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.
