Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2006 ◽  
Author(s):  
Jun Liu ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Computer Experiments ◽  
Design Problems ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Complex Design ◽  
The Impact

The use of metamodels in simulation-based robust design introduces a new source of uncertainty that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty. With the randomness present in noise and/or design variables that propagates through the metamodel, the effects of model interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on robust design objective. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. Even though our proposed methodology is illustrated with a simple container design and an automotive engine piston design example here, the developed analytical approach is the most useful when applied to high-dimensional complex design problems in a similar manner.

scholarly journals Evaluating Clustering Algorithms for Identifying Design Subproblems

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Jeffrey W. Herrmann ◽  
Michael Morency ◽  
Azrah Anparasan ◽  
Erica L. Gralla
Keyword(s):  
Association Rule ◽  
Euclidean Distance ◽  
Clustering Algorithms ◽  
Design Teams ◽  
Clustering Methods ◽  
Design Problems ◽  
Design Variables ◽  
Complex Design ◽  
Synthetic Datasets ◽  
Better Than

Understanding how humans decompose design problems will yield insights that can be applied to develop better support for human designers. However, there are few established methods for identifying the decompositions that human designers use. This paper discusses a method for identifying subproblems by analyzing when design variables were discussed concurrently by human designers. Four clustering techniques for grouping design variables were tested on a range of synthetic datasets designed to resemble data collected from design teams, and the accuracy of the clusters created by each algorithm was evaluated. A spectral clustering method was accurate for most problems and generally performed better than hierarchical (with Euclidean distance metric), Markov, or association rule clustering methods. The method's success should enable researchers to gain new insights into how human designers decompose complex design problems.

Preferences and Correlated Uncertainties in Engineering Design

2003 ◽  
Author(s):  
Tomonori Honda ◽  
Erik K. Antonsson
Keyword(s):  
Engineering Design ◽  
Design Variable ◽  
Design Space ◽  
Design Method ◽  
Design Problems ◽  
Performance Variables ◽  
Design Variables ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
And Performance

The Method of Imprecision (MOI) is a multi-objective design method that maximizes the overall degree of both design and performance preferences. Sets of design variables are iteratively selected, and the corresponding performances are approximately computed. The designer’s judgment (expressed as preferences) are combined (aggregated) with the customer’s preferences, to determine the overall preference for sets of points in the design space. In addition to degrees of preference for values of the design and performance variables, engineering design problems also typically include uncertainties caused by uncontrolled variations, for example, measuring and fabrication limitations. This paper illustrates the computation of expected preference for cases where the uncertainties are uncorrelated, and also where the uncertainties are correlated. The result is a “best” set of design variable values for engineering problems, where the overall aggregated preference is maximized. As is illustrated by the examples shown here, where both preferences and uncontrolled variations are present, the presence of uncertainties can have an important effect on the choice of the overall best set of design variable values.

A preliminary study on the integration of engineering and aesthetics measures via the design of vehicle silhouettes

2014 ◽  
Vol 229 (12) ◽  
pp. 2221-2230 ◽  
Author(s):  
Kuei-Yuan Chan ◽  
Shen-Cheng Chang
Keyword(s):  
Optimal Design ◽  
Level Structure ◽  
Point Of View ◽  
Design Problems ◽  
Physical Constraints ◽  
Engineering Discipline ◽  
Design Variables ◽  
Complex Design ◽  
Target Cascading ◽  
The Aesthetic

The success of a consumer product is the result of not only engineering specifications but also emotional effects. Therefore, product design must be multidisciplinary as well as transdisciplinary across both natural and social science. In this work, we investigate the optimal design of vehicle silhouettes considering various aesthetic and engineering measures. The entire design problem is modeled as a bi-level structure with the top level being the aesthetic subproblem and the lower level consists of subproblems in the engineering discipline. This multi-level system provides a feasible approach in solving complex design problems; it also resembles the interactions of different departments in the auto industry. The aesthetic subproblem uses 11 proportionality measures and curvature to quantify a vehicle silhouette. The engineering discipline includes safety, handling, and aerodynamics of a vehicle with physical constraints on vehicle geometry. The design variables are the locations of 15 nodal points in describing the silhouette of a vehicle. The linking variables between subsystems are body and chassis dimensions that must be consistent for a design to be feasible. The optimal design of this hierarchical problem is obtained using the analytical target cascading from the literature. Results show that the original prohibitively expensive all-in-one problem becomes solvable if systems of smaller subproblems are created. Adding emotional measures in engineering design is invaluable and will reveal the true merits of a product from consumers’ point of view. Although such metrics are generally opaque, this research demonstrates the impacts of these measures once they become available.

Navigating Expensive and Complex Design Spaces Using Genetic Algorithms

1999 ◽  
Author(s):  
David C. Zimmerman
Keyword(s):  
Function Approximation ◽  
Design Space ◽  
Optimization Technique ◽  
Computational Cost ◽  
Approximation Procedure ◽  
Network Approach ◽  
Neural Network Approach ◽  
Design Problems ◽  
Complex Design ◽  
Single Function

Abstract The overall objective of this study is to formulate and study a generic procedure for navigating expensive and complex design spaces. The term generic is meant to imply that the procedure would be equally valid in exploring design problems in a multitude of fields. The term expensive design space implies that the computational cost, or burden, associated with a single function is considered “large”. What is desired is a methodology which can identify “promising regions” of the design space using as few function evaluations as possible. To approach this problem, a neural network approach is developed to serve as an inexpensive and generic function approximation procedure. The genetic algorithm was selected as the optimization technique based on its ability to search multi-modal, discontinuous, mixed parameter, and noisy design spaces.

