Engineering Optimization: A Modern Approach

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 selfsufficient 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

Review :
"… an essential guide to optimization techniques that are used in engineering. … several examples are presented and solved, which greatly helps the understanding of each technique …" — Zentralblatt MATH 1247