Mechanical Optimization with Unknown Mathematical Model Based on Improved Adaptive Genetic Algorithm

2011 ◽  
Vol 287-290 ◽  
pp. 2252-2255
Author(s):  
Zhi Bing Li ◽  
Shi De Xiao
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Constraint Equation ◽  
Convergence Speed ◽  
Fast Convergence ◽  
Adaptive Genetic Algorithm ◽  
Computational Dynamics ◽  
Dynamics Of Multibody Systems ◽  
Mechanical Optimization ◽  
Improved Methods

Adaptive genetic algorithm (AGA) is wildly used nowadays for its features such as global searching, fast convergence speed and so on. But it will be difficult to build its fitness function or constraint equation some times. On the contrast, ADAMS is a dynamic analysis software based on the theory Computational Dynamics of Multibody Systems, it is good at physical model analysis. In order to make full use of both advantages, this paper is aim at combination of AGA with ADAMS. What is more, some improved methods are used in AGA. The optimal design of car hood mechanism shows that the use of improved adaptive genetic algorithm (IAGA) based on ADAMS is feasible. Form the result of simulation; it is easy to see that IAGA have better optimal precision and fast convergence speed compared with AGA.

scholarly journals Limited adaptive genetic algorithm for inner-plant economical operation of hydropower station

2012 ◽  
Vol 44 (4) ◽  
pp. 583-599 ◽  
Author(s):  
Jiao Zheng ◽  
Kan Yang ◽  
Xiuyuan Lu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Convergence Speed ◽  
Hydropower Station ◽  
Adaptive Genetic Algorithm ◽  
Parameter Method ◽  
Initial Population ◽  
Solution Strategy ◽  
Adaptive Parameter ◽  
Solution Generation

A limited adaptive genetic algorithm (LAGA) is proposed in the paper for inner-plant economical operation of a hydropower station. In the LAGA, limited solution strategy, with the feasible solution generation method for generating an initial population and the limited perturbation mutation operator, is presented to avoid hydro units operating in cavitation–vibration regions. The adaptive probabilities of crossover and mutation are introduced to improve the convergence speed of the genetic algorithm (GA). Furthermore, the performance of the limited solution strategy and the adaptive parameter controlling improvement are checked against the historical methods, and the results of simulating inner-plant economical operation of the Three Gorges hydropower station demonstrate the effectiveness of the proposed approach. First, the limited solution strategy can support the safety operations of hydro units by avoiding cavitation–vibration region operations, and it achieves a better solution, because the non-negative fitness function is achieved. Second, the adaptive parameter method is shown to have better performance than other methods, because it realizes the twin goals of maintaining diversity in the population and advancing the convergence speed of GA. Thus, the LAGA is feasible and effective in optimizing inner-plant economical operation of hydropower stations.

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Feng Lu ◽  
Yafan Wang ◽  
Jinquan Huang ◽  
Qihang Wang
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filter ◽  
Fault Diagnosis ◽  
Fitness Function ◽  
Performance Degradation ◽  
Estimation Accuracy ◽  
Adaptive Genetic Algorithm ◽  
Turbofan Engine ◽  
Gas Path Fault ◽  
Fault Mode

AbstractA hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

A Genetic Algorithm for the Production Policy in a Supply Chain under Discrete Demand and Time-Vary Cost

2013 ◽  
Vol 321-324 ◽  
pp. 2137-2140 ◽  
Author(s):  
Bing Chang Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Fitness Function ◽  
Optimal Solution ◽  
Just In Time ◽  
Adaptive Genetic Algorithm ◽  
Production Policy ◽  
Production Scheme ◽  
Distribution Requirement ◽  
Local Optimal Solution

Considering discrete demand and time-vary unit production cost under a foreseeable time horizon, this study presents an adaptive genetic algorithm to determine the production policy for one manufacturer supplying single item to multiple warehouses in a supply chain environment. Based on Distribution Requirement Planning (DRP) and Just in Time (JIT) delivery policy, we assume each gene in chromosome represents a period. Standard GA operators are used to generate new populations. These populations are evaluated by a fitness function using the total cost of production scheme. An explicit procedure for obtaining the local optimal solution is provided.

