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1550 results in Control systems and optimization

Page 42 of 78

  • 10 - Practicing Optimization—Larger Examples

  • from PART III - USING OPTIMIZATION—PRACTICAL ESSENTIALS
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 223-248

  • Summary

    Overview

    This chapter covers the practice of optimization in diverse technical areas. What has been learned thus far is the practical knowledge that is needed to apply optimization to different types of designs or systems. We will explore optimizing systems/designs in the following disciplines: chemistry, mechanics, aerospace, automotive, mathematics (data fitting), nuclear, electrical, portfolio management, and business (Ref. [1]). Our ability to optimize these various designs provides us with the skills to apply optimization beyond the confines of this book or of the class you might be taking, and successfully venture into the real world!

    Mechanical Engineering Example

    Structural Example

    Figure 10.1 represents a ten-bar truss. The members are connected to each other at six nodes, numbered 1 to 6. The truss is assumed to be planar, and every node has two degrees of freedom (i.e., it is free to move both in the x and y directions). The left edge of the truss is fixed to the wall. The displacements u1 to u8 are specified at the specific non-fixed nodes. F1 to F8 represent loads applied to these nodes. The variables by x1 to x10 denote the cross sectional areas of the truss members. The Young's modulus of the material is E = 1 × 106N/mm2. The maximum allowable stress in each bar is σult = ±100N/mm2 (i.e., tension/compression), and the maximum allowable deflection is δmax = ±2 mm. We note that the unit of length in the figure and in this problem is mm, and that of force is N. While the current example is commonly used in the literature, the reader is also encouraged to review the topology optimization survey by Rozvany in Ref. [2].

    Imagine you are hired as the optimization expert for the ten-bar truss project. The structural engineer working on the project provides you with a program blackbox−10bar.m, which allows you to enter the loads at the nodes and the area of each member, and obtain the corresponding nodal displacements and stresses in the members. Figure 10.2 illustrates this task. The ten-bar truss is subjected to 10 different loading conditions given in Table 10.1.

  • PART III - USING OPTIMIZATION—PRACTICAL ESSENTIALS

  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 121-122

  • Summary

    In Part I, we presented some prerequisite material for our study of optimization: and introduction to Matlab, and some elementary mathematics. In Part II, we learned what we needed to explore what optimization is all about, and the important role it can play in your life as and engineer, scientist, business person, or anyone dealing with improving things quantitatively (e.g., performance and cost). In Part III, we get into the thick of things! We learn what we need to know to credibly address optimization problems in practice.

    Specifically, the topics presented, with the chapter numbers, are given below:

    6. Multiobjective Optimization

    7. Numerical Essentials

    8. Global Optimization Basics

    9. Discrete Optimization Basics

    10. Practicing Optimization—Larger Examples

  • 15 - Modeling Complex Systems: Surrogate Modeling and Design Space Reduction

  • from PART V - MORE ADVANCED TOPICS IN OPTIMIZATION
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 355-375

  • Summary

    Overview

    In this chapter, we introduce efficient mathematical approaches to model the behavior of complex systems in the context of optimizing the system. Specifically, complex systems often involve a large number of design parameters (to be tuned), and the optimization of these systems often demand large scale computational simulations or experiments to quantify the system behavior. The resulting high dimensionality of the design space, the prohibitive computation time (or expense), or the lack of mathematical models present important challenges to the quantitative optimization of these complex systems. This chapter introduces traditional and contemporary approaches, such as design variable linking and surrogate modeling, to address the modeling challenges encountered in solving complex optimization problems.

    Section 15.2 discusses the generic challenges in complex optimization problems. The impact of problem dimension is addressed in Sec. 15.3, where design variable linking and design of experiments are introduced. Section 15.4 presents surrogate modeling, where the discussion includes: the process, the polynomial response surface methodology, the radial basis function method, the Kriging method, and the artificial neural network method. The chapter concludes with a summary provided in Section. 15.5.

    Modeling Challenges in Complex Optimization Problems

    Modeling is one of the primary activities in optimization. Leveraging computational tools and models, as opposed to purely depending on experiments, can be considered the way forward in the area of system optimization. The design of complex systems, such as aircrafts, cars, and smart-grid networks, are increasingly performed using simulation-based design and analysis tools such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD). The incorporation of these tools has dramatically transformed modern engineering design and optimization approaches.

