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.
