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Nonlinear system identification and control using a real-coded genetic algorithm

APPLIED MATHEMATICAL MODELLING 2007年第3期31卷 541-550页

作者： Chang, Wei-Der Shu Te Univ Dept Comp & Commun Kaohsiung 824 Taiwan

A real-coded genetic algorithm (GA) applied to the system identification and control for a class of nonlinear systems is proposed in this paper. It is well known that GA is a globally optimal method motivated from natural evolutionary concepts. For solving a given optimization problem, there are two different kinds of GA operations: binary coding and real coding. In general, a real-coded GA is more suitable and convenient to deal with most practical engineering applications. In this paper, in the beginning we attempt to utilize a real-coded GA to identify the unknown system which its structure is assumed to be known previously. Next, according to the estimated system model an optimal off-line PID controller is optimally solved by also using the real-coded GA. Two simulated examples are finally given to demonstrate the effectiveness of the proposed method. (c) 2005 Elsevier Inc. All rights reserved.

关键词： nonlinear system identification parameters estimation PID controller real-coded genetic algorithm optimization problem

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Electromagnetic inverse scattering of two-dimensional perfectly conducting objects by real-coded genetic algorithm

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2001年第3期39卷 665-676页

作者： Qing, A Lee, CK Jen, L Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore SW Jiaotong Univ Inst Electromagnet Theory & Microwave Technol Chengdu 610031 Peoples R China

Shape reconstruction of two-dimensional perfectly conducting objects using noisy measured scattering data is considered in this paper. The contour of each conducting object is denoted by a shape function in the local polar coordinate which is approximated by a trigonometric series. A point-matching method is used to solve the scattering problem. The main idea of the inversion algorithm is to cast the inverse problem into a restrained minimization problem and to solve it by the real-coded genetic algorithm (RGA). The performance of this algorithm is demonstrated by numerically reconstructing arbitrarily shaped objects and by a detailed comparison with both the standard genetic algorithm (SGA) and the Newton-Kantorovitch method.

关键词： real-coded genetic algorithm shape reconstruction two-dimensional (2-D) perfectly conducting objects

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An improved real-coded genetic algorithm with random walk based mutation for solving combined heat and power economic dispatch

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第8期12卷 8561-8584页

作者： Haghrah, A. Nekoui, M. A. Nazari-Heris, M. Mohammadi-ivatloo, B. KN Toosi Univ Technol Fac Elect Engn Tehran Iran Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Combined heat and power economic dispatch (CHPED) is an energy management problem that minimizes the operation cost of power and heat generation while a vast variety of operational constraints of the system should be met. The CHPED is a complicated, non-convex and non-linear problem. In this study, a new real-coded genetic algorithm with random walk-based mutation (RCGA-CRWM) is under study, which is effective in solving large-scale CHPED problem with minimum operation cost. In the presented optimization method, a simple approach is introduced to combine the positive features of different probabilistic distributions for the step size of random walk. Using the presented approach, while the genetic algorithm is speeded up, the premature convergence is also avoided. After verifying the performance of the presented method on the benchmark functions, two large-scale and two medium-scale case studies are used for determining the algorithm strength in solving the CHPED problem. Despite the fact that the complexity of the CHPED rises dramatically by increasing its dimensionality, the algorithm has solved the problems accurately. The application of RCGA-CRWM method improves the results of the CHPED problem in terms of both operation cost and convergence speed in comparison with other optimization methods.

关键词： Combined heat and power (CHP) economic dispatch real-coded genetic algorithm random walk complex optimization problem

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Multi-layer quantum neural network controller trained by real-coded genetic algorithm

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NEUROCOMPUTING 2014年 134卷 159-164页

作者： Takahashi, Kazuhiko Kurokawa, Motoki Hashimoto, Masafumi Doshisha Univ Kyoto 6100321 Japan Doshisha Univ Grad Sch Kyoto 6100321 Japan

We investigate a quantum neural network and discuss its application to controlling systems. First, we consider a multi-layer quantum neural network that uses qubit neurons as its information processing unit. Next, we propose a direct neural network controller using the multi-layer quantum neural network. To improve learning performance, instead of applying a back-propagation algorithm for the supervised training of the multi-layer quantum neural network, we apply a real-coded genetic algorithm. To evaluate the capabilities of the direct quantum neural network controller, we conduct computational experiments controlling a discrete-time nonlinear system and a nonholonomic system (a two-wheeled robot). Experimental results confirm the effectiveness of the real-coded genetic algorithm in training a quantum neural network and prove the feasibility and robustness of the direct quantum neural network controller. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Quantum neural network Qubit neuron real-coded genetic algorithm Control Identification

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Quality design method using process capability index based on Monte-Carlo method and real-coded genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2018年 204卷 358-364页

作者： Otsuka, Akimasa Nagata, Fusaomi Sanyo Onoda City Univ 1-1-1 Daigaku Dori Sanyo Onoda Yamaguchi 7560884 Japan

Variability in the performance and quality of products is an important issue in production engineering. Quality variability in mechanical production is due to irregularity of parts dimensions caused by machining errors. The dimensions of each part are usually managed by conventional tolerance at the design stage. Tight tolerance values result in reduced performance variation along with an increase in the manufacturing cost. Therefore tolerancing, which is a downstream process in mechanical design, is important in a detailed design process. Although quality is usually controlled in the manufacturing stage, not only production strategy but also management strategy will change in a positive direction and manufacturing cost is also reduced if the quality is also controlled at the design stage. This is because the design stage is an upstream process in manufacturing. This paper focuses on quality control in the design stage, and proposes a novel design method of process capability, which can statistically control parts dimensions based on product performance. The method consists of a numerical method and a real-coded genetic algorithm. A case study was analysed to evaluate the effectiveness of the proposed method. The result showed that the proposed method suitably allocates the STI for each part so that the product satisfies the required product performance.

