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Smell Bees Optimization algorithm for continuous engineering problem
https://orcid.org/http://orcid.org/0000-0002-8339-2470
https://orcid.org/http://orcid.org/0000-0003-2626-0321
Nowadays, optimization meta-heuristic algorithms are used in different fields of science, including a computer, mechanics and civil engineering. The algorithms are inspired by the laws governing nature, such as the principle of physics, an association of animals or finding and hunting food by animals. Using the smell sense of a bee insect, called Smell Bees Optimization (SBO), the present paper proposes an optimization algorithm that is meta-heuristic and inspired by nature. To verify and validate, the proposed algorithm, benchmark functions and engineering design examples were applied, which were previously optimized using different algorithms. In doing so, such as a cantilever beam, pressure vessel, three-bar truss, tension/compression spring and a welded beam were applied, which were previously optimized using different algorithms. In order to run programming, MATLAB was used. The results obtained by SBO are compared to the previous algorithms, optimized solutions of engineering examples are improved, and the target global minimums of the standard benchmark functions are almost obtained.
Smell Bees Optimization algorithm for continuous engineering problem
https://orcid.org/http://orcid.org/0000-0002-8339-2470
https://orcid.org/http://orcid.org/0000-0003-2626-0321
Nowadays, optimization meta-heuristic algorithms are used in different fields of science, including a computer, mechanics and civil engineering. The algorithms are inspired by the laws governing nature, such as the principle of physics, an association of animals or finding and hunting food by animals. Using the smell sense of a bee insect, called Smell Bees Optimization (SBO), the present paper proposes an optimization algorithm that is meta-heuristic and inspired by nature. To verify and validate, the proposed algorithm, benchmark functions and engineering design examples were applied, which were previously optimized using different algorithms. In doing so, such as a cantilever beam, pressure vessel, three-bar truss, tension/compression spring and a welded beam were applied, which were previously optimized using different algorithms. In order to run programming, MATLAB was used. The results obtained by SBO are compared to the previous algorithms, optimized solutions of engineering examples are improved, and the target global minimums of the standard benchmark functions are almost obtained.
Smell Bees Optimization algorithm for continuous engineering problem
https://orcid.org/http://orcid.org/0000-0002-8339-2470
https://orcid.org/http://orcid.org/0000-0003-2626-0321
Nowadays, optimization meta-heuristic algorithms are used in different fields of science, including a computer, mechanics and civil engineering. The algorithms are inspired by the laws governing nature, such as the principle of physics, an association of animals or finding and hunting food by animals. Using the smell sense of a bee insect, called Smell Bees Optimization (SBO), the present paper proposes an optimization algorithm that is meta-heuristic and inspired by nature. To verify and validate, the proposed algorithm, benchmark functions and engineering design examples were applied, which were previously optimized using different algorithms. In doing so, such as a cantilever beam, pressure vessel, three-bar truss, tension/compression spring and a welded beam were applied, which were previously optimized using different algorithms. In order to run programming, MATLAB was used. The results obtained by SBO are compared to the previous algorithms, optimized solutions of engineering examples are improved, and the target global minimums of the standard benchmark functions are almost obtained.
Smell Bees Optimization algorithm for continuous engineering problem
Asian J Civ Eng
Massoudi, Mohammad Sajjad (author) / Sarjamei, Sepehr (author) / Esfandi Sarafraz, Mehdi (author)
Asian Journal of Civil Engineering ; 21 ; 925-946
2020-09-01
22 pages
Article (Journal)
Electronic Resource
English
Finite element model updating using bees algorithm
| British Library Online Contents | 2010
| Taylor & Francis Verlag | 2017