    These changes, however, are not free of challenges. For example, modern aerospace systems present significantly complex design requirements [1], where design objectives generally involve improving performance, reducing costs, and maximizing safety. Another example is the optimization of a vehicle structure to absorb crash energy, maintain adequate passenger space, and control crash deceleration pulse during collisions [2].

  • 16 - Design Optimization Under Uncertainty

  • from PART V - MORE ADVANCED TOPICS IN OPTIMIZATION
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 376-405

  • Summary

    Overview

    The previous chapters presented deterministic optimization methods. These do not implicitly take into account the inherent uncertainties typically present in the design process, in the system being designed, or in the models describing the system behavior. Uncertainties emanates from myriad sources. These include imperfect manufacturing processes and material properties, fluctuating loading conditions, over-simplified engineering models, or uncertain operating environment. All of these may have a direct impact on the system performance in its use or in the market place. To obtain a reliable and robust design (these terms are defined later), these uncertainties must be considered as part of the design process.

    In the past, empirical safety factors were often used to guard against engineering design failure [1]. Safety factors resulted in overly conservative designs, increasing the probability that businesses may lose their competitive edge in terms of cost and performance. More recently, design optimization methods under uncertainty have gained broad recognition. These approaches explicitly consider uncertainties of various forms, and search for designs that are insensitive to variations or uncertainty to a significant extent.

    This chapter introduces the concept of design optimization under uncertainty, and discusses the pertinent popular approaches available. Section 16.2 defines a generic example that is used throughout the book to facilitate the presentation of the material. The next Section (Sec. 16.3) defines five generic components/STEPS involved in design under uncertainty. This is followed by the presentation of these STEPS in the following five Sections: (1) uncertainty types identification (Sec. 16.4), (2) uncertainty quantification (Sec. 16.5), (3) uncertainty propagation (Sec. 16.6), (4) formulation of optimization under uncertainty (Sec. 16.7), and (5) results analysis (Sec. 16.8). Section 16.9 briefly discussed other popular methods. A chapter summary is provided in Sec 16.10.

    Chapter Example

    This section defines a generic example that it used throughout the chapter to illustrate the various concepts involved in design under uncertainty. Consider the optimization of a two bar truss shown in Fig. 16.1.

  • PART V - MORE ADVANCED TOPICS IN OPTIMIZATION

  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 333-334

  • Summary

    This part introduces carefully chosen advanced topics that are appropriate for graduate students or undergraduate students who might pursue study in the design optimization field. These topics will generally be of great interest to industry practitioners as well. While these topics are advanced, a mathematically advanced presentation is not provided. Instead, an introductory presentation is provided, with an eye toward practical usefulness.

    Specifically, the topics presented, with the chapter numbers, are given below:

    14. Discrete Optimization

    15. Modeling Complex Systems: Surrogate Modeling and Design Space Reduction

    16. Design Optimization Under Uncertainty

    17. Methods for Pareto Frontier Generation/Representation

    18. Physical Programming for Multiobjective Optimization

    19. Evolutionary Algorithms

  • 11 - Linear Programming

  • from PART IV - GOING DEEPER: INSIDE THE CODES AND THEORETICAL ASPECTS
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 251-278

  • Summary

    Overview

    Linear programming (LP) is a technique for optimizing a linear objective function, subject to linear equality and linear inequality constraints. Linear programming is an important field of optimization for several reasons (Refs. [1, 2]). Many practical problems in engineering and operations research can be expressed as linear programming problems. Linear programming problems can be solved in an easy, fast, and reliable manner.

    Linear programming was first used in the field of economics, and still remains popular in the areas of management, economics, finance, and engineering. During World War II, George Dantzig of the U.S. Air Force used LP techniques for planning problems. He invented the Simplex method, which is one of the most popular methods for solving LP problems.

    Linear programming is a well developed subject in operations research, and several references that focus on linear programming alone are available [3, 4, 5, 6]. The reader is encouraged to consult these references for a more detailed discussion of linear programming.

