Nature-Inspired Computation in Engineering

This timely review book summarizes the
state-of-the-art developments in nature-inspired optimization algorithms and
their applications in engineering. Algorithms and topics include the overview
and history of nature-inspired algorithms, discrete firefly algorithm, discrete
cuckoo search, plant propagation algorithm, parameter-free bat algorithm,
gravitational search, biogeography-based algorithm, differential evolution,
particle swarm optimization and others. Applications include vehicle routing,
swarming robots, discrete and combinatorial optimization, clustering of
wireless sensor networks, cell formation, economic load dispatch, metamodeling,
surrogated-assisted cooperative co-evolution, data fitting and reverse
engineering as well as other case studies in engineering. This book will be an
ideal reference for researchers, lecturers, graduates and engineers who are
interested in nature-inspired computation, artificial intelligence and
computational intelligence. It can also serve as a reference for relevant
courses in computer science, artificial intelligence and machine learning, natural
computation, engineering optimization and data mining.