Springer Hybrid Metaheuristics: Powerful Tools for Optimization (Artificial Intelligence: Foundations, Theory, and Algorithms)

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. Review “From the perspective of a practitioner with real-world experience in combinatorial optimization, the text is comprehensive, and at the same time it offers fresh angles and shares valuable expertise. … I highly recommend this book, both to practitioners and theoreticians at the post graduate levels, be they rooted either at the ‘formal/rigid’ or ‘heuristic/soft’ ends of Combinatorial Optimization research or practice.” (Ofer M. Shir, Genetic Programming and Evolvable Machines, Vol. 19 (1-2), June, 2018) “This book by Blum and Raidl constructs a bridge between these two approaches and aims to share expertise gained from each end. … The book is well-structured. … I highly recommend this book, both to practitioners and theoreticians at the post graduate levels, be they rooted either at the ‘formal/rigid’ or ‘heuristic/soft’ ends of Combinatorial Optimization research or practice.” (Ofer M. Shir, Genetic Programming and Evolvable Machines, Vol. 19 (1-2), June, 2018) Review        From the Back Cover 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, operati