    In this chapter, the basics of linear programming problems will be studied. First, an introduction of the basic terminology and important concepts in LP problems will be presented in Sec. 11.2. In Sec. 11.3, the graphical approach for solving LP problems will be discussed, and four types of possible solutions for an LP problem will be introduced. Details on the Matlab linear programming solver are presented in Sec. 11.4. Sections 11.5 and 11.6 discuss the various concepts involved in the Simplex method. In Sec. 11.7, the notion of duality and interior point methods are briefly discussed. Section 11.8 concludes the chapter with a summary.

    Basics of Linear Programming

    In this section, some basic terminology and definitions in linear programming will be discussed.

    A generic linear programming problem consisting of linear equality and linear inequality constraints is given as where z is the linear objective function to be minimized;

  • 3 - Welcome to the Fascinating World of Optimization

  • from PART II - USING OPTIMIZATION–THE ROAD MAP
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 73-81

  • Summary

    Overview

    Welcome to the fascinating world of optimization. Indeed, you will find optimization to be a powerful addition to your education or to your toolkit in the workplace. This brief initial chapter provides you with a clear perspective as to how the book will play a key role in your understanding of optimization. As discussed in the Preface, this book takes a squarely practical perspective. As such, your learning will involve practicing optimization using many problems. By the end of your study of this book, you should expect to be able to work with others to optimize practically any design or system and improve its performance. If the design you would like to optimize is in your technical area of expertise, you might be able to do all of the work yourself. If the design or system you would like to optimize involves modeling issues with which you are not familiar, you will simply need to collaborate with someone (or a team) who is able to provide you with the computational performance models. In this latter case, as it often is in practice, you will provide your knowledge of optimization, and someone else may model the system. In other words, one person might do the structural analysis, another might do the financial analysis, and you might use your knowledge of this book to optimize the combined system to make it competitive in the market place.

    What Is Optimization? What Is Its Relation to Analysis and Design?

    What is optimization? Optimization can be defined in different ways. Some define it as “the art of making things the best.” Interestingly, many people do not like that definition as it may not be reasonable, or even possible, to do something in the very best possible way. In practice, doing something as well as possible within practical constraints is very desirable. Designing a product and doing all we can to increase profit as much as is practically possible is also very desirable.

  • 7 - Numerical Essentials

  • from PART III - USING OPTIMIZATION—PRACTICAL ESSENTIALS
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 158-199

  • Summary

    Overview

    In this chapter, you will be exposed to perhaps some of the most important issues in optimization. Interestingly, most optimization books and courses leave it up to the students to learn many of these issues simply by chance, leaving a broad set of the important practical topics unaddressed. We are referring here to numerical conditioning issues which strongly control the success of computational optimization.

    We will not engage in highly mathematical and theoretical issues of numerical optimization. Instead, we will learn about the tangible and practical issues that directly affect our success in formulating and solving optimization problems. Fortunately, for most of this chapter, all we need is the knowledge of first-year college mathematics.

    Keeping these issues in mind will often make the difference between success and failure. That is: (i) easily applying optimization successfully, or (ii) experiencing great frustration in trying to obtain an adequate solution unsuccessfully. In addition, we will learn how to control the resulting accuracy of our results, a critical issue in practice. The topics explored here include: numerical conditioning, scaling, finite differences, automatic differentiation, termination criteria, and sensitivities of optimal solutions, as well as examples that illustrate how to handle these issues in practice (Refs. [1, 2, 3].

    Numerical Conditioning—Algorithms, Matrices and Optimization Problems

    We begin by asking the following questions: What is numerical conditioning? And how does it relate to optimization? As we have learned, optimization depends on the numerical evaluation of the performance of the system being optimized. This performance evaluation typically involves coding, simulation, or software-based mathematical analysis, often also involving matrix manipulations (e.g., involving Hessians). For example, linear programming involves extensive matrix manipulations. Therefore, understanding the numerical properties of the matrices and of the algorithms used in optimization codes is important. From a practical point of view, we can think of a numerically well-conditioned problem or matrix as one that lends itself to easy numerical computation. Conversely, we can think of a numerically ill-conditioned problem or matrix as one that lends itself to difficult numerical computation.

