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Critical Buckling of Prestress-Stable Tensegrity Structures Solved by real-coded genetic algorithm

INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2018年第4期18卷

作者： Zhang, Pei Feng, Jian Southeast Univ Sch Civil Engn Natl Prestress Engn Res Ctr Nanjing 210096 Jiangsu Peoples R China

Tensegrity structures are classified as kinematically determinate ones with two subcases and kinematically indeterminate ones with three subcases in view of their respective stability properties. How the stiffness of a tensegrity structure changes as the level of prestress changes is explored for different scenarios using six carefully chosen samples. For a tensegrity structure merely satisfying the prestress-stability condition, a new optimization model is presented to determine its critical buckling state corresponding to zero stiffness. A real-coded genetic algorithm (RCGA) is then developed to solve this problem, featured by the fact that a special sign-control technique is embedded in the fundamental genetic operations, making the individuals generated in each step fall into the admissible region automatically. The stability of the neutral equilibrium state for tensegrity structures violating the prestress-stability condition is also discussed. Several numerical examples are tested to validate the efficiency of the present approach.

关键词： Buckling tensegrity structure prestress-stability real-coded genetic algorithm neutral equilibrium state

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Parameter optimization and sensitivity analysis for large kinetic models using a real-coded genetic algorithm

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GENE 2013年第1期518卷 84-90页

作者： Tohsato, Yukako Ikuta, Kunihiko Shionoya, Akitaka Mazaki, Yusaku Ito, Masahiro Ritsumeikan Univ Coll Life Sci Dept Bioinformat Kusatsu Shiga 5258577 Japan

Dynamic modeling is a powerful tool for predicting changes in metabolic regulation. However, a large number of input parameters, including kinetic constants and initial metabolite concentrations, are required to construct a kinetic model. Therefore, it is important not only to optimize the kinetic parameters, but also to investigate the effects of their perturbations on the overall system. We investigated the efficiency of the use of a real-coded genetic algorithm (RCGA) for parameter optimization and sensitivity analysis in the case of a large kinetic model involving glycolysis and the pentose phosphate pathway in Escherichia coli K-12. Sensitivity analysis of the kinetic model using an RCGA demonstrated that the input parameter values had different effects on model outputs. The results showed highly influential parameters in the model and their allowable ranges for maintaining metabolite-level stability. Furthermore, it was revealed that changes in these influential parameters may complement one another. This study presents an efficient approach based on the use of an RCGA for optimizing and analyzing parameters in large kinetic models. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Parameter sensitivity Global parameter sensitivity Parameter correlation Kinetic model real-coded genetic algorithm

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Asset portfolio optimization using support vector machines and real-coded genetic algorithm

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JOURNAL OF GLOBAL OPTIMIZATION 2012年第2期53卷 297-315页

作者： Gupta, Pankaj Mehlawat, Mukesh Kumar Mittal, Garima Univ Delhi Dept Operat Res Delhi 110007 India

This paper presents an integrated approach for portfolio selection in a multicriteria decision making framework. Firstly, we use Support Vector Machines for classifying financial assets in three pre-defined classes, based on their performance on some key financial criteria. Next, we employ real-coded genetic algorithm to solve a mathematical model of the multicriteria portfolio selection problem in the respective classes incorporating investor-preferences.

关键词： Portfolio optimization Support vector machines real-coded genetic algorithm Multicriteria decision making

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Asset portfolio optimization using support vector machines and real-coded genetic algorithm

Asset portfolio optimization using support vector machines a...

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International Conference on Optimization and its Applications (ICOIA)

作者： Gupta, Pankaj Mehlawat, Mukesh Kumar Mittal, Garima Univ Delhi Dept Operat Res Delhi 110007 India

关键词： Portfolio optimization Support vector machines real-coded genetic algorithm Multicriteria decision making

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Self-tuning Fuzzy Logic Control of Greenhouse Temperature using real-coded genetic algorithm

Self-tuning Fuzzy Logic Control of Greenhouse Temperature us...

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9th International Conference on Control, Automation, Robotics and Vision

作者： Xu, Fang Chen, Jiaoliao Zhang, Libin Zhan, Hongwu Zhejiang Univ Technol MOE Key Lab Mech Manufacture & Automat Hangzhou 310032 Peoples R China

ISBN: (纸本)9781424403417

The greenhouse temperature model is built based on the balance of the energy. A new real-coded genetic algorithm (GA) for self-tuning fuzzy logic control (FLQ of greenhouse temperature is proposed, in which, an arithmetical crossover operator, a ranldng-based reproduction operator and a non-uniform mutation operator are adopted. The Gaussian input membership functions for the error and the change-in-error of the temperature of FLC is optimized by GA in terms of the root-mean-square error (RMSE) with setpoint and input energy. Compared with the basic fuzzy control, the tuned FLC gives better performance in terms of improving control precision and saving energy.

关键词： greenhouse temperature fuzzy logic control real-coded genetic algorithm self-tuning

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A Direction-Based Exponential Mutation Operator for real-coded genetic algorithm 5

A Direction-Based Exponential Mutation Operator for Real-Cod...

