Multi Objective Optimization in Theory and Practice I Classical Methods

This book PDF is perfect for those who love Technology & Engineering genre, written by Andre A. Keller and published by Bentham Science Publishers which was released on 13 December 2017 with total hardcover pages 296. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Multi Objective Optimization in Theory and Practice I Classical Methods books below.

Multi Objective Optimization in Theory and Practice I  Classical Methods
Author : Andre A. Keller
File Size : 40,5 Mb
Publisher : Bentham Science Publishers
Language : English
Release Date : 13 December 2017
ISBN : 9781681085685
Pages : 296 pages
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Multi Objective Optimization in Theory and Practice I Classical Methods by Andre A. Keller Book PDF Summary

Multi-Objective Optimization in Theory and Practice is a traditional two-part approach to solving multi-objective optimization (MOO) problems namely the use of classical methods and evolutionary algorithms. This first book is devoted to classical methods including the extended simplex method by Zeleny and preference-based techniques. This part covers three main topics through nine chapters. The first topic focuses on the design of such MOO problems, their complexities including nonlinearities and uncertainties, and optimality theory. The second topic introduces the founding solving methods including the extended simplex method to linear MOO problems and weighting objective methods. The third topic deals with particular structures of MOO problems, such as mixed-integer programming, hierarchical programming, fuzzy logic programming, and bimatrix games. Multi-Objective Optimization in Theory and Practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other mathematical packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science, and mathematics degree programs.

Multi Objective Optimization in Theory and Practice I  Classical Methods

Multi-Objective Optimization in Theory and Practice is a traditional two-part approach to solving multi-objective optimization (MOO) problems namely the use of classical methods and evolutionary algorithms. This first book is devoted to classical methods including the extended simplex method by Zeleny and preference-based techniques. This part covers three main topics

Get Book
Multi Objective Optimization in Theory and Practice II  Metaheuristic Algorithms

Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of

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Classical Methods

Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. The first book presents the use of classical methods and preference-based techniques. The book explains classical methods for solving MOO problems through nine chapters. Topics covered in this part are the design of current

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Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are

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Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need

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