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Biogeography-Based Optimization
Abstract
Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems. Just as the mathematics of biological genetics inspired the development of genetic algorithms (GAs), and the mathematics of biological neurons inspired the development of artificial neural networks, this paper considers the mathematics of biogeography as the basis for the development of a new field: biogeography-based optimization (BBO). We discuss natural biogeography and its mathematics, and then discuss how it can be used to solve optimization problems. We see that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO). This makes BBO applicable to many of the same types of problems that GAs and PSO are used for, namely, high-dimension problems with multiple local optima. However, BBO also has some features that are unique among biology-based optimization methods. We demonstrate the performance of BBO on a set of 14 standard benchmarks and compare it with seven other biology-based optimization algorithms. We also demonstrate BBO on a real-world sensor selection problem for aircraft engine health estimation.
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Cites
Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches
Simon, D. (2006) · doi:10.1002/0470045345 · Cited by 4,875
Self-citation
Cited by
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A novel hybrid GWO-SCA approach for optimization problems
Singh, N., & Singh, S. B. (2017) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2017.11.001 · Cited by 214
Design, optimization, modeling and testing of a piezoelectric footwear energy harvester
Qian, F., Xu, T.-B., & Zuo, L. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.06.069 · Cited by 194
Binary biogeography-based optimization based SVM-RFE for feature selection
Albashish, D., Hammouri, A. I., Braik, M., Atwan, J., & Sahran, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107026 · Cited by 154
Parameter optimization of power system stabilizer via Salp Swarm algorithm
Ekinci, S., & Hekimoglu, B. (2018) · 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE) · doi:10.1109/iceee2.2018.8391318 · Cited by 85
Metadata
- Authors
- Simon, D.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2008-12
- Volume
- 12
- Issue
- 6
- Pages
- 702-713
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2168081761
- Cited by
- 4,305
- References
- 1
- FWCI
- 73.67
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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