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5th International Conference on Emerging Applications of Information Technology (EAIT)

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

ISBN: (纸本)9781538637197

In genetic algorithm (GA), mutation is one of the most important operators responsible for maintaining diversity in the population. For a real-coded genetic algorithm (RCGA), this mutation operator is applied variable-wise. In this study, a new direction-based exponential mutation operator (DEM) has been proposed for an RCGA. This newly developed operator is influenced by the directional information of the design variables. The locations of mutated solutions are made biased to the said information of the problem with the higher probability value. The performance of the proposed operator with an RCGA has been tested on twenty classical benchmark optimization functions and these results are compared with that of an RCGA with polynomial mutation operator. The said comparison clearly shows the superiority of the proposed operator.

关键词： real-coded genetic algorithm Mutation operator Directional genetic algorithm Exponential mutation

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Integrated real-coded genetic algorithm and Particle Swarm Optimization for Solving Constrained Global Optimization Problems

Integrated Real-Coded Genetic Algorithm and Particle Swarm O...

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International Conference on Computer Science and Education (CSE 2011)

作者： Wu, Jui-Yu Lunghwa Univ Sci & Technol Dept Business Adm Tao Yuan Taiwan

ISBN: (纸本)9783642224171

This work presents an integrated real-coded genetic algorithm and particle swarm optimization with a penalty function method (RGA-PSO) to solve constrained global optimization (CGO) problems. An outer RCA is used to optimize the parameter settings of an inner PSO algorithm, which includes the constriction coefficient, cognitive parameter, social parameter, penalty parameter and mutation probability. To increase the diversity of particles, the inner PSO method uses a multi-non-uniform operator. Performance of the proposed RGA-PSO algorithm is evaluated using a set of benchmark COO problems. Additionally, this work compares the numerical results obtained using the proposed RGA-PSO algorithm with those of GAs and artificial immune algorithms (AIAs). Experimental results indicate that the proposed RGA-PSO algorithm converges to a global optimum solution to a CGO problem, and that the performance of the proposed RGA-PSO algorithm is competitive with those of GAs and AIAs. Although a hybrid method increases the complexity of an algorithm, the proposed RGA-PSO algorithm can obtain the optimum parameter settings for the inner PSO algorithm by using the outer RGA and can find a global optimum solution to a COO problem by using the inner PSO algorithm, simultaneously. Hence, the proposed RGA-PSO algorithm can be considered as an alternative stochastic global optimization method for solving CGO problems.

关键词： real-coded genetic algorithm particle swarm optimization nonlinear programming constrained global optimization

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Remarks on Multi-layer Quantum Neural Network Controller Trained by real-coded genetic algorithm

Remarks on Multi-layer Quantum Neural Network Controller Tra...

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2nd Sino-Foreign-Interchange Workshop on Intelligence Science and Intelligent Data Engineering (IScIDE)

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

ISBN: (纸本)9783642319181;9783642319198

This paper investigates quantum neural networks and discusses its application to controlling systems. Multi-layer quantum neural networks having qubit neurons as its information processing unit are considered and a direct neural network controller using the multi-layer quantum neural networks is proposed. A real-coded genetic algorithm is applied instead of a back-propagation algorithm for the supervised training of the multi-layer quantum neural networks to improve learning performance. To evaluate the capability of the direct quantum neural network controller, computational experiments are conducted for controlling a discrete-time system and a nonholonomic system - in this study a two-wheeled robot. Experimental results confirm the effectiveness of the real-coded genetic algorithm for the training of the quantum neural networks and show both feasibility and robustness of the direct quantum neural control system.

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

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Using A real-coded genetic algorithm to Predict Characterization Parameters for Petroleum Fraction 3

Using A Real-coded Genetic Algorithm to Predict Characteriza...

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3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Belghit, Chafik Lahiouel, Yasmina Albahri, Tareq A. Univ Larbi Tebessi Dept Sci Matiere Tebess Algeria Univ 8 Mai 1945 Dept Genie Proc Lab Silicates Polymeres & Nanocompositesline POB 401 Guelma Algeria Kuwait Univ Chem Engn Dept POB 5969 Safat 13060 Kuwait

ISBN: (纸本)9781538642382

In this paper, an efficient real-coded genetic algorithm (RCGA) for process optimization is developed to build new correlations to estimate properties of petroleum. This equation is a function of two input parameters that can be easily obtained. Moreover, an available data bank was used to develop the correlations using optimization. general experiment results reveal that the proposed RCGA is simple to use and provides a significantly faster convergence speed and much better search performance than comparative methods. The results of the proposed models are compared to others recommended in literature that have had large acceptance in the oil industry. The comparison results indicate that the proposed model is more precise than the most common models for characterizing petroleum fractions.

关键词： real-coded genetic algorithm Characterization correlation Petroleum fraction

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A hybrid real-coded genetic algorithm with forgetting and its applications

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International Congress Series 2006年 1291卷 189-192页

作者： Zhang, H. Ishikawa, M. Department of Brain Science and Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu 808-0196 2-4 Hibikino Wakamatsu Japan

This paper proposes a hybrid real-coded genetic algorithm with forgetting for improving the generalization ability of classification models. A crucial idea here is the introduction of structural learning with forgetting into a hybrid real-coded genetic algorithm. The proposed method has two advantages: (1) finding near optimal classification models efficiently by a hybrid technique and (2) improving the generalization ability of the resulting classification models by the forgetting technique. Applications of the proposed method to an iris classification problem well demonstrate its effectiveness. Our results indicate that it has not only high learning performance for training data, but also high generalization ability for the test data compared with conventional algorithms such as backpropagation and structural learning with forgetting. © 2006 Elsevier B.V. All rights reserved.

关键词： Local search Multilayer preceptron real-coded genetic algorithm Structural learning with forgetting

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