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Language | English |

Pages | 830 |

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Size | 4.53 MB |

Engineering Optimization Theory and Practice 4th Edition by Singiresu S. Rao | PDF Free Download.

- Introduction to Optimization
- Classical Optimization Techniques
- Linear Programming I: Simplex Method
- Linear Programming II: Additional Topics and Extensions
- Nonlinear Programming I: One-Dimensional Minimization Methods
- Nonlinear Programming II: Unconstrained Optimization Techniques
- Nonlinear Programming III: Constrained Optimization Techniques
- Geometric Programming
- Dynamic Programming
- Integer Programming
- Stochastic Programming
- Optimal Control and Optimality Criteria Methods
- Modern Methods of Optimization
- Practical Aspects of Optimization

The ever-increasing demand on engineers to lower production costs to withstand global competition has prompted engineers to look for rigorous methods of decision making, such as optimization methods, to design and produce products and systems both economically and efficiently.

Optimization techniques, having reached a degree of maturity in recent years, are being used in a wide spectrum of industries, including aerospace, automotive, chemical, electrical, construction, and manufacturing industries.

With rapidly advancing computer technology, computers are becoming more powerful, and correspondingly, the size and the complexity of the problems that can be solved using optimization techniques are also increasing.

Optimization methods, coupled with modern tools of computer-aided design, are also being used to enhance the creative process of the conceptual and detailed design of engineering systems.

The purpose of this textbook is to present the techniques and applications of engineering optimization in a comprehensive manner.

The style of the prior editions has been retained, with the theory, computational aspects, and applications of engineering optimization presented with detailed explanations.

As in previous editions, essential proofs and developments of the various techniques are given in a simple manner without sacrificing accuracy. New concepts are illustrated with the help of numerical examples.

Although most engineering design problems can be solved using nonlinear programming techniques, there are a variety of engineering applications for which other optimization methods, such as linear, geometric, dynamic, integer, and stochastic programming techniques, are most suitable.

The theory and applications of all these techniques are also presented in the book. Some of the recently developed methods of optimization, such as genetic algorithms, simulated annealing, particle swarm optimization, ant colony optimization, neural-network-based methods, and fuzzy optimization, are also discussed.

Favorable reactions and encouragement from professors, students, and other users of the book have provided me with the impetus to prepare this fourth edition of the book. The following changes have been made from the previous edition:

- Some less-important sections were condensed or deleted.
- Some sections were rewritten for better clarity.
- Some sections were expanded.
- A new chapter on modern methods of optimization is added.
- Several examples to illustrate the use of Matlab for the solution of different types of optimization problems are given.

The book consists of fourteen chapters and three appendixes.

Chapter 1 provides an introduction to engineering optimization and optimum design and an overview of optimization methods. The concepts of design space, constraint surfaces, and contours of the objective function are introduced here.

In addition, the formulation of various types of optimization problems is illustrated through a variety of examples taken from various fields of engineering.

Chapter 2 reviews the essentials of differential calculus useful in finding the maxima and minima of functions of several variables.

The methods of constrained variation and Lagrange multipliers are presented for solving problems with equality constraints. The Kuhn–Tucker conditions for inequality-constrained problems are given along with a discussion of convex programming problems.

Chapters 3 and 4 deal with the solution of linear programming problems. The characteristics of a general linear programming problem and the development of the simplex method of the solution are given in Chapter 3.

Some advanced topics in linear programmings, such as the revised simplex method, duality theory, the decomposition principle, and post-optimality analysis, are discussed in Chapter 4.

The extension of linear programming to solve quadratic programming problems is also considered in Chapter 4.

Chapters 5–7 deal with the solution of nonlinear programming problems. In Chapter 5, numerical methods of finding the optimum solution of a function of a single variable are given.

Chapter 6 deals with the methods of unconstrained optimization. The algorithms for various zeroth-, first-, and second-order techniques are discussed along with their computational aspects. Chapter 7 is concerned with the solution of nonlinear optimization problems in the presence of inequality and equality constraints.

Both the direct and indirect methods of optimization are discussed. The methods presented in this chapter can be treated as the most general techniques for the solution of any optimization problem.

Chapter 8 presents the techniques of geometric programming. The solution techniques for problems of mixed inequality constraints and complementary geometric programming are also considered.

In Chapter 9, computational procedures for solving discrete and continuous, dynamic programming problems are presented. The problem of dimensionality is also discussed.

Chapter 10 introduces integer programming and gives several algorithms for solving integer and discrete linear and nonlinear optimization problems.

Chapter 11 reviews the basic probability theory and presents techniques of stochastic linear, nonlinear, and geometric programming. The theory and applications of the calculus of variations, optimal control theory, and optimality criteria methods are discussed briefly in Chapter 12.

Chapter 13 presents several modern methods of optimization including genetic algorithms, simulated annealing, particle swarm optimization, ant colony optimization, neural-network-based methods, and fuzzy system optimization.

Several of the approximation techniques used to speed up the convergence of practical mechanical and structural optimization problems, as well as parallel computation and multiobjective optimization techniques are outlined in Chapter 14.

Appendix A presents the definitions and properties of convex and concave functions. A brief discussion of the computational aspects and some of the commercial optimization programs are given in Appendix B.

Finally, Appendix C presents a brief introduction to Matlab, optimization toolbox, and the use of Matlab programs for the solution of optimization problems.

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