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Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms

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Title: Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms
Description: 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 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.
Authors: André A. Keller
Resource Type: eBook.
Subjects: Metaheuristics, Algorithms, Computer algorithms
Categories: MATHEMATICS / Optimization, TECHNOLOGY & ENGINEERING / Operations Research
Database: eBook Collection (EBSCOhost)
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  – Type: ebook-epub
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  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
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RelevancyScore: 1090
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
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  Data: 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 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.
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      – Code: 511.8
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Metaheuristics
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Computer algorithms
        Type: general
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      – TitleFull: Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms
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            NameFull: André A. Keller
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2019
            – D: 02
              M: 10
              Type: profile
              Y: 2019
          Identifiers:
            – Type: isbn-print
              Value: 9781681087061
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              Value: 9781681087054
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            – TitleFull: Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms
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