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A comparison of fitness functions for the identification of a piezoelectric hysteretic actuator based on the real-coded genetic algorithm

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SENSORS AND ACTUATORS A-PHYSICAL 2006年第2期132卷 643-650页

作者： Ha, Jih-Lian Kung, Ying-Shieh Fung, Rong-Fong Hsien, Shao-Chien Natl Kaohsiung First Univ Sci & Technol Dept Mech & Automat Engn Kaohsiung 824 Taiwan So Taiwan Univ Technol Dept Elect Engn Tainan 710 Hsien Taiwan Far E Coll Dept Mech Engn Tainan 744 Taiwan

The main objective of this study is to identify the piezoelectric actuator (PA) with hysteresis, which is described by Bouc-Wen model. The identification method based on real-coded genetic algorithm (RGA) has the advantages not only to, identify the parameters of the hysteresis model but also the mass, stiffness, damping coefficient and piezoelectric coefficient of the PA simultaneously. Furthermore, the selection of fitness functions in the RGA is the key point to influence the final identified results. In this paper, three types of fitness functions are designed to identify the hysteretic PA and their differences are discussed. The numerical simulations and experimental results demonstrate that the identification method based on the RGA with suitable fitness functions is feasible. (c) 2006 Elsevier B.V. All rights reserved.

关键词： identification piezoelectric actuator hysteretic model real-coded genetic algorithm

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An enhancement of selection and crossover operations in real-coded genetic algorithm for large-dimensionality optimization

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2016年第1期30卷 237-247页

作者： Kwak, Noh Sung Lee, Jongsoo Yonsei Univ Sch Mech Engn Seoul 120749 South Korea

The present study aims to implement a new selection method and a novel crossover operation in a real-coded genetic algorithm. The proposed selection method facilitates the establishment of a successively evolved population by combining several subpopulations: an elitist subpopulation, an off-spring subpopulation and a mutated subpopulation. A probabilistic crossover is performed based on the measure of probabilistic distance between the individuals. The concept of 'allowance' is suggested to describe the level of variance in the crossover operation. A number of nonlinear/non-convex functions and engineering optimization problems are explored to verify the capacities of the proposed strategies. The results are compared with those obtained from other genetic and nature-inspired algorithms.

关键词： real-coded genetic algorithm Large-scale global optimization Subpopulation Probabilistic crossover

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A real-coded genetic algorithm for optimizing the damping response of composite laminates

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COMPUTERS & STRUCTURES 2018年 198卷 51-60页

作者： Pathan, M. V. Patsias, S. Tagarielli, V. L. Imperial Coll London Dept Aeronaut London SW7 2AZ England Rolls Royce Plc POB 31 Derby DE24 8BJ England

We develop a real-coded constrained genetic algorithm (GA) and assess its performance for the case of selected classical optimisation problems. The proposed GA uses a roulette selection method, BLX-alpha cross-over operation, non-uniform mutation along with single elitist selection at every generation. The GA is then applied, in conjunction with the finite element (FE) method, to optimise the damping response of a laminate comprising unidirectional composite laminae and viscoelastic damping layers. Modal loss factors are maximised against the constraints of given structural stiffness and mass. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： real-coded genetic algorithm Composite laminates Optimization Damping

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Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

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Journal of Central South University 2012年第6期19卷 1764-1770页

作者：李守巨邵龙潭王吉喆刘迎曦 State Key Laboratory of Structural Analysis for Industrial Equipment(Dalian University of Technology) The Second Affiliated Hospital of Dalian Medical University

The hybrid genetic algorithm is utilized to facilitate model parameter *** tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of *** order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization *** numerical and experimental results for conditioned soil are *** forecast strains based on identified nonlinear constitutive model of soil agree well with observed *** effectiveness and accuracy of proposed parameter estimation approach are validated.

关键词： parameter estimation real-coded genetic algorithm tri-dimensional compression test gradient-based optimization

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A real-coded genetic algorithm with a direction-based crossover operator

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INFORMATION SCIENCES 2015年 305卷 320-348页

作者： Chuang, Yao-Chen Chen, Chyi-Tsong Hwang, Chyi Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan Natl Quemoy Univ Ctr Gen Educ Jinning Township 892 Kinmen Taiwan

In this paper, we develop a parallel-structured real-coded genetic algorithm (RCGA), named the RGA-RDD, for numerical optimization. Technically, the proposed RGA-RDD integrates three specially designed evolutionary operators - the Ranking Selection (RS), Direction-Based Crossover (DBX), and the Dynamic Random Mutation (DRM) - as a whole to mimic a specific evolutionary process. Unlike the conventional RCGAs that perform evolutionary operators in a series framework, the RGA-RDD embeds a coordinator in the inner parallel loop to organize the operations of the DBX and DRM so that a higher possibility of locating the global optimum is ensured. Besides, based on the results of a systematic parametric analysis, we provide a parameter selection guideline for the settings of the proposed RGA-RDD. Furthermore, a data-driven optimization scheme, which incorporates the uniform design for design of experiments and a shape-tunable neural network for auxiliary decision support, is applied to search for an optimal set of the algorithm parameters. The effectiveness and applicability of the proposed RGA-RDD are demonstrated through a variety of benchmarked optimization problems, followed by comprehensive comparisons with some existing state-of-the-art evolutionary algorithms. Extensive simulation results reveal that the performance of the proposed RGA-RDD is superior to comparative methods in locating the global optimum for real-parameter optimization problems, especially for unsolved multimodal and high-dimensional hybrid functions. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithm real-coded genetic algorithm Parallel structure real-parameter optimization Data-driven optimization scheme Parameter tuning