关键词： Statistical tolerance index Process capability index Monte-carlo simulation real-coded genetic algorithm Product performance

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NONLINEAR SYSTEM IDENTIFICATION WITH A real-coded genetic algorithm (RCGA)

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2015年第4期25卷 863-875页

作者： Cherif, Imen Fnaiech, Farhat Natl Higher Engn Sch Tunis ENSIT Lab Signal Image & Energy Mastery SIME Tunis 1008 Tunisia

This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.

关键词： blind nonlinear identification Volterra series higher order cumulants real-coded genetic algorithm

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Solving engineering optimization problems using an improved real-coded genetic algorithm (IRGA) with directional mutation and crossover

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SOFT COMPUTING 2021年第7期25卷 5455-5481页

作者： Das, Amit Kumar Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 W Bengal India

genetic algorithm (GA) is used to solve a variety of optimization problems. Mutation operator also is responsible in GA for maintaining a desired level of diversity in the population. Here, a directional mutation operator is proposed for real-coded genetic algorithm (RGA) along with a directional crossover (DX) operator to improve its performance. These evolutionary operators use directional information to guide the search process in the most promising area of the variable space. The performance of an RGA with the proposed mutation operator and directional crossover (DX) is tested on six benchmark optimization problems of different complexities, and the results are compared to that of the RGAs with five other mutation schemes. The proposed IRGA is found to outperform other RGAs in terms of accuracy in the solutions, convergence rate, and computational time, which is established firmly through statistical analysis. Furthermore, the performance of the proposed IRGA is compared to that of a few recently proposed optimization algorithms. The proposed IRGA is seen to yield the superior results compared to that of the said techniques. It is also applied to solve five constrained engineering optimization problems, where again, it has proved its supremacy. The proposed mutation scheme using directional information leads to an efficient search, and consequently, a superior performance is obtained.

关键词： real-coded genetic algorithm Directional mutation Directional crossover Computational time

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Microwave imaging of parallel perfectly conducting cylinders using real-coded genetic algorithm coupled with Newton-Kantorivitch method

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Progress in Electromagnetics Research 2000年 28卷 275-294页

作者： Qing, A. Lee, C.K. School of Electrical and Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore

The electromagnetic inverse scattering problem [l 4] is to recover infor- mation concerning some inaccessible region from the scattered electro- magnetic elds measured outside. It has been one of the most challeng- ing research topics in recent years due to its considerable practical im- portance in various areas of technology [1 14] such as non-destructive evaluation, subsurface and ground-penetrating radar, geophysical re- mote sensing, medical imaging, seismology and target identication etc. However, it is dicult to solve because of its ill-posedness and nonlinearity [1 4, 15 17]. It is the multiple scattering eects that make the electromagnetic inverse scattering problem inherently non- linear while contaminated and insucient measuring data result in the ill-posedness.

关键词： .Inversion Numerical Results conducting Microwave imaging Scattering problem PARALLEL PERFECTLY real-coded genetic algorithm NEWTON-KANTORIVITCH RGA NKM electromagnetic inverse

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Development of a Mutation Operator in a real-coded genetic algorithm for Bridge Model Optimization

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KSCE JOURNAL OF CIVIL ENGINEERING 2024年第5期28卷 1822-1835页

作者： Kim, Jaecheon Han, Manseok Shin, Soobong Inha Univ Dept Civil Engn Incheon 22212 South Korea

A mutation operator in a real-coded genetic algorithm is developed and applied for efficient bridge-model optimization. A mutation operator that changes uniformly or dynamically with a crossover operator is proposed to address optimization problems. The performance of the combined genetic operators was verified using a variety of available test problems based on the convergence and search speed of the global optimal solution. It is shown that the genetic algorithm proposed in this study yields relatively better results than the available algorithms and is more effective in constrained optimization problems. The performance of the proposed genetic algorithm is also verified through a sample study using a field load test for the model optimization of an existing bridge.

关键词： real-coded genetic algorithm Mutation operator Model optimization Field load test Crossover operator

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Selection of real-coded genetic algorithm parameters in solving simulation-optimization problems for the design of water distribution networks

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WATER SUPPLY 2023年第12期23卷 5046-5061页

作者： Kumar, Vidavaluru Hemanth Sai Pramada, S. K. Natl Inst Technol Calicut Calicut 673601 Kerala India

The design of the water distribution network (WDN) is very difficult mainly due to the nonlinear relation between head and flow. The distribution network should also be cost-effective. In any simulation-optimization approach, the computational time requirement is very high for very complex problems. The optimization module plays a crucial role in reducing the computational time. This study aims to apply a simulation-optimization approach to designing a WDN. EPANET is selected as the simulation model and real-coded genetic algorithm (RCGA) is selected as the optimization module. To link the simulation module with the optimization module, a program is written in MATLAB. The developed simulation-optimization approach was applied to two benchmark network problems to check the suitability of the method. The parameters of the RCGA were optimized for the two networks. The computational efficiency of the developed simulation-optimization model is checked based on the number of function evaluations. For both networks, the number of function evaluations to get the optimum network design was less than the number of function evaluations required for other methods mentioned in the literature.

关键词： real-coded genetic algorithm simulation-optimization water distribution system

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