The Handbook of Metaheuristics is a comprehensive collection of chapters that cover various metaheuristic methods used in optimization. It includes topics such as Scatter Search, Tabu Search, Genetic Algorithms, Genetic Programming, Memetic Algorithms, Variable Neighborhood Search, Guided Local Search, GRASP, Ant Colony Optimization, Simulated Annealing, Iterated Local Search, Multi-Start Methods, Constraint Programming, Constraint Satisfaction, Neural Network Methods for Optimization, Hyper-Heuristics, Parallel Strategies for Metaheuristics, Metaheuristic Class Libraries, and A-Teams. These methods are designed to solve complex problems, especially those of a combinatorial nature, by escaping from local optima and performing robust searches of solution spaces. The handbook is intended to provide researchers and practitioners with a broad coverage of the concepts, themes, and instrumentalities of this important and evolving area of optimization. It aims to encourage the wider adoption of metaheuristic methods for problem solving and to stimulate research that may lead to additional innovations in metaheuristic procedures. The chapters in the handbook are designed to facilitate the modification of basic methods in response to problem characteristics, past experiences, and personal preferences. The authors who have contributed to this volume represent leading figures from the metaheuristic community and are responsible for pioneering contributions to the fields they write about. Their collective work has significantly enriched the field of optimization in general and combinatorial optimization in particular. The handbook is a valuable resource for both researchers and practitioners in the field of optimization.The Handbook of Metaheuristics is a comprehensive collection of chapters that cover various metaheuristic methods used in optimization. It includes topics such as Scatter Search, Tabu Search, Genetic Algorithms, Genetic Programming, Memetic Algorithms, Variable Neighborhood Search, Guided Local Search, GRASP, Ant Colony Optimization, Simulated Annealing, Iterated Local Search, Multi-Start Methods, Constraint Programming, Constraint Satisfaction, Neural Network Methods for Optimization, Hyper-Heuristics, Parallel Strategies for Metaheuristics, Metaheuristic Class Libraries, and A-Teams. These methods are designed to solve complex problems, especially those of a combinatorial nature, by escaping from local optima and performing robust searches of solution spaces. The handbook is intended to provide researchers and practitioners with a broad coverage of the concepts, themes, and instrumentalities of this important and evolving area of optimization. It aims to encourage the wider adoption of metaheuristic methods for problem solving and to stimulate research that may lead to additional innovations in metaheuristic procedures. The chapters in the handbook are designed to facilitate the modification of basic methods in response to problem characteristics, past experiences, and personal preferences. The authors who have contributed to this volume represent leading figures from the metaheuristic community and are responsible for pioneering contributions to the fields they write about. Their collective work has significantly enriched the field of optimization in general and combinatorial optimization in particular. The handbook is a valuable resource for both researchers and practitioners in the field of optimization.