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.

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 Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

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.

scholarly journals A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer Detection

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Selim ◽  
Hatem Khater
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Early Stage ◽  
Fitness Function ◽  
Support Vector ◽  
Initial Population ◽  
Tree Classifier ◽  
Selection Approach ◽  
Feature Selection Approach

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

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.

Genetic Algorithm for Assembly Sequences Planning Based on Heuristic Assembly Knowledge

2010 ◽  
Vol 44-47 ◽  
pp. 3657-3661 ◽  
Author(s):  
Hao Pan ◽  
Wen Jun Hou ◽  
Tie Meng Li
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Graph Model ◽  
Initial Population ◽  
New Approach ◽  
Assembly Sequences ◽  
Connection Graph ◽  
Assembly Sequences Planning

To improve the efficiency of Assembly Sequences Planning (ASP), a new approach based on heuristic assembly knowledge and genetic algorithm was proposed. First, Connection Graph of Assembly (CGA) was introduced, and then, assembly knowledge was described in the form of Assembly Rings, on that basis, the assembly connection graph model containing Assembly Rings was defined, and the formation of initial population algorithm was given. In addition, a function was designed to measure the feasible assembly and then the genetic algorithm fitness function was given. Finally, an example was shown to illustrate the effectiveness of the algorithm.

Improved Genetic Algorithm on Minimum Spanning Tree of Length Constraint Problem

2015 ◽  
Vol 713-715 ◽  
pp. 1737-1740
Author(s):  
Ying Ying Duan ◽  
Kang Zhou ◽  
Wen Bo Dong ◽  
Kai Shao
Keyword(s):  
Genetic Algorithm ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Fitness Function ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Length Constraint ◽  
C Programming ◽  
Mutation Operation ◽  
Constraint Problem

The first minimum spanning tree of length constraint problem (MSTLCP) is put forward, which can not be solved by traditional algorithms. In order to solve MSTLCP, improved genetic algorithm is put forward based on the idea of global and feasible searching. In the improved genetic algorithm, chromosome is generated to use binary-encoding, and more reasonable fitness function of improved genetic algorithm is designed according to the characteristics of spanning tree and its cotree; in order to ensure the feasibility of chromosome, more succinct check function is introduced to three kinds of genetic operations of improved genetic algorithm (generation of initial population, parental crossover operation and mutation operation); three kinds of methods are used to expand searching scope of algorithm and to ensure optimality of solution, which are as follows: the strategy of preserving superior individuals is adopted, mutation operation is improved in order to enhance the randomness of the operation, crossover rate and mutation rate are further optimized. The validity and correctness of improved genetic algorithm solving MSTLCP are explained by a simulate experiment where improved genetic algorithm is implemented using C programming language. And experimental results are analyzed: selection of population size and iteration times determines the efficiency and precision of the simulate experiment.

Optimization of the Wireless Sensor Nodes Localization Algorithm Based on Genetic Algorithm

2014 ◽  
Vol 6 (4) ◽  
pp. 55-64
Author(s):  
Tan Zhi ◽  
Zhang Yuting
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Initial Population ◽  
Localization Algorithm ◽  
Node Localization ◽  
Anchor Nodes ◽  
Reference Weight ◽  
Positioning Algorithm

The node localization technology is a foundation for practical application in wireless sensor networks. According to DV-HOP positioning algorithm in wireless sensor network low precision, the defect of inaccurate positioning, this paper presents an optimization algorithm of improved DV-HOP based on genetic algorithm. The algorithm is to redefine the scope of initial population, the reference weight, redesigned the fitness function and selection of anchor nodes. The simulation results show that compared with the traditional DV - HOP algorithm, the algorithm without any increase in the node hardware overhead on the basis of significantly higher positioning accuracy.

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