The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications
Abstract
Abstract A new bio-inspired optimization technique, named Manta Ray Foraging Optimization (MRFO) algorithm, is proposed and presented, aiming to providing a novel algorithm that provides an alternate optimization approach for addressing real-world engineering issues. The inspiration of this algorithm is based on intelligent behaviors of manta rays. This work mimics three unique foraging strategies of manta rays, including chain foraging, cyclone foraging, and somersault foraging, to develop an efficient optimization paradigm for solving different optimization problems. The performance of MRFO is evaluated, through comparisons with other state-of-the-art optimizers, on benchmark optimization functions and eight real-world engineering design cases. The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors. In addition, the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision. The MATLAB codes of the MRFO algorithm are available at https://www.mathworks.com/matlabcentral/fileexchange/73130-manta-ray-foraging-optimization-mrfo .
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Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,002
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 19,343
The Whale Optimization Algorithm
Mirjalili, S., & Lewis, A. (2016) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2016.01.008 · Cited by 14,581
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Ant system: optimization by a colony of cooperating agents
Dorigo, M., Maniezzo, V., & Colorni, A. (1996) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.484436 · Cited by 12,067
GSA: A Gravitational Search Algorithm
Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009) · Information Sciences · doi:10.1016/j.ins.2009.03.004 · Cited by 7,358
A New Heuristic Optimization Algorithm: Harmony Search
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001) · SIMULATION · doi:10.1177/003754970107600201 · Cited by 6,247
Cuckoo Search via Lévy flights
Yang, X.-S., & Deb, S. (2009) · 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) · doi:10.1109/nabic.2009.5393690 · Cited by 6,185
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New machine learning method for image-based diagnosis of COVID-19
Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Lu, S., & Sahlol, A. T. (2020) · PLOS ONE · doi:10.1371/journal.pone.0235187 · Cited by 396
Metadata
- Authors
- Zhao, W., Zhang, Z., & Wang, L.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 87
- Pages
- 103300
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2982453621
- Cited by
- 1,080
- References
- 84
- FWCI
- 44.67
- Type
- journal article
- Language
- en
- Open access
- closed
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