Application of Intelligent Algorithm to Transformer Optimal Design

2011 ◽  
Vol 219-220 ◽  
pp. 1578-1583
Author(s):  
Shuang Zhang ◽  
Qing He Hu ◽  
Xing Wei Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Penalty Function ◽  
Fitness Function ◽  
Mutation Rates ◽  
Adaptive Genetic Algorithm ◽  
Speed Of Convergence ◽  
Intelligent Algorithm ◽  
Transformer Model ◽  
Crossover And Mutation

The paper studies transformer optimal design, establishes optimal transformer model based on total owning cost. It adopts penalty function to process objective function with weighted coefficients. For prematurity and low speed of convergence of Simple Genetic Algorithm, improved adaptive genetic algorithm is adopted. It increases crossover and mutation rates, and improves fitness function. It is adopted to search for minimum total owning cost of transformer. The result shows that the algorithm performs well, increases converging speed and betters solution.

scholarly journals Multiobjective Optimization of Carbon Fiber-Reinforced Plastic Composite Bumper Based on Adaptive Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Dawei Gao ◽  
Haotian Liang ◽  
Guijie Shi ◽  
Liqin Cao
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Plastic Composite ◽  
Mutation Operators ◽  
Adaptive Crossover

Genetic algorithm (GA) is a common optimization technique that has two fatal limitations: low convergence speed and premature convergence to the local optimum. As an effective method to solve these drawbacks, an adaptive genetic algorithm (AGA) considering adaptive crossover and mutation operators is proposed in this paper. Verified by two test functions, AGA shows higher convergence speed and stronger ability to search the global optimal solutions than GA. To meet the crashworthiness and lightweight demands of automotive bumper design, CFRP material is employed in the bumper beam instead of traditional aluminum. Then, a multiobjective optimization procedure incorporating AGA and the Kriging surrogate model is developed to find the optimal stacking angle sequence of CFRP. Compared with the conventional aluminum bumper, the optimized CFRP bumper exhibits better crashworthiness and achieves 43.19% weight reduction.

Research on Nondestructive Image Compression Technology Based on Genetic Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 789-794
Author(s):  
Jie He ◽  
Hui Guo
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Adaptive Genetic Algorithm ◽  
Coding Method ◽  
Iteration Function ◽  
Block Iteration ◽  
Definition Of ◽  
Adaptive Crossover ◽  
The Individual

Based on nondestructive and block iteration function the characteristics of the system, and put forward a kind of improved the global optimal solution from similar partition adaptive genetic algorithm is proposed. In the algorithm for the father the searching space of the individual pieces by gray coding method; Definition of father and son of minimum error for the match fitness function; Genetic algorithm is put forward the improvement of the linear adaptive crossover and mutation probability; Take excellent protection strategy choice. The experimental results show that this method in the similar image guarantee the quality and the compression ratio decompression also can obviously reduce compressed time, effectively improve the searching efficiency.

scholarly journals Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Actuators ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 4
Author(s):  
Haoting Liu ◽  
Jianyue Ge ◽  
Yuan Wang ◽  
Jiacheng Li ◽  
Kai Ding ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Artificial Bee Colony ◽  
Fitness Function ◽  
Optimal Path ◽  
Adaptive Genetic Algorithm ◽  
Transmission Tower ◽  
Bee Colony ◽  
Searching Strategy ◽  
Revenue Function

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

scholarly journals Prediction of Chaotic Time Series Based on BEN-AGA Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
LiYun Su ◽  
Fan Yang
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Network Model ◽  
Fitness Function ◽  
Activation Function ◽  
Single Step ◽  
Chaotic Time Series ◽  
Adaptive Genetic Algorithm ◽  
Observation Sequence ◽  
The Brain

