Physical programming design optimization for High Speed Civil Transport

Messac, Achille; Hattis, Philip D.

doi:10.2514/3.46961

Cited by 35 publications

(7 citation statements)

References 4 publications

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“…With this simple process, without splines and mathematical properties, the OVO rule is guaranteed by the increasing curvature of the square function (Messac and Hattis 1996).…”

Section: The Modified Physical Programming (Mpp) Optimization Methodsmentioning

confidence: 99%

“…PP has been successfully applied to many different design problems including structural design of aircraft wings (Messac and Hattis 1996), propeller design (Chen et al 2000), robust feedback control (Messac and Wilson 1999), production planning (Messac et al 2002), and customer satisfaction in product design (Xin et al 2006). This range of applications demonstrates the usefulness of the PP method.…”

Section: An Introduction To Physical Programmingmentioning

confidence: 99%

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Multi-objective engineering design using preferences

Sanchis

Martínez

Blasco

2008

Engineering Optimization

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System design is a complex task when design parameters have to satisy a number of specifications and objectives which often conflict with those of others. This challenging problem is called multi-objective optimization (MOO). The most common approximation consists in optimizing a single cost index with a weighted sum of objectives. However, once weights are chosen the solution does not guarantee the best compromise among specifications, because there is an infinite number of solutions. A new approach can be stated, based on the designer's experience regarding the required specifications and the associated problems. This valuable information can be translated into preferences for design objectives, and will lead the search process to the best solution in terms of these preferences. This article presents a new method, which enumerates these a priori objective preferences. As a result, a single objective is built automatically and no weight selection need be performed. Problems occuring because of the multimodal nature of the generated single cost index are managed with genetic algorithms (GAs).

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“…With this simple process, without splines and mathematical properties, the OVO rule is guaranteed by the increasing curvature of the square function (Messac and Hattis 1996).…”

Section: The Modified Physical Programming (Mpp) Optimization Methodsmentioning

confidence: 99%

Section: An Introduction To Physical Programmingmentioning

confidence: 99%

Multi-objective engineering design using preferences

Sanchis

Martínez

Blasco

2008

Engineering Optimization

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“…In this case new weights need to be defined to resolve the problem. Messac and Hattis (1996) showed that weights must be a function of the original objectives, not constants, in order for a weighted sum to mimic a preference function accurately. Second, the weighted sums method is not able to capture solutions on the non-convex regions of the Pareto frontier.…”

Section: Pareto Set Generation Methodsmentioning

confidence: 99%

“…The physical programming method was initially introduced by Messac and Hattis (1996) to provide a means of incorporating preferences without the need to define relative weights (Chen et al (2000)). Objective functions, constraints and goals are treated equally and combined into design metrics.…”

Section: Pareto Set Generation Methodsmentioning

confidence: 99%

Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method

Munk

Kipouros

Vio

et al. 2017

Struct Multidisc Optim

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To date the design of structures using topology optimization methods has mainly focused on single-objective problems. Since real-world design problems typically involve several different objectives, most of which counteract each other, it is desirable to present the designer with a set of Pareto optimal solutions that capture the trade-off between these objectives, known as a smart Pareto set. Thus far only the weighted sums and global criterion methods have been incorporated into topology optimization problems. Such methods are unable to produce evenly distributed smart Pareto sets. However, recently the smart normal constraint method has been shown to be capable of directly generating smart Pareto sets. Therefore, in the present work, an updated smart Normal Constraint Method is combined with a Bi-directional Evolutionary Structural Optimization (SNC-BESO) algorithm to produce smart Pareto sets for multiobjective topology optimization problems. Two examples are presented, showing that the Pareto solutions found by the SNC-BESO method make up a smart Pareto set. The first example, taken from the literature, shows the benefits of the SNC-BESO method. The second example is an industrial design problem for a micro fluidic mixer. Thus, the problem is multi-physics as well as multiobjective, highlighting the applicability of such methods to real-world problems. The results indicate that the method is capable of producing smart Pareto sets to industrial problems in an effective and efficient manner.

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“…The maxi-min method can be used only when all criteria are comparable so that they can be measure on a common scale, which is a limitation. Messac and Hattis (1996) apply physical programming to the design of high-speed transport planes. Messac and Wilson (1998) apply physical programming to the design of a robust controller for a two degree-of-freedom spring-and-mass system.…”

Section: Review Of Multi-criteria Optimization Methods -Theory and Apmentioning

confidence: 99%

Review of Multi-criteria Optimization Methods – Theory and Applications

Odu¹,

Charles-Owaba²

2013

IOSRJEN

111

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-A review of multi-criteria optimization concepts and methods is presented. The techniques provide solutions to the problems involving conflicting and multiple objectives. Several methods based on weighted averages, priority setting, outranking and their combinations are employed. The application and procedure in respect to material selection is presented. It is observed from the literature that no single approach is superior, rather, the selection of a specific method depends on the type of information that is provided in the problem, the designer"s preferences, the solution requirements and the availability of software adopted.

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Physical programming design optimization for High Speed Civil Transport

Cited by 35 publications

References 4 publications

Multi-objective engineering design using preferences

Multi-objective engineering design using preferences

Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method

Review of Multi-criteria Optimization Methods – Theory and Applications

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