Conceptual design of helicopters via genetic algorithm

Crossley, William A.; Laananen, David H.

doi:10.2514/3.47058

Cited by 39 publications

(9 citation statements)

References 8 publications

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“…Although simple, this approach requires extra data record and additional steps in the genetic engine to properly link together a set of individuals This occurrence becomes unfeasible when the optimization code must be connected with external programs such as FEM (Finite Elements Method) or CFD (Computational Fluid Dynamic) calculus engines, due to some troubles that can occur at asynchronous data interface level. With a bit-masking data structure it is possible to de®ne an analogous operator, but based on a single-step operation and directly working on genotypic structure, in a similar way than standard bitwise G-improvement [9]. This one is a technique which performs several systematic cyclic changes in the binary structure (bit-shifting, inversion) and retails any individual that reaches a ®tness improvement.…”

Section: A Single-step Bit-masking Based Prediction Operatormentioning

confidence: 98%

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

Iuspa¹,

Scaramuzzino²

2001

Soft Computing

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In the present paper a special bit-masking oriented data structure for an improved implementation of crossover and mutation operators in genetic algorithms is shown. The developed data structure performs evolutionary operators in two separate steps: crossover and mutation mask ®ll and a special boolean based function application. Both phases are optimized to reach a more ef®cient, fast and¯exible genetic reproduction than standard implementations. The method has been powered adding a multi-layered, bit-masking oriented data structure and a boolean operation based control mixer, allowing special blended crossover operators obtained by superposition of the standard ones. Several examples of crossover schemes produced by these extended controls are presented. In addition, a special purpose crossover scheme, capable to process at the same time two distinct groups of design variables with separate crossover schemes is shown, in order to improve ef®ciency and convergence speed of some discrete/continuous optimization problems. Finally, to highlight further capabilities of the bit-masking approach, a special single-step version of an evolutionary direction operator is also illustrated. IntroductionThe data structures usually employed in binary code based Genetic Algorithms (bGAs) can be essentially subdivided into two typical groups: explicit or implicit binary formulation [1]. First type is obtained using a simple boolean matrix structure (or an equivalent data type) for the population genetic code and a decoding function to properly represent chromosomal, design variable oriented information, packed into an equivalent discrete domain of representation [2] (in this work is de®ned chromosome a genetic structure related to the current design variable, typically a sub-string in explicit binary formulation and an integer decimal value in the implicit one, while genes are binary digits which constitute it, without further distinction between position [locus] and contents [allele]). The main advantage of explicit binary representation is à`n atural'' transposition of the abstract genoma model used for genetic simulations as to the binary data type, with a consequent simpli®cation of genetic operators implementation. The crossover and mutation operators can be easily de®ned by programming a set of operations which, according to a selected scheme (i.e. cut-crossover, mutation), directly works on the boolean array structure, allowing a simple parent-to-child string bit transfer. However, this approach requires additional steps to extract and decode design variables related sub-strings, causing a considerable computer time consuming activity. On the other hand, an implicit binary codi®cation based data structure is simply formed by an integer matrix. This explicit data object is related to a chromosomal level abstraction data, therefore no decoding operations are required to obtain integer design variables values, but a speci®c function, allowing bit weighted integer modi®ca-tions, must be de®ned and applied to emulate genetic i...

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Section: A Single-step Bit-masking Based Prediction Operatormentioning

confidence: 98%

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

Iuspa¹,

Scaramuzzino²

2001

Soft Computing

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“…The range of application encompassed by these examples demonstrates the broad capability of a genetic algorithm for problem solving and design optimization. Specific applications involving shape [8] and topology [9] also exist which indicates that this class of algorithms can handle issues that are fundamental to aesthetic design, although few if any of these applications actually address such concerns.…”

Section: Review Of Related Workmentioning

confidence: 99%

An Interactive Evolutionary Environment for Creative Design

Alobaidi¹,

Sandgren²

2021

MME

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A creative and/or innovative computer aided design environment is developed around the concept of an evolutionary optimization algorithm. Designs are generated within a set of prescribed design frameworks using a problem specific encoding and modified through operations including recombination, crossover and mutation. Evaluation of all candidate designs is performed by a user through a graphical user interface. A set of problems involving the creation of graphic images is presented. The examples include the generation of a set of two dimensional polygonal shapes, fractal images, path generation from a multi-degree freedom mechanical planar linkage and a mathematically prescribed pattern generation from a graphic design application utilized in quilting. Post design evaluation of the user input to the process provides insight into the individuals design strategy as well as determination of common user attributes in the creative design process.

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“…The focus was on conceptual design in Refs. [95]- [98], in which the objective was power and weight minimization subject to a variety of performance and mission constraints. Two integer, two discrete, and five continuous design variables resulted in a 28-bit design string.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Recent Applications of Design Optimization to Rotorcraft-A Survey

Celi

1999

Journal of Aircraft

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This paper presents a survey of applications of design optimization to helicopter problems carried out in the last decade. Helicopter optimization has not yet reached the same maturity as structural optimization, and some potential reasons are discussed first. Next, published optimization studies are reviewed, divided into sensitivity analysis studies, applications to rotor dynamics and aeroelasticity, applications to other helicopter problems, emerging technologies such as genetic algorithms and simulated annealing, and studies with experimental verification. The usefulness of design optimization for helicopter applications will increase with the availability of improved analyses, more efficient algorithms especially for sensitivity calculations, and further experimental verifications. The formulation of representative optimization test problem, including experimental verification, is also recommended.

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Conceptual design of helicopters via genetic algorithm

Cited by 39 publications

References 8 publications

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

An Interactive Evolutionary Environment for Creative Design

Recent Applications of Design Optimization to Rotorcraft-A Survey

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