Research on Optimization Performance of Nonlinear Function Based on Multigroup Genetic Algorithm

During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite.

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Design of an Intelligent NPID Controller Based on Genetic Algorithm for Disturbance Rejection in Single Integrating Process with Time Delay

Journal of Marine Science and Engineering ◽

10.3390/jmse9010025 ◽

2020 ◽

Vol 9 (1) ◽

pp. 25

Author(s):

GunBaek So

Keyword(s):

Genetic Algorithm ◽

Time Delay ◽

Pid Controller ◽

Nonlinear Function ◽

Absolute Error ◽

Open Loop ◽

Real Coded Genetic Algorithm ◽

On Line ◽

Response Performance ◽

Integrating Process

The integrating process with time delay (IPTD) is a fundamentally unstable open-loop system due to poles at the origin of the transfer function, and designing controllers with satisfactory control performance is very difficult because of the associated time delay, which is a nonlinear element. Therefore, this study focuses on the design of an intelligent proportional-integral-derivative (PID) controller to improve the regulatory response performance to disturbance in an IPTD, and addresses problems related to optimally tuning each parameter of the controller with a real coded genetic algorithm (RCGA). Each gain of the nonlinear PID (NPID) controller consists of a product of the gains of the linear PID controller and a simple nonlinear function. Each of these nonlinear functions changes the gains in the controller to on line by nonlinearly scaling the error signal. A lead-lag compensator or first-order filter is also added to the controller to mitigate noise, which is a disadvantage of ideal derivative action. The parameters in the controller are optimally tuned by minimizing the integral of time-weighted absolute error (ITAE) using a RCGA. The proposed method is compared with three other methods through simulation to verify its effectiveness.

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Pose Estimation of Round-Shape Workpieces Based on Genetic Algorithm

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.579-580.665 ◽

2013 ◽

Vol 579-580 ◽

pp. 665-669

Author(s):

Gui Chao Lin ◽

Xiang Jun Zou ◽

Meng Si Zhu ◽

Ke Yin Chen ◽

Zhuang Xu Ke

Keyword(s):

Genetic Algorithm ◽

Pose Estimation ◽

Error Function ◽

Estimation Method ◽

Nonlinear Function ◽

Transformation Model ◽

Estimation Methods ◽

Improved Genetic Algorithm ◽

Analytic Equation ◽

Round Shape

Since traditional pose estimation methods with the features of points, lines and so on might not be applied directly to round-shape workpieces, a new pose estimation method for round-shape workpieces genetic algorithm based was proposed. Compared with previous studies, this method needs no auxiliary information, such as points, lines, concentric circles and so on. Firstly, transformation model of perspective projection of round-shape workpiece was created, and the round-shape workpiece was characterized by analytic equation. Secondly, via detecting the contour and extracting its feature of workpiece, a feature error function was established with respect to the pose angles, which was a multi-objective nonlinear function. Finally, the error function was solved by an improved genetic algorithm and the pose estimation of round-shape workpiece was achieved. The result of related experiments showed that the method had high accuracy, and to some extent inhibited the effects of noise.

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Longitudinal Nonlinear PID Controller Parameter Optimization of Autopilot Using Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.709.252 ◽

2014 ◽

Vol 709 ◽

pp. 252-255 ◽

Cited By ~ 1

Author(s):

Xin Zhao ◽

Wei Ping Zhao ◽

Song Xiang

Keyword(s):

Genetic Algorithm ◽

Parameter Optimization ◽

Pid Controller ◽

Channel Model ◽

Time Integration ◽

Nonlinear Function ◽

Controller Parameter ◽

Nonlinear Pid Controller ◽

Rate Optimization ◽

Longitudinal Channel

This paper performed the longitudinal nonlinear PID Controller parameter optimization of general aircraft autopilot based on the longitudinal channel model and genetic algorithm. Proportion, integration and differential gain of nonlinear PID Controller is nonlinear function of controlling error. The objection function involves time integration of error’s absolute value, output of controller and system overshoot. The longitudinal controlling rate optimization of general aircraft autopilot is realized by minimizing the objection function value. Simulation results show that controller designed by the present method is better than traditional PID controller.

