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Engineering optimisation by cuckoo search
Abstract
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 de-signed stochastic test functions. We then apply the CS algorithm to solve \nengineering 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 ptimiser. We will discuss the unique search features used in CS and the implications for fur-ther research.
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Cited by
Grasshopper Optimisation Algorithm: Theory and application
Saremi, S., Mirjalili, S., & Lewis, A. (2017) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.01.004 · Cited by 2,727
Yang, X.-S. (2021) · Nature-Inspired Optimization Algorithms · doi:10.1016/b978-0-12-821986-7.00008-1 · Cited by 677
Self-citationBio inspired computing – A review of algorithms and scope of applications
Kar, A. K. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.018 · Cited by 578
Modified cuckoo search: A new gradient free optimisation algorithm
Walton, S., Hassan, O., Morgan, K., & Brown, M. R. (2011) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2011.06.004 · Cited by 543
Multiobjective firefly algorithm for continuous optimization
Yang, X.-S. (2013) · Engineering with Computers · doi:10.1007/s00366-012-0254-1 · Cited by 513
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Metadata
- Authors
- Yang, X. S., & Deb, S.
- Venue
- International Journal of Mathematical Modelling and Numerical Optimisation
- Publisher
- Inderscience Publishers
- Published
- 2010
- Volume
- 1
- Issue
- 4
- Pages
- 330
- ISSN
- 2040-3607 (ISSN-L)
- 2040-3615
- OpenAlex
- W2000135657
- Cited by
- 1,583
- References
- 0
- FWCI
- 49.65
- Type
- journal article
- Language
- en
- Open access
- closed
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