OPTIMAL GEOMETRICAL DESIGN OF AIRCRAFT USING GENETIC ALGORITHMS

2003 ◽  
Vol 26 (4) ◽  
pp. 373-388 ◽  
Author(s):  
Nicholas Ali ◽  
Kamran Behdinan
Keyword(s):  
Genetic Algorithm ◽  
Penalty Method ◽  
Aircraft Design ◽  
Cost Effective ◽  
Design Parameters ◽  
Adaptive Penalty ◽  
Search Tool ◽  
And Performance ◽  
Aircraft Sizing ◽  
Adaptive Penalty Method

With the advent of computers and search and optimization tools such as the genetic algorithm, the ability to manipulate numerous aircraft design parameters in a reasonable amount of time is feasible. From this angle, the lengthy time and effort spent creating and integrating aerodynamics codes, sizing routines, and performance modules, can be mitigated by the use of a genetic algorithm. Consequently, a genetic algorithm has been created and employed as a cost effective tool to explore possible aircraft geometries in the conceptual design process of the aircraft. A program has been developed to address most aspects of aircraft design such as aircraft sizing and configuration, performance, and propulsion, to name a few. These codes have been integrated into a genetic algorithm, which performs the task of searching and optimizing. The adaptive penalty method has been employed to handle all constraints imposed on the design. In addition, adjustments for varying degrees of selection and crossover intensities and types have been studied. A design study has also been carried out to compare an existing aircraft shape with the genetic algorithm optimized aircraft shape and configuration. Results indicate that the genetic algorithm is a powerful multi-disciplinary optimization and search tool, capable of simultaneously managing and varying numerous aircraft design parameters. Moreover, the genetic algorithm is capable of finding aircraft geometries and configurations that are both efficient and cost effective.

A New Adaptive Penalty Method for Constrained Genetic Algorithm and Its Application to Water Distribution Systems

2006 ◽  
Author(s):  
Berge Djebedjian ◽  
Ashraf Yaseen ◽  
Magdy Abou Rayan
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Penalty Method ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimization Problem ◽  
Water Distribution Systems ◽  
Building Blocks ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

This paper presents a new adaptive penalty method for genetic algorithms (GA). External penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. The success of the genetic algorithm application to the design of water distribution systems depends on the choice of the penalty function. The optimal design of water distribution systems is a constrained non-linear optimization problem. Constraints (for example, the minimum pressure requirements at the nodes) are generally handled within genetic algorithm optimization by introducing a penalty cost function. The optimal solution is found when the pressures at some nodes are close to the minimum required pressure. The goal of an adaptive penalty function is to change the value of the penalty draw-down coefficient during the search allowing exploration of infeasible regions to find optimal building blocks, while preserving the feasibility of the final solution. In this study, a new penalty coefficient strategy is assumed to increase with the total cost at each generation and inversely with the total number of nodes. The application of the computer program to case studies shows that it finds the least cost in a favorable number of function evaluations if not less than that in previous studies and it is computationally much faster when compared with other studies.

Differential evolution with the adaptive penalty method for structural multi-objective optimization

2018 ◽  
Vol 20 (1) ◽  
pp. 65-88 ◽  
Author(s):  
Dênis E. C. Vargas ◽  
Afonso C. C. Lemonge ◽  
Helio J. C. Barbosa ◽  
Heder S. Bernardino
Keyword(s):  
Differential Evolution ◽  
Penalty Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

Active Torsion Bar Body Roll Minimization System: Design and Testing

2003 ◽  
Author(s):  
David Cimba ◽  
Kyle Gilbert ◽  
John Wagner
Keyword(s):  
Cost Effective ◽  
Vehicle Body ◽  
Design Parameters ◽  
Test Environment ◽  
Component Design ◽  
Body Roll ◽  
And Performance ◽  
Emergency Scenarios ◽  
The Impact ◽  
Torsion Bar

Sport utility and light-duty commercial vehicles exhibit a higher propensity for rollover during aggressive driving maneuvers, emergency scenarios, and degraded environmental conditions. A variety of strategies have been proposed to reduce vehicle body roll including active suspensions, comprehensive yaw stability systems, and active torsion bars. The active torsion bar systems have recently gained popularity due to their cost effective design and adaptability to existing chassis systems. However, the development of new control algorithms, design of subsystem components, and the evaluation of parameter sensitivity via testing a full scale vehicle is not always practical due to cost and safety concerns. Thus, a modular simulation tool and bench top testing environment is required to facilitate design and performance studies. In this paper, a series of mathematical models will be introduced to describe the vehicle dynamics and the roll prevention system. Representative numerical results are discussed to investigate a vehicle’s transient response with and without an active torsion bar system, as well as the impact of torsion bar and hydraulic component design parameters. Finally, a hardware in-the-loop test environment will be presented.

scholarly journals Improved Immune Genetic Algorithm to Optimize the Design of Wing Structure of Competition Aircraft

2021 ◽  
Vol 2137 (1) ◽  
pp. 012075
Author(s):  
Xi Feng ◽  
Yafeng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Aircraft Design ◽  
Flight Test ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Immune Genetic Algorithm ◽  
Maximum Deformation ◽  
Design Variables ◽  
Wing Structure

Abstract An improved immune genetic algorithm is used to design and optimize the wing structure parameters of a competition aircraft. According to the requirements of aircraft design, multi-objective optimization index is established. On this basis, the basic steps of using immune algorithm to optimize the main design parameters of aircraft wing structure are proposed, and the optimization of the wing parameters of a competition aircraft is used as an example for simulation calculation. The design variables in the optimization are the size of the wing components, and the optimization goal is to minimize the weight of the wing and the maximum deformation of the wing structure. Research shows that compared with traditional optimization methods; the improved immune genetic algorithm is a very effective optimization method. At the same time, a prototype is made to check the validity and feasibility of the design. Flight test results show that the optimization method is very effective. Although the method is proposed for competition aircraft, it is also applicable to other types of aircraft.

scholarly journals Otimização estrutural de treliças considerando restrições de frequências naturais de vibração

2020 ◽  
Vol 18 (2) ◽  
pp. 14
Author(s):  
Afonso Celso de Castro Lemonge ◽  
Patrícia Habib Hallak ◽  
José Pedro Gonçalves Carvalho
Keyword(s):  
Particle Swarm Optimization ◽  
Penalty Method ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

Este artigo apresenta um Algoritmo Evolucionário (AE) baseado no comportamento de enxame de partículas (Particle Swarm Optimization - PSO) adaptado para a obtenção de soluções de problemas de otimização estrutural com restrições. O PSO é um algoritmo de fácil implementação e competitivo perante os demais algoritmos populacionais inspirados na natureza. Neste artigo, são analisados problemas de otimização estrutural de treliças submetidas a restrições de frequências naturais de vibração. Para o tratamento destas restrições, incorpora-se ao PSO uma técnica de penalização adaptativa (Adaptive Penalty Method - APM), que tem demonstrado robustez e eficiência quando aplicada no tratamento de problemas de otimização com restrições. O algoritmo proposto é validado através de experimentos computacionais em problemas de otimização estrutural amplamente discutidos na literatura.

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