Engineering Optimization

A Modern Approach

Sharing & Social Bookmarking:

Question about this product?

This book provides a thorough understanding of the concepts of optimization methods from a modern perspective at the conceptual stage of complex technical systems. It focuses on nonlinear optimization with emphasis on methods such as response surface and genetic algorithm, an approach suited for moving the concept of optitnization from an academic setting to the industry platform. Equal importance has been given to classical methods also for making the contents sufficient for an undergraduate course.

Table of Contents

Basic Concepts

Statement of the Optimization Problem

Basic Definitions

Taylor Series Expansion

Quadratic Forms

Optimality Criteria for Unconstrained Optimization

Optimality Criteria for Constrained Optimization

Convexity

Examples in Engineering

Problems

Direct One-Dimensional Search

General Numerical Optimization Algorithm

Numerical Methods to Calculate Step Size

Equal-Interval Search

Golden-Section Search

Polynomial Interpolation

Inexact Line Search

Problems

Gradient-Based Methods

Properties of the Gradient Vector

Steepest-Descent Method

Rates of Convergence

Conjugate-Gradient Method

Basic Conjugate-Gradient Algorithms for Quadratic Functions

Conjugate-Gradient Directions for Quadratic Functions

Problems

Newtonian Methods

Newton's Method

Marquardt's Method

Quasi-Newton Methods

Direct-Update Methods

Broyden-Fletcher-Goldfarb-Shanno Method

Davidson-Fletcher-Powell Method

Numerical Derivatives

Automatic Differentiation

Analytical Gradients for Computational Problems

Problems

Constrained Optimization Methods

Linear Programming

The Simplex Method

Two-Phase Simplex Method

Penalty and Barrier Methods

Important Definitions

Sequential Linear Programming

Quadratic Programming

Constrained Steepest-Descent Method

Trust Region Methods

Problems

Response Surface Method

Response Surfaces

The Least-Squares Method

Two-Level Factorial Designs

Addition of Centre Points

Central Composite Design (CCD)

Sequential Nature of RSM

Limitations of RSM

Other Experimental Designs

Taguchi Orthogonal Arrays

Problems

Genetic Algorithm

Optimization Problems

Binary GA

Real Coded G A

Hybrid Genetic Algorithm

Automated Hybrid Genetic Algorithm

Problems

Bibliography

Index

Customers who bought Engineering Optimization also bought:

  • Reliability and Maintenance

    Reliability and Maintenance

    Networks and Systems

  • Advanced Risk Analysis in Engineering Enterprise Systems

    Advanced Risk Analysis in Engineering Enterprise Systems

  • The Lean 3P Advantage

    The Lean 3P Advantage

    A Practitioner's Guide to the Production Preparation Process

  • Fourier Modal Method and Its Applications in Computational Nanophotonics

    Fourier Modal Method and Its Applications in Computational Nanophotonics

  • Handbook of Nonlinear Partial Differential Equations, Second Edition

    Handbook of Nonlinear Partial Differential Equations, Second Edition