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.

Assembly Sequence Generation and Optimization for Compliant Assemblies Based on Genetic Algorithm

2011 ◽  
Vol 230-232 ◽  
pp. 978-981
Author(s):  
Yan Feng Xing ◽  
Yan Song Wang ◽  
Xiao Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Initial Population ◽  
Assembly Sequence ◽  
Constraint Function ◽  
Assembly Modeling ◽  
Sequence Generation ◽  
Assembly Sequences ◽  
Assembly Sequence Generation ◽  
Crossover And Mutation

This paper proposes a genetic algorithm to generate and optimize assembly sequences for compliant assemblies. An assembly modeling is presented to describe the geometry of the assembly, which includes three sets of parts, relationships and joints among the parts. Based on the assembly modeling, an assembly sequence is denoted as an individual, which is assigned an evaluation function that consists of the fitness and constraint functions. The fitness function is used to evaluate feasible sequences; in addition, the constraint function is employed to evolve unfeasible sequences. The genetic algorithm starts with a randomly initial population of chromosomes, evolves new populations by using reproduction, crossover and mutation operations, and terminates until acceptable sequences output. Finally an auto-body side assembly is used to illustrate the algorithm of assembly sequence generation and optimization.

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.

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.

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.

scholarly journals An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Ramin Ranjbarzadeh ◽  
Amir Hussein Dadkhah ◽  
Yaghoub Pourasad ◽  
Malika Bendechache
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Method ◽  
Fitness Function ◽  
Jaccard Index ◽  
Diabetic Patients ◽  
Growth Region ◽  
New Approach ◽  
Fuzzy C Means ◽  
Extended Approach ◽  
Selection Of

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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.

Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 961-966
Author(s):  
Fei Xiang ◽  
Shan Li
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Adaptive Mutation ◽  
System Response ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Adaptive Immune ◽  
Design Scheme

For power plant boiler combustion control system has large inertia, nonlinear and other complex characteristics, a control algorithm of PID optimized by means of adaptive immune genetic algorithm is presented. A variety of improved schemes of GA were designed, include: initial population generating scheme, fitness function design scheme, immunization strategy, adaptive crossover probability and adaptive mutation probability design scheme. By taking the rise time, error integral and overshoot of system response as the performance index, and using genetic algorithm for real-coded of PID parameters, then a group of optimal values were obtained. Simulation results show that the method has a good dynamic performance, superior to the conventional PID controller.

Automated Initial-Population Generation for Genetic-Algorithm-Based Assembly Planning

2000 ◽  
Author(s):  
Gregory C. Smith ◽  
Shiang-Fong Chen
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Assembly Planning ◽  
Initial Population ◽  
Search Efficiency ◽  
Assembly Sequence ◽  
Assembly Sequences ◽  
Component Assembly ◽  
Product Assembly ◽  
Initial Assembly

Abstract Genetic algorithms show particular promise for automated assembly planning. As a result, several recent research reports present genetic-algorithm-based mechanical-product assembly planners. However, genetic-algorithm-based assembly planners require an initial assembly-sequence population, and search efficiency greatly depends upon input-population quality. State-of-the-art genetic-algorithm-based assembly planners use one of two techniques for generating an initial assembly-sequence population: use a user-supplied assembly-sequence set or use a randomly generated assembly-sequence set. Generating a user-supplied initial population requires a substantial amount of manpower. Using a randomly generated initial population reduces search efficiency. As a result, we propose an algorithm for automatically generating an initial assembly-sequence population. Our algorithm calculates component assembly complexity and uses both component assembly complexity and component connectivity to automatically generate a valid assembly-sequence population. Using automatically generated initial populations, we achieve search efficiencies comparable to search efficiencies achieved when using user-supplied initial assembly-sequence populations, while eliminating manpower required to generate user-supplied assembly sequences.

Land Use and Transportation Planning for Twin Cities Using a Genetic Algorithm

2000 ◽  
Vol 1722 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Richard J. Balling ◽  
John Taber ◽  
Kirsten Day ◽  
Scott Wilson
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Transportation Planning ◽  
Fitness Function ◽  
High Growth ◽  
Pareto Set ◽  
Decision Makers ◽  
Twin Cities ◽  
New Approach ◽  
Future Plans

A new approach to future land use and transportation planning for high-growth cities is presented. The approach employs a genetic algorithm to efficiently search through hundreds of thousands of possible future plans. A new fitness function is developed to guide the genetic algorithm toward a Pareto set of plans for the multiple competing objectives that are involved. This set may be placed before decision makers. A Pareto set scanner also is described that assists decision makers in shopping through the Pareto set to select a plan. Some of the differences between simultaneous planning and separate planning of highly coupled twin cities also are examined.

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.

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