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2019 | OriginalPaper | Buchkapitel

7. Cuckoo Search Algorithm

verfasst von : Ali Kaveh, Taha Bakhshpoori

Erschienen in: Metaheuristics: Outlines, MATLAB Codes and Examples

Verlag: Springer International Publishing

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Abstract

Cuckoo Search (CS) algorithm was developed by Yang and Deb [1, 2] as an efficient population-based metaheuristic inspired by the behavior of some cuckoo species in combination with the Lévy flight. It is also used in steel structural optimization problems by Kaveh and Bakhshpoori [3, 4]. Cuckoos are fascinating birds because of their special lifestyle and aggressive reproduction strategy. These species lay their eggs in the nests of other host birds with amazing abilities like selecting the recently spawned nests and removing existing eggs that increase the hatching probability of their eggs. The host takes care of the eggs presuming that the eggs are its own. However, some of host birds are able to combat with this parasites behavior of cuckoos and throw out the discovered alien eggs or build their new nests in new locations. Metaheuristics update the candidate solutions repetitively using the step sizes generated in the search space based on the inspired mechanisms or formulations. The randomization plays an important role in both exploration and exploitation in meta-heuristic algorithms. Therefore, these step sizes combined also with a series of consecutive random steps. In fact, the randomization part of the step sizes can vary according to a known distribution. A very special case can be achieved when the step length obeys the Lévy distribution and is called Lévy flight. The cuckoo breeding analogy combining with the Lévy flight behavior is the essence of the CS. Each solution represents a nest or an egg or a bird. Like other metaheuristics CS starts from randomly generated initial candidate solutions. These are the eggs of host birds. In the cyclic body of the algorithm, cuckoos aim to improve the quality of the solutions by generating new eggs with step sizes toward the best known solution in combination with the Lévy flight and intruded them to the nests of host birds. Moreover, in each iteration, after generating the new eggs by cuckoos, host birds have a chance to determine the alien eggs, abandon them, and generate new ones in the search space. Discovering the cuckoo’s eggs and building new nests instead of them can take place with different mechanisms. For example, it can take place for a fraction of eggs.

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Literatur
1.
Zurück zum Zitat Yang XS, Deb S (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol Yang XS, Deb S (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol
2.
Zurück zum Zitat Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1:330–343MATH Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1:330–343MATH
3.
Zurück zum Zitat Kaveh A, Bakhshpoori T, Afshari E (2011) An optimization-based comparative study of double layer grids with two different configurations using cuckoo search algorithm. Int J Optim Civ Eng 1:507–520 Kaveh A, Bakhshpoori T, Afshari E (2011) An optimization-based comparative study of double layer grids with two different configurations using cuckoo search algorithm. Int J Optim Civ Eng 1:507–520
4.
Zurück zum Zitat Kaveh A, Bakhshpoori T, Ashoory M (2012) An efficient optimization procedure based on cuckoo search algorithm for practical design of steel structures. Int J Optim Civ Eng 2:1–14 Kaveh A, Bakhshpoori T, Ashoory M (2012) An efficient optimization procedure based on cuckoo search algorithm for practical design of steel structures. Int J Optim Civ Eng 2:1–14
Metadaten
Titel
Cuckoo Search Algorithm
verfasst von
Ali Kaveh
Taha Bakhshpoori
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-04067-3_7

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