“In conclusion, I found reading Metaheuristics: From Design to Implementation to be pleasant and enjoyable. I particularly recommend it as a reference for researchers and students of computer science or operations research who want a global outlook of metaheuristics methods. It would also be extremely useful for introducing graduate and PhD students who are new to the field of heuristics and metaheuristics to the amazing world of the designing of these procedures.” (Informs, 1 July 2012)
"It will be an indispensable text for advanced undergraduate and graduate students in computer science, operations research, applied mathematics, control, business and management and engineering." (Zentralblatt MATH, 2010)
From the Back Cover
A UNIFIED VIEW OF METAHEURISTICS
This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code.
Throughout the book, the key search components of metaheuristics are considered as a toolbox for:
- Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems
- Designing efficient metaheuristics for multi-objective optimization problems
- Designing hybrid, parallel, and distributed metaheuristics
- Implementing metaheuristics on sequential and parallel machines
Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.