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.
