Parallel Genetic Algorithm for Design of Morphing Cellular Truss Structures

Aerospace ◽  
2005 ◽  
Author(s):  
Deepak S. Ramrakhyani ◽  
George A. Lesieutre ◽  
Mary Frecker ◽  
Smita Bharti
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Unit Cell ◽  
Truss Structures ◽  
Optimization Approach ◽  
Parallel Genetic Algorithm ◽  
Ground Structure ◽  
Structural Layout ◽  
Fitness Value ◽  
Wing Structure

A parallel genetic algorithm is developed for the design of morphing aircraft structures using tendon actuated compliant truss. The wing structure in this concept is made of solid members and cables. The solid members are connected through compliant joints so that they can be deformed relatively easily without storing much strain energy in the structure. The structure is actuated using cables to deform into a required shape. Once the structure is deformed, the cables are locked and hence carry loads. Previously an octahedral unit cell made of cables and truss members was developed to achieve the required shape change of a morphing wing developed at NASA. It was observed that a continuously deformable truss structure with required morphing capability can be achieved by a cellular geometry tailored to local strain deformation. A wing structure made of these unit cells was sized for a representative aircraft and was found to be suitable. This paper describes the development of new unit cell designs that fit the morphing requirements using topology optimization. A ground structure approach is used to set up the problem. A predetermined set of points is selected and the members are connected in between the neighboring nodes. Each member in this ground structure has four possibilities, 1) a truss member, 2) a cable that morphs the structure into a required shape, 3) a cable that is antagonistic and brings it back to the original shape 4) a void, i.e., the member doesn’t exist in the structure. This choice is represented with a discrete variable. A parallel genetic algorithm is used as an optimization approach to optimize the variables in the ground structure to get the best structural layout. The parallelization is done using a master slave process. A fitness function is used to calculate how well a structural layout fits the design requirements. In general, a unit cell that has lesser deflection under external loads and higher deflection under actuation has a higher fitness value. Other requirements such as having fewer cables and achieving a required morphing shape are also included in the fitness function. The finite element calculations in the fitness function can be done using either linear or nonlinear analysis. The paper discusses the different ways of formulating the fitness function and the results thereof.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

2018 ◽  
Vol 7 (4.33) ◽  
pp. 130
Author(s):  
Atiqa Zukreena Zakuan ◽  
Shuzlina Abdul-Rahman ◽  
Hamidah Jantan ◽  
. .
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Succession Planning ◽  
Fitness Function ◽  
Academic Staff ◽  
Fitness Value ◽  
Selection Decision ◽  
The University ◽  
Hierarchy Process

Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.    

scholarly journals MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 969
Author(s):  
Zahra Momeni ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Gradient Boosting ◽  
Range Data ◽  
Parallel Genetic Algorithm ◽  
Data Parallel ◽  
Cpg Sites ◽  
Age Related ◽  
Age Range ◽  
Age Prediction

Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R2) of 95.96% between age and DNAm. In the train data, the MAD and R2 are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.

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 Optimasi Pendistribusian Kelas Pada Dosen di STMIK STIKOM Indonesia Menggunakan Algoritma Genetika

2019 ◽  
Vol 2 (1) ◽  
pp. 145-154
Author(s):  
Aniek Suryanti Kusuma ◽  
Komang Sri Aryati
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Parameters ◽  
Fitness Function ◽  
Genetic Process ◽  
Fitness Value ◽  
Distribution Process ◽  
Class Scheduling ◽  
System Requirements

The stage of class scheduling starts from scheduling courses in classes, then distributing the class to lecturers. The process of distributing classes to lecturers becomes an obstacle for the STMIK STIKOM Indonesia academic body because the academic body must adjust the existing class with the lecturer who is interested in it as well as the lecturer chosen to support a class so that it does not have classes that have a time conflict. One method for solving these problems is by using genetic algorithms that work by generating a number of random solutions and then processing the collection of solutions in a genetic process. There are eight genetic algorithm procedures, which are random chromosome generation procedures, chromosome repair to validate chromosomes from their limits, fitness function to calculate the feasibility of a solution, crossover, mutation, child repair and elitism. The output of this research is in the form of an analysis and determination of the system requirements that must exist. In addition, it produces a trial report on the effect of genetic parameters to determine the effect of changes in the value of genetic parameters on the fitness value and the time used to carry out the distribution process.  

scholarly journals Using Fitness Value for Monitoring Kiwifruit’s Variant Seedling in Tissue Culture

2016 ◽  
Vol 16 (5) ◽  
pp. 59-68
Author(s):  
Shouguo Tang ◽  
Yong Li ◽  
Zhikun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Diversity ◽  
Pattern Recognition ◽  
Tissue Culture ◽  
Genetic Stability ◽  
Genomic Dna ◽  
Dna Polymorphism ◽  
Fitness Function ◽  
Fitness Value ◽  
Pattern Recognition Approach

Abstract Based on Genetic Algorithm, a pattern recognition approach using fitness to dynamically monitor the sub cultured seeding of kiwifruit is proposed in order to decrease the loss of variant seedlings in tissue culture. By coding, selection, mutation and cross-overing the selected primer pairs of the sub cultured seeding, we simulate the process of optimizing the kiwifruit’s genomic DNA polymorphism. The corresponding fitness values of the primer pairs are evaluated with fitness function for monitor the variation of kiwi’s DNA. The result shows that kiwi’s plantlets can better maintain their genes’ genetic stability for the first to the ninth generation. But from the tenth generation, the fitness values become variation. The results are based on experimentation, which uses optimized AFLP system for analyzing genetic diversity of 75 samples of seventh to eleventh 5 generations of kiwi.

PALMPRINTS: A COOPERATIVE CO-EVOLUTIONARY ALGORITHM FOR CLUSTERING HAND IMAGES

2005 ◽  
Vol 05 (03) ◽  
pp. 595-616 ◽  
Author(s):  
NAWWAF KHARMA ◽  
CHING Y. SUEN ◽  
PEI F. GUO
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Practical Problem ◽  
Research Effort ◽  
Fitness Function ◽  
Image Clustering ◽  
Number Of Clusters ◽  
Fitness Value ◽  
Evolutionary Genetic ◽  
Hand Image

The main objective of Project PalmPrints is to develop and demonstrate a special co-evolutionary genetic algorithm (GA) that optimizes (a clustering fitness function) with respect to three quantities, (a) the dimensions of the clustering space; (b) the number of clusters; and (c) and the locations of the various clusters. This genetic algorithm is applied to the specific practical problem of hand image clustering, with success. In addition to the above, this research effort makes the following contributions: (i) a CD database of (raw and processed) right-hand images; (ii) a number of novel features designed specifically for hand image classification; (iii) an extended fitness function, which is particularly suited to a dynamic (i.e. dimensionality varying) clustering space. Despite the complexity of the multi-optimizational task, the results of this study are clear. The GA succeeded in achieving a maximum fitness value of 99.1%; while reducing the number of dimensions (features) of the space by more than half (from 84 to 41).

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