Metaheuristics
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Metaheuristics provides a complete background of metaheuristics, enabling readers to design and deploy powerful algorithms to solve complex optimization problems in a diverse range of industries. Using case studies in different domains, including telecommunications, transportation and logistics, bioinformatics, design engineering, and scheduling provides clear information for these diverse markets. The book is an effective resource for engineers, researchers, and developers, and an ideal text for graduate students in computer science, bioinformatics, electrical engineering, and applied mathematics courses.
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.
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.
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