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A New Optimization Model for MLP Hyperparameter Tuning: Modeling and Resolution by real-coded genetic algorithm

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NEURAL PROCESSING LETTERS 2024年第2期56卷 1-31页

作者： El-Hassani, Fatima Zahrae Amri, Meryem Joudar, Nour-Eddine Haddouch, Khalid Sidi Mohamed Ben Abdellah Univ ENSA Engn Syst & Applicat Lab Fes Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol Fez Dept Math Modelling & Math Struct Lab Fes Morocco

This paper introduces an efficient real-coded genetic algorithm (RCGA) evolved for constrained real-parameter optimization. This novel RCGA incorporates three specially crafted evolutionary operators: Tournament Selection (RS) with elitism, Simulated Binary Crossover (SBX), and Polynomial Mutation (PM). The application of this RCGA is directed toward optimizing the MLPRGA+5 model. This model is designed to configure Multilayer Perceptron neural networks by optimizing both their architecture and associated hyperparameters, including learning rates, activation functions, and regularization hyperparameters. The objective function employed is the widely recognized learning loss function, commonly used for training neural networks. The integration of this objective function is supported by the introduction of new variables representing MLP hyperparameter values. Additionally, a set of constraints is thoughtfully designed to align with the structure of the Multilayer Perceptron (MLP) and its corresponding hyperparameters. The practicality and effectiveness of the MLPRGA+5 approach are demonstrated through extensive experimentation applied to four datasets from the UCI machine learning repository. The results highlight the remarkable performance of MLPRGA+5, characterized by both complexity reduction and accuracy improvement.

关键词： real-coded genetic algorithm Hyperparameter Constrained optimization Multi-layer perceptron

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New technique for computation of closest Hopf bifurcation point using real-coded genetic algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第1期5卷 11-18页

作者： Phadke, A. R. Fozdar, M. Niazi, K. R. Mithulananthan, N. Bansal, R. C. Malaviya Natl Inst Technol Jaipur Rajasthan India Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

Hopf bifurcation in power systems leads to oscillatory instability, and it is desirable to operate the system with sufficiently large loading margin to Hopf bifurcation. A new technique to determine the shortest distance to Hopf bifurcation is developed. The problem of determining the closest Hopf bifurcation point is formulated as an optimisation problem and solved using real-coded genetic algorithm. The advantage of this method is that it is capable of handling various operational constraints and can determine the closest Hopf bifurcation point accurately even if the hypersurface is not smooth. The proposed approach has been applied on two-area and IEEE 14-bus test systems. The details of implementation and simulation results are presented. The effect of load modelling on closest Hopf bifurcation point is also investigated for various loading patterns.

关键词： optimisation problem Hopf bifurcation point Power system control bifurcation power system stability IEEE 14-bus test systems genetic algorithms real-coded genetic algorithm Optimisation techniques power systems

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Nonconvex economic load dispatch using an efficient real-coded genetic algorithm

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APPLIED SOFT COMPUTING 2009年第1期9卷 321-329页

作者： Kumar, Sushil Naresh, R. Natl Inst Technol Dept Elect Engn Hamirpur 177005 HP India

An efficient optimisation procedure based on real-coded genetic algorithm (RCGA) is proposed for the solution of economic load dispatch (ELD) problem with continuous and nonsmooth/nonconvex cost function and with various constraints being considered. The effectiveness of the proposed algorithm has been demonstrated on different systems considering the transmission losses and valve point loading effect in thermal units. The proposed algorithm is equipped with an effective constraint handling technique, which eliminates the need for penalty parameters. For the purpose of comparison, the same problem has also been solved using binary-coded genetic algorithm (BCGA) and three other popular RCGAs. In the proposed RCGA, simulated binary crossover and polynomial mutation are used against the single point crossover and bit-flipping mutation in BCGA. It has been observed from the test results that the proposed RCGA is more efficient in terms of thermal cost minimisation and execution time for ELD problem with continuous search space than BCGA and some other popular RCGAs. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Economic load dispatch Constraint handling Valve point loading effect real-coded genetic algorithm Simulated binary crossover Polynomial mutation

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A real-coded genetic algorithm for training recurrent neural networks

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NEURAL NETWORKS 2001年第1期14卷 93-105页

作者： Blanco, A Delgado, M Pegalajar, MC Univ Granada Dept Comp Sci & Artificial Intelligence ETSI Informat E-18071 Granada Spain

The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a real-coded genetic algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our genetic algorithm with the real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： recurrent neural network fuzzy recurrent neural network training algorithms real-coded genetic algorithm fuzzy grammatical inference fuzzy finite-state automaton

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

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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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