  • 4 - Analysis, Design, Optimization and Modeling

  • from PART II - USING OPTIMIZATION–THE ROAD MAP
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 82-92

  • Summary

    Overview

    In this chapter, we introduce the important activities of analysis, design, optimization, and modeling. While we are all generally familiar with these terms, it is important to understand them in the context of how they relate to our optimization activities. We also develop an important understanding of how modeling system behavior is a distinct activity from modeling an optimization problem. These involve two distinct lines of expertise. In most of your courses (e.g., structures, dynamics and finance) you focused on the former. In this book, we focus on the latter.

    Analysis, Design and Optimization

    Analysis and optimization are two activities integral to the process of design (Refs. [1, 2, 3, 4, 5]). In this chapter, as well as in the remainder of the book, we will primarily focus on these two activities in the context of engineering or systems design. However, the mathematical concepts, approaches, algorithms, and software tools that you will learn in this book could be applicable to diverse fields beyond engineering (e.g., optimization of market portfolios) [6]. Although, from a technical perspective, analysis and optimization can be considered to be steps within the process of design, they can also be performed as stand-alone activities toward other end goals within the scope of academic research and industrial R&D. In this section, you will learn the definition of these activities and how they are related to each other. At the same time, you will have the opportunity to understand and appreciate the roles and responsibilities of the users, researchers, or engineers who execute these activities, whether individually or as a part of a team.

    In order to help you better understand the practical essence of these activities, the following simple design example will be used throughout this section. You are designing a table that can carry as much weight (of objects placed on it) as possible, while fitting within a particular corner of your room. You are allowed a limited budget to construct the table.

  • 14 - Discrete Optimization

  • from PART V - MORE ADVANCED TOPICS IN OPTIMIZATION
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 335-354

  • Summary

    Overview

    In most previous chapters, continuous optimization problems were considered where the design variables were assumed to be continuous; that is, design variables assume real values within given ranges. In many practical engineering problems, the acceptable values of the design variables do not form a continuous set. These problems are referred to as discrete optimization problems. For example, the number of rivets required in a riveted joint has to be an integer (such as 1, 2, 3). Another example is when the feasible region of the design variable is a set of given discrete numbers, such as {6.25, 6.95, 7.65}, which may be the available standardized sizes of nuts. The basics of discrete optimization were introduced in Chapter 9, where some pertinent elementary methods were presented. This chapter introduces more advanced approaches. The reader is advised to first review Chapter 9 as preparation for the current chapter.

    This chapter is organized as follows. The next section (Sec. 14.2) provides the problem classes, examples, and definition (along with the notion of computational complexity of the solution algorithms). Section 14.3 discusses the basics of some popular techniques used to solve integer programming problems, with examples. The methods studied will be: the exhaustive search method (Sec. 14.3.1), the graphical method (Sec. 14.3.2), the relaxation method (Sec. 14.3.3), the branch and bound method (Sec. 14.3.4), and the cutting plane method (Sec. 14.3.5). Popular current software options (Sec. 14.3.7) are also discussed. The chapter concludes with a summary in Sec. 14.4.

    Problem Classes, Examples and Definition

    This section presents discrete optimization problem classes, problem examples, and problem definition. Computational complexity of the solution algorithms is also briefly addressed in connection with the discrete optimization problem definition.

  • 19 - Evolutionary Algorithms

  • from PART V - MORE ADVANCED TOPICS IN OPTIMIZATION
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 445-460

  • Summary

    Overview

    This book has presented various algorithms and applications where the optimizer was primarily gradient-based (i.e., the search direction is governed by gradient and/or Hessian information). This chapter introduces an entirely different class of optimization algorithms called the evolutionary algorithms (EA). Evolutionary algorithms imitate natural selection processes to develop powerful computational algorithms to select optimal solutions. Genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), particle swarm optimization (PSO), and tabu search (TS) are some of the popular techniques that fall under the umbrella of evolutionary algorithms.

    The motivation for using biologically-inspired computational approaches stems from two key observation. First, the mathematical optimization algorithms in solving complex problems in engineering, computing, and other fields suffer strong limitations. The common challenges in these areas revolve around the lack of mathematical models that define the physical phenomena, discontinuous functions, and high nonlinearity. Second, many complex problems encountered in engineering already exist in nature in some relevant form. Optimization is inherent in nature, such as in the process of adaptation performed by biological organisms in order to survive. Engineers and scientists continue to explore the various efficient problem-solving techniques employed by nature to optimize natural systems.

