Engineering optimisation by cuckoo search

By Xin-She Yang, Suash Deb. Int. J. Math. Model. Numer. Optimisation, 2010

1,815 citations recorded by the source corpus.

A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0d9155f763ad1d72e67b9ea5dbf6c84e62fcf535

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Citations in the corpus

RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021, methodology): “…and efficiency were compared with those of other well-known algorithms, including the GWO (Mirjalili, et al., 2014), WOA(Mirjalili & Lewis, 2016), WCA (Eskandar, et al., 2012), IWO (Hosseini, 2007), and CS (Yang & Deb, 2010) algorithms, based on the average and standard deviation of the results.”
Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization (2020, methodology): “This behavior was used to generate new solutions for a cuckoo based on the following equation [42]:”
Bat algorithm: literature review and applications (2013, methodology): “Many variants of PSO and improvements exist in the literature, and many new metaheuristic algorithms have been developed (Cui, 2009; Yang, 2010; Yang and Deb, 2010; Yang et al., 2011; Yang et al., 2013).”
Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm (2012, neutral): “, 1996), ABC, PSO, Cuckoo-Search Algorithm (Yang and Deb, 2010; Civicioglu and Besdok, 2011).”
Stochastic Fractal Search: A powerful metaheuristic algorithm (2015, methodology): “The competence of metaheuristic algorithms can depend on the fact that they imitate the best features in nature, especially the selection of the fittest in biological systems which has evolved by natural selection over millions of years [2].”
Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy (2014, methodology): “In 2009, Yang and Deb (2009) and Yang and Deb (2010) formulated a new metaheuristic algorithm, called cuckoo search algorithm which is based on the interesting breeding behavior such as brood parasitism of certain species of Cuckoos, and the preliminary studies show that it is very promising and…”
Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “…et al., 2013), bean optimization algorithm (Zhang et al., 2010), individual bird based algorithms based on doves (Su et al., 2009), and eagles (Yang & Deb, 2010); individual insect based algorithms like fruit fly (Pan, 2012), wasp (Theraulaz et al. 1991), and glow-worm (Krishnanand and Ghose,…”
A social spider algorithm for global optimization (2015, methodology): “…these state-of-the-art algorithms, we also performed simulations with other famous algorithms, namely, Real-Coded Genetic Algorithm [42], Adaptive Particle Swarm Optimization [43], Artiﬁcial Bee Colony Optimization [18], Fireﬂy Algorithm [22], Cuckoo Search [44], and Group Search Optimizer [45].”
A cuckoo search algorithm by Lévy flights for solving reliability redundancy allocation problems (2013, methodology): “…of Sistan and Baluchestan, Iran; bDepartment of Mathematics, Islamic Azad University of Kermanshah Branch, Iran (Received 27 April 2012; final version received 20 August 2012) A new metaheuristic optimization algorithm, called cuckoo search (CS), was recently developed by Yang and Deb (2009, 2010).”
Modified Grey Wolf Optimizer for Global Engineering Optimization (2016, methodology): “A new evolutionary optimization algorithm, Cuckoo Search (CS) Algorithm [23], is inspired by lifestyle of cuckoo birds.”
Bat algorithm for multi-objective optimisation (2011, methodology): “For example, a new cuckoo search algorithm was developed by Xin-She Yang and Suash Deb (2009) and more detailed studies by the same authors (Yang and Deb, 2010a) suggested that it is very efficient for solving nonlinear engineering design problems.”