The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems
Abstract
Abstract This paper presents a novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.
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Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
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
Holland, J. H. (1992) · Scientific American · doi:10.1038/scientificamerican0792-66 · Cited by 5,764
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Self-citationTunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization
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Self-citationJia, H., Peng, X., & Lang, C. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115665 · Cited by 417
Metadata
- Authors
- Dhiman, G., & Kumar, V.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 165
- Pages
- 169-196
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2902421512
- Cited by
- 1,195
- References
- 4
- FWCI
- 34.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2018.11.024
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