Digestion Optimization Algorithm: A Bio-inspired Intelligence Approach for Global Optimization Problems
Abstract
Digestion Optimization Algorithm is a biologically inspired metaheuristic method for solving complex optimization problems. The development of the algorithm is inspired by studying the human digestive system. The algorithm mimics the process of food ingestion, breakdown, absorption, and elimination to effectively and efficiently search for an optimal solution. This algorithm was tested for optimal solutions on seven different types of optimization benchmark functions. The algorithm produced optimal solutions with standard errors, which were compared with the exact solutions of the test functions.
References
Abdollahzadeh, B., Gharehchopogh, F., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2021.107408
Akinsunmade, A. E., & Aina, I. I. (2021). Water distribution network design using hybrid self-adaptive multi-population elitist population intelligence (HSAMPEPI) Jaya algorithm. Earthline Journal of Mathematical Sciences, 5(2), 329–343. https://doi.org/10.34198/ejms.5221.329343
Alauddin, M. (2016). Mosquito flying optimization. International Conference on Electrical, Electronics, and Optimization Techniques, 79–84. https://doi.org/10.1109/iceeot.2016.7754783
Bayraktar, Z., Komurcu, M., & Werner, D. H. (2010). Wind driven optimization: A novel nature-inspired optimization algorithm and its application to electromagnetic. Proceedings of the Antennas and Propagation Society International Symposium (APSURSI), IEEE, Toronto, Canada, 1–4. https://doi.org/10.1109/aps.2010.5562213
Dieterich, J. M. (2012). Empirical review of standard benchmark functions using evolutionary global optimization. Applied Mathematics, 3, 1552–1564. https://doi.org/10.4236/am.2012.330215
Ejieji, C. N., & Akinsunmade, A. E. (2020). Agricultural model for allocation of crops using pollination intelligence method. Applied Computational Intelligence and Soft Computing, Article ID 4830359, 6 pp. https://doi.org/10.1155/2020/4830359
Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. (2020). Marine predators algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377. https://doi.org/10.1016/j.eswa.2020.113377
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA.
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI.
Jamil, M., & Yang, X. S. (2013). A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4, 150–194. https://doi.org/10.1504/ijmmno.2013.055204
Ketel, E. C., Aguayo-Mendoza, M. G., De Wijk, R. A., De Graaf, C., Piqueras-Fiszman, B., & Stieger, M. (2019). Age, gender, ethnicity and eating capability influence oral processing behaviour of liquid, semi-solid and solid foods differently. Food Research International, 119, 143–151. https://doi.org/10.1016/j.foodres.2019.01.048
Kirkpatrick, S., Gelatt, J., & Vecchi, M. (1983). Optimization by simulated annealing. Science, 220(4798), 671–680. https://doi.org/10.1126/science.220.4598.671
Sensoy, I. (2021). A review on the food digestion in the digestive tract and the used in vitro models. Current Research in Food Science, 4, 308–319. https://doi.org/10.1016/j.crfs.2021.04.004
Shi, Y. (2011). An optimization algorithm based on brainstorming process. International Journal of Swarm Intelligence Research, 2(4), 35–62. https://doi.org/10.4018/jsir.2011100103
Storn, R., & Price, K. (1997). Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359. https://doi.org/10.1023/a:1008202821328
Yang, X. S. (2008). Nature-inspired metaheuristic algorithms. Luniver Press, Cambridge, United Kingdom.
Yang, X. S. (2012). Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation, 7445, 240–249. https://doi.org/10.1007/978-3-642-32894-7_27
Fu, Y., Liu, D., Chen, J., & He, L. (2024). Secretary bird optimization algorithm: A new metaheuristic for solving global optimization problems. Artificial Intelligence Review, 57, Article No. 123. https://doi.org/10.1007/s10462-024-10729-y
Zhao, S., Zhang, T., Ma, S., & Wang, M. (2023). Sea-horse optimizer: A novel nature-inspired meta-heuristic for global optimization problems. Applied Intelligence, 53, 11833–11860. https://doi.org/10.1007/s10489-022-03994-3
This work is licensed under a Creative Commons Attribution 4.0 International License.
.jpg){kind=logo}
