Ke-Lin Du, M. N. S. Swamy ... 489 pages - Publisher: Springer; (December, 2016) ... Language: English - ISBN-10: 3319454013 - ISBN-13: 978-3319454016.
This textbook provides a comprehensive introduction to
nature-inspired metaheuristic methods for search and optimization,
including the latest trends in evolutionary algorithms and other forms
of natural computing. Over 100 different types of these methods are
discussed in detail. The authors emphasize non-standard optimization
problems and utilize a natural approach to the topic, moving from basic
notions to more complex ones. An introductory
chapter covers the necessary biological and mathematical backgrounds
for understanding the main material. Subsequent chapters then explore
almost all of the major metaheuristics for search and optimization
created based on natural phenomena, including simulated annealing,
recurrent neural networks, genetic algorithms and genetic programming,
differential evolution, memetic algorithms, particle swarm optimization,
artificial immune systems, ant colony optimization, tabu search and
scatter search, bee and bacteria foraging algorithms, harmony search,
biomolecular computing, quantum computing, and many others. General
topics on dynamic, multimodal, constrained, and multiobjective
optimizations are also described. Each chapter includes detailed
flowcharts that illustrate specific algorithms and exercises that
reinforce important topics. Introduced in the appendix are some
benchmarks for the evaluation of metaheuristics. Search
and Optimization by Metaheuristics is intended primarily as a textbook
for graduate and advanced undergraduate students specializing in
engineering and computer science. It will also serve as a valuable
resource for scientists and researchers working in these areas, as well
as those who are interested in search and optimization methods.