A unified view of metaheuristics
This book provides a complete background on metaheuristics andshows readers how to design and implement efficient algorithms tosolve complex optimization problems across a diverse range ofapplications, from networking and bioinformatics to engineeringdesign, routing, and scheduling. It presents the main designquestions for all families of metaheuristics and clearlyillustrates how to implement the algorithms under a softwareframework to reuse both the design and code.
Throughout the book, the key search components of metaheuristicsare considered as a toolbox for:
* Designing efficient metaheuristics (e.g. local search, tabusearch, simulated annealing, evolutionary algorithms, particleswarm optimization, scatter search, ant colonies, bee colonies,artificial immune systems) for optimization problems
* Designing efficient metaheuristics for multi-objectiveoptimization problems
* Designing hybrid, parallel, and distributed metaheuristics
* Implementing metaheuristics on sequential and parallelmachines
Using many case studies and treating design and implementationindependently, this book gives readers the skills necessary tosolve large-scale optimization problems quickly and efficiently. Itis a valuable reference for practicing engineers and researchersfrom diverse areas dealing with optimization or machine learning;and graduate students in computer science, operations research,control, engineering, business and management, and appliedmathematics.