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Multi-objective optimization in theo...

Keller, André A.

Multi-objective optimization in theory and practice II[electronic resource] :metaheuristic algorithms /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	511.8
書名/作者:	Multi-objective optimization in theory and practice II : metaheuristic algorithms // authored by André A. Keller.
作者:	Keller, André A.
出版者:	Sharjah, UAE : : Bentham Science Publishers,, 2019.
面頁冊數:	1 online resource (310 p.) : : ill.
標題:	Algorithms.
標題:	Metaheuristics.
ISBN:	9781681087054
書目註:	Includes bibliographical references at the end of each chapters and index.
摘要、提要註:	Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to 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 solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.
電子資源:	https://doi.org/10.2174/97816810870541190101

Multi-objective optimization in theory and practice II[electronic resource] :metaheuristic algorithms /
Keller, André A.

Multi-objective optimization in theory and practice IImetaheuristic algorithms /[electronic resource] :authored by André A. Keller. - 1st ed. - Sharjah, UAE :Bentham Science Publishers,2019. - 1 online resource (310 p.) :ill.

Includes bibliographical references at the end of each chapters and index.

Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to 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 solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.

ISBN: 9781681087054Subjects--Topical Terms:

182797
Algorithms.

LC Class. No.: QA9.58

Dewey Class. No.: 511.8

Multi-objective optimization in theory and practice II[electronic resource] :metaheuristic algorithms /
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005 20191002042609.8
006 m o d
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020 $a 9781681087054 $q (electronic bk.)
020 $z 9781681087061
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041 0 $a eng
050 4 $a QA9.58
082 0 4 $a 511.8 $2 23
100 1 $a Keller, André A. $3 710111
245 1 0 $a Multi-objective optimization in theory and practice II $h [electronic resource] : $b metaheuristic algorithms / $c authored by André A. Keller.
250 $a 1st ed.
260 $a Sharjah, UAE : $b Bentham Science Publishers, $c 2019.
300 $a 1 online resource (310 p.) : $b ill.
504 $a Includes bibliographical references at the end of each chapters and index.
520 $a Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to 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 solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.
588 $a Description based on print version record.
650 0 $a Algorithms. $3 182797
650 0 $a Metaheuristics. $3 665404
856 4 0 $u https://doi.org/10.2174/97816810870541190101

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