Description

Book Synopsis
This well-received book, now in its second edition, continues to provide a number of optimization algorithms which are commonly used in computer-aided engineering design. The book begins with simple single-variable optimization techniques, and then goes on to give unconstrained and constrained optimization techniques in a step-by-step format so that they can be coded in any user-specific computer language. In addition to classical optimization methods, the book also discusses Genetic Algorithms and Simulated Annealing, which are widely used in engineering design problems because of their ability to find global optimum solutions.The second edition adds several new topics of optimization such as design and manufacturing, data fitting and regression, inverse problems, scheduling and routing, data mining, intelligent system design, Lagrangian duality theory, and quadratic programming and its extension to sequential quadratic programming. It also extensively revises the linear programming algorithms section in the Appendix. This edition also includes more number of exercise problems.The book is suitable for senior undergraduate/postgraduate students of mechanical, production and chemical engineering. Students in other branches of engineering offering optimization courses as well as designers and decision-makers will also find the book useful.This book is recommended in NIT Agartala, Tripura, Assam Engineering College, Assam, IIT Guwuhati, Asssam and NIT Silchar, Assam.Key Features:Algorithms are presented in a step-by-step format to facilitate coding in a computer language.Sample computer programs in FORTRAN are appended for better comprehension.Worked-out examples are illustrated for easy understanding.The same example problems are solved with most algorithms for a comparative evaluation of the algorithms.Read more

Optimization for Engineering Design - Algorithms

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    A Paperback / softback by Kalyanmoy Deb

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      View other formats and editions of Optimization for Engineering Design - Algorithms by Kalyanmoy Deb

      Publisher: PHI Learning
      Publication Date: Publication Date: 30/07/2013
      ISBN13: 9788120346789, 978-8120346789
      ISBN10: 8120346785

      Description

      Book Synopsis
      This well-received book, now in its second edition, continues to provide a number of optimization algorithms which are commonly used in computer-aided engineering design. The book begins with simple single-variable optimization techniques, and then goes on to give unconstrained and constrained optimization techniques in a step-by-step format so that they can be coded in any user-specific computer language. In addition to classical optimization methods, the book also discusses Genetic Algorithms and Simulated Annealing, which are widely used in engineering design problems because of their ability to find global optimum solutions.The second edition adds several new topics of optimization such as design and manufacturing, data fitting and regression, inverse problems, scheduling and routing, data mining, intelligent system design, Lagrangian duality theory, and quadratic programming and its extension to sequential quadratic programming. It also extensively revises the linear programming algorithms section in the Appendix. This edition also includes more number of exercise problems.The book is suitable for senior undergraduate/postgraduate students of mechanical, production and chemical engineering. Students in other branches of engineering offering optimization courses as well as designers and decision-makers will also find the book useful.This book is recommended in NIT Agartala, Tripura, Assam Engineering College, Assam, IIT Guwuhati, Asssam and NIT Silchar, Assam.Key Features:Algorithms are presented in a step-by-step format to facilitate coding in a computer language.Sample computer programs in FORTRAN are appended for better comprehension.Worked-out examples are illustrated for easy understanding.The same example problems are solved with most algorithms for a comparative evaluation of the algorithms.Read more

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