Hybrid metaheuristics : powerful tools for optimization
- 作者： Blum, Christian, author.
- 其他作者：
- 其他題名：
- Artificial intelligence: foundations, theory, and algorithms.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Artificial intelligence: foundations, theory, and algorithms,
- 主題： Heuristic programming. , Mathematical optimization. , Computer Science. , Artificial Intelligence (incl. Robotics) , Theory of Computation. , Computational Intelligence. , Operation Research/Decision Theory. , Optimization.
- ISBN： 9783319308838 (electronic bk.) 、 9783319308821 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Introduction -- Incomplete Solution Representations and Decoders -- Hybridization Based on Problem Instance Reduction -- Hybridization Based on Large Neighborhood Search -- Making Use of a Parallel, Non-independent, Construction of Solutions Within Metaheuristics -- Hybridization Based on Complete Solution Archives -- Further Hybrids and Conclusions.
- 摘要註: This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives. The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.
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讀者標籤：
- 系統號： 005361137 | 機讀編目格式
館藏資訊
This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives. The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.