Constraint Dominance Determination Methods

2003 ◽  
Author(s):  
James R. Rinderle ◽  
Ashish D. Deshpande
Keyword(s):  
Design Space ◽  
Transformation Method ◽  
Design Problems ◽  
Interval Method ◽  
Identification Methods ◽  
Complex Design ◽  
Range Determination ◽  
Determination Methods ◽  
Function Range ◽  
Critical Aspects

Dominance among constraints exists when the satisfaction of a constraint guarantees the satisfaction of another, rendering the second constraint irrelevant. Identifying dominance not only facilitates numerical solution but may also focus the designer’s attention on critical aspects of the design. A number of dominance identification methods have been described in the literature, including the Constraint Difference Method, the Constraint Transformation Method, and the Necessary-Sufficient Interval Method. We elaborate on the basis for and the character of these methods and we discuss relative similarities, differences, strengths, and weaknesses of the methods. We also discuss computational issues relevant to the application of these methods, most specifically function range determination and interval analysis issues. We observe that the differences among the methods lead to advantages for each method in circumstances that depend on the nature of the constraints and the extent of the design space. These distinct advantages suggest a synergism among the methods in the identification of constraint dominance in complex design problems.

A new multidisciplinary robust design optimization framework for an autonomous underwater vehicle in system and tactic design

2018 ◽  
Vol 233 (3) ◽  
pp. 918-936
Author(s):  
Mohsen Bidoki ◽  
Mehdi Mortazavi ◽  
Mehdi Sabzehparvar
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Robust Design ◽  
Multidisciplinary Design Optimization ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Robust Design Optimization ◽  
Multidisciplinary Design ◽  
Optimization Framework ◽  
Design Variables

The design process of an autonomous underwater vehicle requires mathematical model of subsystems or disciplines such as guidance and control, payload, hydrodynamic, propulsion, structure, trajectory and performance and their interactions. In early phases of design, an autonomous underwater vehicle is often encountered with a high degree of uncertainty in the design variables and parameters of system. These uncertainties present challenges to the design process and have a direct effect on the autonomous underwater vehicle performance. Multidisciplinary design optimization is an approach to find both optimum and feasible design, and robust design is an approach to make the system performance insensitive to variations of design variables and parameters. It is significant to integrate the robust design and the multidisciplinary design optimization for designing complex engineering systems in optimal, feasible and robust senses. In this article, we present an improved multidisciplinary design optimization methodology for conceptual design of an autonomous underwater vehicle in both engineering and tactic aspects under uncertainty. In this methodology, uncertain multidisciplinary feasible is introduced as uncertain multidisciplinary design optimization framework. The results of this research illustrate that the new proposed robust multidisciplinary design optimization framework can carefully set a robust design for an autonomous underwater vehicle with coupled uncertain disciplines.

An Interval-Based Focalization Method for Decision-Making in Decentralized, Multi-Functional Design

2005 ◽  
Author(s):  
Jitesh H. Panchal ◽  
Marco Gero Ferna´ndez ◽  
Janet K. Allen ◽  
Christiaan J. J. Paredis ◽  
Farrokh Mistree
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Cooperative Game ◽  
Design Space ◽  
Cooperative Game Theory ◽  
Functional Design ◽  
Space Partitioning ◽  
Design Problems ◽  
Design Variables ◽  
Conflicting Objectives

Multi-functional design problems are characterized by strong coupling between design variables that are controlled by stakeholders from different disciplines. This coupling necessitates efficient modeling of interactions between multiple designers who want to achieve conflicting objectives but share control over design variables. Various game-theoretic protocols such as cooperative, non-cooperative, and leader/follower have been used to model interactions between designers. Non-cooperative game theory protocols are of particular interest for modeling cooperation in multi-functional design problems. These are the focus of this paper because they more closely reflect the level of information exchange possible in a distributed environment. Two strategies for solving such non-cooperative game theory problems are: a) passing Rational Reaction Sets (RRS) among designers and combining these to find points of intersection and b) exchanging single points in the design space iteratively until the solution converges to a single point. While the first strategy is computationally expensive because it requires each designer to consider all possible outcomes of decisions made by other designers, the second strategy may result in divergence of the solution. In order to overcome these problems, we present an interval-based focalization method for executing decentralized decision-making problems that are common in multi-functional design scenarios. The method involves propagating ranges of design variables and systematically eliminating infeasible portions of the shared design space. This stands in marked contrast to the successive consideration of single points, as emphasized in current multifunctional design methods. The key advantages of the proposed method are: a) targeted reduction of design freedom and b) non-divergence of solutions. The method is illustrated using two sample scenarios — solution of a decision problem with quadratic objectives and the design of multi-functional Linear Cellular Alloys (LCAs). Implications include use of the method to guide design space partitioning and control assignment.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

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