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.