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GUIDED HYBRID GENETIC ALGORITHM FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

Radio Electronics Computer Science Control ◽

10.15588/1607-3274-2021-2-18 ◽

2021 ◽

pp. 174-188

Author(s):

S. E. Avramenko ◽

T. A. Zheldak ◽

L. S. Koriashkina

Keyword(s):

Genetic Algorithm ◽

Global Optimization ◽

Local Search ◽

Optimization Problems ◽

Hybrid Methods ◽

Hybrid Genetic Algorithm ◽

Solution Process ◽

Nonlinear Function ◽

Search Space ◽

Guided Local Search

Context. One of the leading problems in the world of artificial intelligence is the optimization of complex systems, which is often represented as a nonlinear function that needs to be minimized. Such functions can be multimodal, non-differentiable, and even set as a black box. Building effective methods for solving global optimization problems raises great interest among scientists. Objective. Development of a new hybrid genetic algorithm for solving global optimization problems, which is faster than existing analogues. Methods. One of the crucial challenges for hybrid methods in solving nonlinear global optimization problems is the rational use of local search, as its application is accompanied by quite expensive computational costs. This paper proposes a new GBOHGA hybrid genetic algorithm that reproduces guided local search and combines two successful modifications of genetic algorithms. The first one is BOHGA that establishes a qualitative balance between local and global search. The second one is HGDN that prevents reexploration of the previously explored areas of a search space. In addition, a modified bump-function and an adaptive scheme for determining one of its parameters – the radius of the “deflation” of the objective function in the vicinity of the already found local minimum – were presented to accelerate the algorithm. Results. GBOHGA performance compared to other known stochastic search heuristics on a set of 33 test functions in 5 and 25dimensional spaces. The results of computational experiments indicate the competitiveness of GBOHGA, especially in problems with multimodal functions and a large number of variables. Conclusions. The new GBOHGA hybrid algorithm, developed on the basis of the integration of guided local search ideas and BOHGA and HGDN algorithms, allows to save significant computing resources and speed up the solution process of the global optimization problem. It should be used to solve global optimization problems that arise in engineering design, solving organizational and management problems, especially when the mathematical model of the problem is complex and multidimensional.

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A Method to Obtain Parameters of One-Column Jansen–Rit Model Using Genetic Algorithm and Spectral Characteristics

Applied Sciences ◽

10.3390/app11020677 ◽

2021 ◽

Vol 11 (2) ◽

pp. 677

Author(s):

Adam Łysiak ◽

Szczepan Paszkiel

Keyword(s):

Genetic Algorithm ◽

Amplitude Ratio ◽

Spectral Characteristics ◽

Intraclass Correlation ◽

Nonlinear Function ◽

Measured Signal ◽

Eeg Signal ◽

Potential Amplitude ◽

Input Noise ◽

Amplitude Maximum

In this paper, a method of obtaining parameters of one-column Jansen–Rit model was proposed. Methods present in literature are focused on obtaining parameters in an on-line manner, producing a set of parameters for every point in time. The method described in this paper can provide one set of parameters for a whole, arbitrarily long signal. The procedure consists of obtaining specific frequency features, then minimizing mean square error of those features between the measured signal and the modeled signal, using genetic algorithm. This method produces an 8-element vector, which can be treated as an EEG signal feature vector specific for a person. The parameters which were being obtained are maximum postsynaptic potential amplitude, maximum inhibitory potential amplitude, ratio of the number of connections between particular neuron populations, the shape of a nonlinear function transforming the average membrane potential into the firing rate and the input noise range. The method shows high reproducibility (intraclass correlation coefficient for particular parameters ranging from 0.676 to 0.978) and accuracy (ranging from 0.662 to 0.863). It was additionally verified using EEG signal obtained for a single participant. This signal was measured using Emotiv EPOC+ NeuroHeadset.

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