Aerospace Design Optimization Using a Steady State Real-Coded Genetic Algorithm

Dyer, John; Hartfield, Roy J.; Dozier, Gerry; Burkhalter, John E.

doi:10.2514/6.2008-5921

Cited by 20 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It suggests that the GA has quickly found promising solutions while the AIS needed more time to converge to solutions of such quality. The fast convergence of the GA could be attributed to the implemented variant of GA (steady-state GA with elitism) that yields higher selection pressure on the account of diversity [14,6]. The AIS seems to be robust and prone to premature convergence as it becomes more successful with longer execution times.…”

Section: Discussionmentioning

confidence: 99%

Implementing Artificial Immune Systems for the Linear Ordering Problem

Krömer

Platoš

Snåšel

2013

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Abstract. Linear Ordering Problem (LOP) is a well know NP-hard combinatorial optimization problem attractive for its complexity, rich library of test data, and variety of real world applications. This study investigates the bio-inspired Artificial Immune Systems (AIS) as a pure metaheuristic soft computing solver of the LOP. The well known LOP library LOLIB was used to compare the results obtained by AIS and other pure soft computing metaheuristics.

show abstract

Section: Discussionmentioning

confidence: 99%

Implementing Artificial Immune Systems for the Linear Ordering Problem

Krömer

Platoš

Snåšel

2013

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

“…The Genetic Algorithm [17,18] is applied to solve the optimization problem obtained and listed in Table 1. The parameters values in Table 1 are used to size the Ignoring the spillage of external compression three-dimensional inlet, the inlet at design point should attain the maximum total pressure recovery.…”

Section: Optimization Of the Inlet Sectionmentioning

confidence: 99%

A Study on Blended Inlet Body Design for a High Supersonic Unmanned Aerial Vehicle

You¹,

Yu²,

Li³

2016

International Journal of Aeronautical and Space Sciences

View full text Add to dashboard Cite

The design process of blended inlet body (BIB) for the preliminary design of a near-space high supersonic unmanned aerial vehicle (HSUAV) is presented. The mass flow rate and cowl area of inlet at a design point are obtained according to the cruise condition of the HSUAV. A mixed-compression axisymmetric supersonic inlet section with a fixed geometry reasonably matching the high supersonic cruise state is created by using the inviscid theory of aerodynamics. The inlet section is optimized and used as a baseline section for the BIB design. Three BIB concepts for the HSUAV are proposed, and their internal aerodynamic characteristics of inlet are evaluated using Euler computational fluid dynamics (Euler CFD) solver. The preferred concept is identified, in which the straight leading edge of the baseline HSUAV configuration is modified into the convex leading edge to accommodate the inlet and meet the requirements of the cowl area to capture the sufficient air flow. The total recovery of inlet for the preferred BIB concept and the aerodynamic characteristics of the modified HSUAV configuration are verified using Navier-Stokes computational fluid dynamics (NS CFD) solver. The validation indicates that the preferred BIB concept can meet both the requirements of the inlet and aerodynamic performance of the HSUAV.

show abstract

“…Owing to the powerful capability of solving real-world optimization problems, the real-coded genetic algorithm (RCGA) is one of the most effective and commonly used evolutionary algorithm (EA), and many successful applications using RCGAs in diversified fields have been reported recently [1][2][3][4][5]. By mimicking the biological world of natural selection and survival of the fittest, the RCGAs are essentially a kind of population-based stochastic search schemes that implement the selection, crossover, and mutation operators in a series framework.…”

Section: Introductionmentioning

confidence: 99%