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This book explores prominent and promising techniques that merge metaheuristics with various optimization methods. It begins with an introductory chapter that reviews local search principles, key metaheuristics, and techniques like tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization. Subsequent chapters detail five broadly applicable hybridization strategies, illustrated with case studies on selected problems: incomplete solution representations and decoders, problem instance reduction, large neighborhood search, parallel non-independent solution construction within metaheuristics, and hybridization using complete solution archives. The authors are leading researchers in the hybridization of metaheuristics, reflecting a shift towards problem-oriented approaches that enhance real-life application efficiency. This hybridization extends beyond different metaheuristic variants to include combinations with mathematical programming, dynamic programming, and constraint programming, showcasing cross-fertilization across optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book serves as a valuable introduction and reference for researchers and graduate students in these fields.
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Hybrid Metaheuristics, Christian Blum
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- 2018
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