Engineering Optimization: Theory and Practice, Fourth Edition

Technology/Engineering/Mechanical

Helps you move from theory to optimizing engineering systems in almost any industry

Now in its Fourth Edition, Professor Singiresu Rao's acclaimed text Engineering Optimization enables readers to quickly master and apply all the important optimization methods in use today across a broad range of industries. Covering both the latest and classical optimization methods, the text starts off with the basics and then progressively builds to advanced principles and applications.

This comprehensive text covers nonlinear, linear, geometric, dynamic, and stochastic programming techniques as well as more specialized methods such as multiobjective, genetic algorithms, simulated annealing, neural networks, particle swarm optimization, ant colony optimization, and fuzzy optimization. Each method is presented in clear, straightforward language, making even the more sophisticated techniques easy to grasp. Moreover, the author provides:

• Case examples that show how each method is applied to solve real-world problems across a variety of industries

• Review questions and problems at the end of each chapter to engage readers in applying their newfound skills and knowledge

• Examples that demonstrate the use of MATLAB® for the solution of different types of practical optimization problems

• References and bibliography at the end of each chapter for exploring topics in greater depth

• Answers to Review Questions available on the author's Web site to help readers to test their understanding of the basic concepts

With its emphasis on problem-solving and applications, Engineering Optimization is ideal for upper-level undergraduates and graduate students in mechanical, civil, electrical, chemical, and aerospace engineering. In addition, the text helps practicing engineers in almost any industry design improved, more efficient systems at less cost.Content:
Chapter 1 Introduction to Optimization (pages 1–62):
Chapter 2 Classical Optimization Techniques (pages 63–118):
Chapter 3 Linear Programming I: Simplex Method (pages 119–176):
Chapter 4 Linear Programming II: Additional Topics and Extensions (pages 177–247):
Chapter 5 Nonlinear Programming I: One?Dimensional Minimization Methods (pages 248–300):
Chapter 6 Nonlinear Programming II: Unconstrained Optimization Techniques (pages 301–379):
Chapter 7 Nonlinear Programming III: Constrained Optimization Techniques (pages 380–491):
Chapter 8 Geometric Programming (pages 492–543):
Chapter 9 Dynamic Programming (pages 544–587):
Chapter 10 Integer Programming (pages 588–631):
Chapter 11 Stochastic Programming (pages 632–667):
Chapter 12 Optimal Control and Optimality Criteria Methods (pages 668–692):
Chapter 13 Modern Methods of Optimization (pages 693–736):
Chapter 14 Practical Aspects of Optimization (pages 737–778):