Flower Pollination Algorithm and its Applications in Engineering.- An Evolutionary Discrete Firefly Algorithm with

Novel Operators for Solving the Vehicle Routing Problem with TimeWindows.- The Plant Propagation Algorithm for Discrete Optimisation: The Case of the Travelling Salesman Problem.- Enhancing Cooperative Coevolution with Surrogate-Assisted Local Search.- Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial
Optimization.- Clustering Optimization for WSN based on Nature-Inspired Algorithms.- Discrete Firefly Algorithm for Recruiting Task in a Swarm of Robots.- Nature-Inspired Swarm Intelligence for Data Fitting in Reverse Engineering: Recent Advances and FutureTrends.- A Novel Fast Optimisation Algorithm Using Differential Evolution Algorithm Optimisation and Meta-
Modelling Approach.- A Hybridization of Runner-Based and Seed-Based Plant Propagation
Algorithm.- Gravitational Search Algorithm Applied to Cell Formation Problem.- Parameterless Bat Algorithm and its Performace Study.

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.