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.
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