Aiming at the prediction problem of chaotic time series, this paper proposes a brain emotional network combined with an adaptive genetic algorithm (BEN-AGA) model to predict chaotic time series. First, we improve the emotional brain learning (BEL) model using the activation function to change the two linear structures the amygdala and the orbitofrontal cortex into the nonlinear structure, and then we establish the brain emotional network (BEN) model. The brain emotional network model has stronger nonlinear calculation ability and generalization ability. Next, we use the adaptive genetic algorithm to optimize the parameters of the brain emotional network model. The weights to be optimized in the model are coded as chromosomes. We design the dynamic crossover probability and mutation probability to control the crossover process and the mutation process, and the optimal parameters are selected through the fitness function to evaluate the chromosome. In this way, we increase the approximation capability of the model and increase the calculation speed of the model. Finally, we reconstruct the phase space of the observation sequence based on the short-term predictability of the chaotic time series; then we establish a brain emotional network model and optimize its parameters with an adaptive genetic algorithm and perform a single-step prediction on the optimized model to obtain the prediction error. The model proposed in this paper is applied to the prediction of Rossler chaotic time series and sunspot chaotic time series. The experimental results verify the effectiveness of the BEN-AGA model and show that this model has higher prediction accuracy and more stability than other methods.

scholarly journals Fast error calibration of Flexible Measuring Arm based on an adaptive Genetic Algorithm

2021 ◽  
pp. 002029402110309
Author(s):  
Xinhua Zhao ◽  
Jiahao Wang ◽  
Lei Zhao ◽  
Bin Li ◽  
Haibo Zhou
Keyword(s):  
Genetic Algorithm ◽  
Kinematic Model ◽  
Calibration Method ◽  
Convergence Speed ◽  
Least Square ◽  
Adaptive Genetic Algorithm ◽  
Adaptive Mechanism ◽  
Search Efficiency ◽  
Mutation Operators ◽  
Optimization Calculation

With the development of measurement technology, the Flexible Measuring Arm (FMA) is widely used in quality test of automobile processing and industrial production. FMA is a kind of nonlinear system with many parameters. Low cost and efficient calibration method have become the focuses of attention. This article presents a fast calibration method for FMA based on an adaptive Genetic Algorithm (GA) just with several standard balls and a ball plate. It can greatly reduce the calibration cost than common external calibration method which needs high precision instruments and sensors. Firstly, the kinematic model of FMA is established by RPY theory. Secondly, the common GA is optimized and improved, and an adaptive mechanism is added to the algorithms which can realize the automatic adjustment of crossover and mutation operators. A Normalized Genetic Algorithm (NGA) with adaptive mechanism is proposed to complete the optimization calculation. It can improve the numbers of optimal individuals and the convergence speed. So, the search efficiency will be enhanced greatly. Finally, the Least square method (LSM), the General Genetic Algorithm (GGA), and the proposed NGA are respectively used to finish the calibration work. The compensation accuracy and the search efficiency with the above three different algorithms have been systematically analyzed. Experiment indicates that the performance of NGA is much better than LSM and GGA. The data also has proved that the LSM is suitable to complete optimization calculation for linear system. Its convergence stability is much poorer than NGA and GGA because of the ill-condition Jacobin matrix. GGA is easy to fall into local optimization because of the fixed operators. The proposed NGA obviously owns fast convergence speed, high accuracy and better stability than GGA. The position error is reduced from 3.17 to 0.5 mm after compensation with the proposed NGA. Its convergence rate is almost two time of GGA which applies constant genetic factors. The effectiveness and feasibility of proposed method are verified by experiment.

The Application of the Improved Genetic Algorithm in the Aeroengine Fault Diagnosis

2013 ◽  
Vol 846-847 ◽  
pp. 840-843
Author(s):  
Xiao Bo Liu ◽  
Jun Chao Tu ◽  
Liang Ni Shen
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Fitness Function ◽  
Convergence Speed ◽  
Fault Classification ◽  
Fault Identification ◽  
Improved Genetic Algorithm ◽  
Characteristic Parameters

A improved genetic algorithm is proposed based on a new fitness function in allusion to the problem that the traditional genetic algorithm is not fully consider the knowledge of the problem itself.The improved genetic algorithm is used to analyze the fault feature , to extract the fault and remove redundant characteristic parameters for the fault classification and calculation.The diagnosis example shows that the method has faster convergence speed and can be effective for fault identification.

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