    The relative advantages and limitations of EAs vs. traditional optimization methods are as follows:

  • Traditional algorithms typically generate a single candidate optimum at each iteration that progresses toward the optimal solution. Evolutionary algorithms generate a population of points at each iteration. The best point in the population approaches an optimal solution.

  • Traditional algorithms calculate the candidate optimal point at the next iteration by a deterministic computation. EAs usually select the next population by a combination of operations that use random number generators.

  • Traditional algorithms require gradient and/or Hessian information to proceed, while EAs usually require only function values. As a result, EAs can solve a variety of optimization problems in which the objective function is not smooth and potentially discontinuous.[…]

  • 6 - Multiobjective Optimization

  • from PART III - USING OPTIMIZATION—PRACTICAL ESSENTIALS
  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp 123-157

  • Summary

    Overview

    In Part I of this book, we reviewed preparatory knowledge needed to start learning optimization: Matlab, and elementary mathematics. In Part II, we were exposed to the world of optimization and its potentially powerful role in our lives as engineers or professionals in quantitative fields. We then delved into the specific activities of analysis, design, and optimization, their links and distinctions. In the previous chapter, we then began addressing the fundamental aspects of computational optimization. These included single objective optimization, and the different approaches to optimization (analytical, numerical, experimental, and graphical). This was followed by a discussion of software options.

    In this chapter, we study one of the most important aspects of optimization in practice, the notion of multiobjective optimization. Stated simply, Multiobjective optimization is the art and science of formulating how to optimize a set of competing objectives, which is almost always the case in practice. A detailed presentation of the pertinent methods is provided in such a way as to allow you to be readily productive and effective in practical design.

    The Multiobjective Problem Definition

    The identification of the right design objectives plays a crucial role in the design of any system. More often than not, in real-life design, you will find that your optimization problem contains more than one design objective. For example, wouldn't it be nice if your car dealer would tell you that the car you like happens to feature more miles per gallon and also costs less than a competitor's car? As an engineer, you would think twice before making a decision based on his interesting comments. If he were right, the other car company would probably not stay in business for very long. The point here is that while designing any product or system, you will almost always have to consider several competing design objectives. As a car designer, you would like the car to provide the most miles per gallon possible, while taking care that the car does not cost a million dollars.

  • Preface

  • Achille Messac, Mississippi State University
  • Book:
    Optimization in Practice with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    19 March 2015, pp xxv-xxx

  • Summary

    Contacting the Author Regarding this Book

    I am delighted that you are using this book in your study of, or involvement with, optimization. I would very much welcome your comments, particularly regarding this first edition, and any suggestions that you might have for the next edition. For your comments regarding this book, I will be happy to receive your direct email at OptimizationInPracticeMessac@google.com.

    Book Website

    The website www.cambridge.org/Messac will be maintained for this book. Information for instructors and students will be separately provided. Software for various problems will be provided in this website. I expect it to be a dynamic website, where the information available will evolve over time to be responsive to readers’ requests and feedback.

    Book Organization

    This book is intended to be used by undergraduate and graduate students in the classroom, or by industry practitioners learning independently. Its organization suits these objectives. Following are messages specifically tailored for students, for industry practitioners and for instructors. The book has five parts:

  • Part I Helpful Preliminaries

  • Part II Using Optimization—The Road Map

  • Part III Using Optimization—Practical Essentials

  • Part IV Going Deeper: Inside the Codes and Theoretical Aspects

  • Part V More Advanced Topics in Optimization

    • Part I has two chapters that present prerequisite material explaining how to use Matlab and some useful mathematical information. Most of this book assumes knowledge of undergraduate calculus and elementary linear algebra. The second chapter provides a brief review of the math needed. In Part II, three chapters introduce the world of optimization in the form of a road map. This part provides the basics of what should be known about optimization before attempting to use it. In Part III, there are five chapters that teach the basic use of optimization. In fact, learning the material up to Part III provides the practitioner with sufficient information for solving practical problems. In doing so, student users will not be experts on how optimization works under the hood, so to speak, but will have the ability to use optimization in general practical contexts.

Page 42 of 78