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1577 results in Control systems and optimization

Page 22 of 79

  • 9 - Multiobjective Optimization

  • Joaquim R. R. A. Martins, University of Michigan, Ann Arbor, Andrew Ning, Brigham Young University, Utah
  • Book:
    Engineering Design Optimization
    Published online:
    05 January 2022
    Print publication:
    18 November 2021, pp 355-372

  • Summary

    Up to this point in the book, all of our optimization problem formulations have had a single objective function. In this chapter, we consider multiobjective optimization problems, that is, problems whose formulations have more than one objective function. Some common examples of multiobjective optimization include risk versus reward, profit versus environmental impact, acquisition cost versus operating cost, and drag versus noise.

  • 7 - Gradient-Free Optimization

  • Joaquim R. R. A. Martins, University of Michigan, Ann Arbor, Andrew Ning, Brigham Young University, Utah
  • Book:
    Engineering Design Optimization
    Published online:
    05 January 2022
    Print publication:
    18 November 2021, pp 281-326

  • Summary

    Gradient-free algorithms fill an essential role in optimization. The gradient-based algorithms introduced inare efficient in finding local minima for high-dimensional nonlinear problems defined by continuous smooth functions. However, the assumptions made for these algorithms are not always valid, which can render these algorithms ineffective. Also, gradients might not be available when a function is given as a black box.

Understanding Process Dynamics and Control
  • Costas Kravaris, Ioannis K. Kookos
  • Published online:
    29 October 2021
    Print publication:
    08 April 2021
  • Presenting a fresh look at process control, this new text demonstrates state-space approach shown in parallel with the traditional approach to explain the strategies used in industry today. Modern time-domain and traditional transform-domain methods are integrated throughout and explain the advantages and limitations of each approach; the fundamental theoretical concepts and methods of process control are applied to practical problems. To ensure understanding of the mathematical calculations involved, MATLAB® is included for numeric calculations and MAPLE for symbolic calculations, with the math behind every method carefully explained so that students develop a clear understanding of how and why the software tools work. Written for a one-semester course with optional advanced-level material, features include solved examples, cases that include a number of chemical reactor examples, chapter summaries, key terms, and concepts, as well as over 240 end-of-chapter problems, focused computational exercises and solutions for instructors.

Design Optimization using MATLAB and SOLIDWORKS
  • Krishnan Suresh
  • Published online:
    17 July 2021
    Print publication:
    29 April 2021
  • A unique text integrating numerics, mathematics and applications to provide a hands-on approach to using optimization techniques, this mathematically accessible textbook emphasises conceptual understanding and importance of theorems rather than elaborate proofs. It allows students to develop fundamental optimization methods before delving into MATLAB®'s optimization toolbox, and to link MATLAB's results with the results from their own code. Following a practical approach, the text demonstrates several applications, from error-free analytic examples to truss (size) optimization, and 2D and 3D shape optimization, where numerical errors are inevitable. The principle of minimum potential energy is discussed to highlight the deep relationship between engineering and optimization. MATLAB code in every chapter illustrates key concepts and the text demonstrates the coupling between MATLAB and SOLIDWORKS® for design optimization. A wide variety of optimization problems are covered including constrained non-linear, linear-programming, least-squares, multi-objective, and global optimization problems.

Page 22 of 79