Engineering Optimization: Theory and Practice

Engineering Optimization

Engineering 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.

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Engineering Optimization: Theory and Practice

Engineering Optimization: Theory and Practice

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.

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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.

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