Hybrid Metaheuristics

This book explains the most prominent, successful§hybridization techniques and some newer promising strategies. 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, constraint§programming or statistical modeling 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.§§