied for system identification and controller tuning in non-linear pH process. The simulation results show that the CA based approach is effective in identifying the parameters of the system and the nonlinearity at various operating points in the nonlinear system. (C) 2008 Elsevier Inc. All rights reserved.

关键词： System identification PID controller real-coded genetic algorithm pH process

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real-coded genetic algorithm and Fuzzy Logic Approach for real-Time Load-Tracking Performance of an Autonomous Power System

Real-Coded Genetic Algorithm and Fuzzy Logic Approach for Re...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Banerjee, Abhik Mukherjee, V. Ghoshal, S. P. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

ISBN: (纸本)9783319037554;9783319037561

This paper focuses on the application of real-coded genetic algorithm (RGA) to determine the optimal controller parameters of an autonomous power system model for its load tracking performance analysis. To determine the real-time parameters of the studied model, Sugeno fuzzy logic (SFL) is used. RGA is applied to obtain the controller parameters for transient response analysis under various operating conditions and fuzzy logic is applied to develop the rule base of the SFL model. The developed fuzzy system gives the on-line controller parameters for different operating conditions. Time-domain simulation of the investigated power system model reveals that the proposed RGA-SFL yields on-line, off-nominal controller parameters, resulting in on-line terminal voltage response. To show the efficiency and effectiveness of RGA, binary coded genetic algorithm is taken for the sake of comparison.

关键词： Automatic voltage regulator load-tracking performance optimization real-coded genetic algorithm Sugeno fuzzy logic

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An improved real-coded genetic algorithm for parameters estimation of nonlinear systems

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2006年第1期20卷 236-246页

作者： Chang, WD Shute Univ Dept Comp & Commun Kaohsiung 824 Taiwan

This paper presents a searching method for parameters estimation of nonlinear system by using a modified real-coded genetic algorithm (GA). It is well known that GA method is an optimal or nearoptimal search technique borrowing the concepts from biological evolutionary theory. The ordinary form of GA used for solving a given optimization problem is a binary encoding during operating procedures. However, in the real applications a real-valued encoding is usually used and is easy to directly implement the programming operations. Thus, in this paper we develop a multi-crossover real-coded GA and utilize it to estimate the parameters of nonlinear process systems, even though those have the term of the time delay or are not linear in the parameters. The effectiveness of the proposed algorithms is compared with different evolutionary algorithms. Simulation results of two kinds of process systems will be illustrated to show that the more accurate estimations can be achieved by using our proposed method. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： system identification parameters estimation multiple crossover real-coded genetic algorithm

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A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2004年第2期26卷 141-154页

作者： Kuo, RJ Chen, JA Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan Natl Taipei Univ Technol Inst Prod Syst Engn & Management Taipei 106 Taiwan

This research attempts to develop a decision support system for order selection. The proposed system is able to integrate both the quantitative and qualitative factors together. For the qualitative factors, the fuzzy IF-THEN rules are summarized from the questionnaire survey for the production experts and learned by a proposed fuzzy neural network (FNN) with initial weights generated by real-coded genetic algorithm (GA). Then, a feedforward artificial neural network (ANN) with error back-propagation (EBP) learning algorithm is employed to integrate the above two parts together. Both the simulation and real-life problem provided by an internationally OEM company results show that the proposed FNN can well learn the fuzzy IF-THEN rules. In addition, real-coded GA is proved to be better than the binary GA both in speed and accuracy. Considering both the quantitative and qualitative factors has more accurate results compared with considering only the quantitative factors. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： order selection electronic commerce artificial neural networks fuzzy neural networks real-coded genetic algorithm

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Fuel consumption minimization of transport aircraft using real-coded genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2018年第10期232卷 1925-1943页

作者： Singh, Vedant Abhilashi Univ Fac Engn Technol Mandi Himachal Prades 175028 India

The issue of rising carbon dioxide emissions from aviation fuel consumption is increasingly crucial in terms of the Paris Agreement on climate change, which was adopted in 2015. The rapid growth of the global transport network has been affecting the environment adversely owing to the emission of greenhouse gases. Therefore, researchers are attempting to minimize aviation fuel consumption. This study presents a fuel consumption minimization problem in transport aircraft design. A real-coded genetic algorithm with direction-based crossover is employed to determine the optimum design for minimum fuel consumption. To mimic the aircraft fuel consumption issues, the proposed real-coded genetic algorithm employs the following three operators: ranking selection, direction-based crossover, and dynamic random mutation. Compared with earlier studies that used uniform crossover, the present study reduces the fuel consumption by applying real-coded genetic algorithm with a direction-based crossover operator. Comprehensive results show that the proposed real-coded genetic algorithm achieves remarkably faster convergence and improved search performance than the compared method. Direction-based crossover significantly enhances the fitness by guiding the crossover along a certain direction. In addition, it ensures a higher probability of locating the global optimum. Therefore, adopting such a technique can reduce the aircraft development cost.

关键词： Fuel consumption optimization preliminary aircraft design real-coded genetic algorithm direction-based crossover

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A new weather-routing system that accounts for ship stability based on a real-coded genetic algorithm

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY 2011年第3期16卷 311-322页

作者： Maki, Atsuo Akimoto, Youhei Nagata, Yuichi Kobayashi, Shigenobu Kobayashi, Eiichi Shiotani, Shigeaki Ohsawa, Teruo Umeda, Naoya Kobe Univ Grad Sch Maritime Sci Higashinada Ku Kobe Hyogo 6580022 Japan Tokyo Inst Technol Interdisciplinary Grad Sch Sci & Engn Yokohama Kanagawa 2268502 Japan Osaka Univ Dept Naval Architecture & Ocean Engn Grad Sch Engn Osaka 5650971 Japan

The operation schedule of an oceangoing vessel can be influenced by wave or wind disturbances, and is therefore weather routed. The weather-routing problem is considered to be a multimodal function problem. Therefore, in the present research, the real-coded genetic algorithm technique (an evolutionary calculation technique) is applied to globally search for the optimum route. Additionally, to avoid maritime accidents due to parametric rolling, this route optimization method takes into account the risk of parametric rolling as one of its objective functions. Numerical verification is carried out for three kinds of objective functions with different weight ratios between fuel efficiency and ship safety in parametric rolling. As a result, it is numerically confirmed that the relation between economics and ship safety is a trade-off, and the safer route is not necessarily the most economical. Considering its robustness, the proposed method appears to be a powerful practical tool by choosing the most appropriate weights for economics and ship safety.

关键词： Weather-routing Ship stability real-coded genetic algorithm

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Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

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SOFT COMPUTING 2007年第11期11卷 1033-1052页

作者： Ling, S. H. Leung, F. H. F. Lam, H. K. Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA Australia Hong Kong Polytech Univ Ctr Signal Proc Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China Kings Coll London Div Engn London WC2R 2LS England

This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7-31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.

关键词： neural network real-coded genetic algorithm short-term load forecasting hand-written recognition

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