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Ity of Cambridge, United Kingdom d E d ion (20 it Luniver Press Published in 2010 by Luniver Press Frome, BA11 6TT, United Kingdom www.luniver.com Copyright c Luniver Press 2010 Copyright c Xin-She Yang 2010 All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without permission in writing from the copyright holder. British Library C.

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Famous examples of metaheuristics are genetic algorithms, particle swarm optimization, simulated annealing, and variable neighborhood search.

Nature‐inspired optimization algorithms (NIOA) are a set of algorithms that are illumed by the behavior of natural situations. NIOAs have been inspired by animal behaviors, biology, chemical reactions, etc. This has provided engineering solutions, medical solutions, etc.

Algorithms Simulated annealing (Kirkpatrick et al., 1983) Ant colony optimization (ACO) (Dorigo, 1992) Particle swarm optimization (PSO) (Kennedy & Eberhart, 1995) Harmony search (HS) (Geem, Kim & Loganathan, 2001) Artificial bee colony algorithm (Karaboga, 2005) Bees algorithm (Pham, 2005)

Nature-inspired optimization algorithms (NIOAs), defined as a group of algorithms that are inspired by natural phenomena, including swarm intelligence, biological systems, physical and chemical systems and, etc..

Nature‐inspired optimization algorithms (NIOA) are a set of algorithms that are illumed by the behavior of natural situations. NIOAs have been inspired by animal behaviors, biology, chemical reactions, etc. This has provided engineering solutions, medical solutions, etc.

Metaheuristic is an approach method based on a heuristic method that does not rely on the type of the problem. The metaheuristic method can be distinguished into two which are metaheuristic with single-solution based (local search) and metaheuristic based on population (random search).

The meta heuristic approach uses a group of search agents to explore feasible regions in search space, following specific rules. Nature inspired Computing is paradigm falls in the meta heuristic category. It uses an analogy from nature to generate approximate solutions for practical optimization problems.

Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, variable neighborhood search, (adaptive) large neighborhood search, and ant colony optimization, although many more exist.

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