Stochastic process and tutorial of the African bufalo optimization

• Main Authors: Odili, Julius Beneoluchi, Noraziah, Ahmad, Alkazemi, Basem Y., M., Zarina
• Format: Article
• Language: English
• Published: Nature Publishing Group 2022
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/37404/1/Stochastic%20process%20and%20tutorial%20of%20the%20African%20buffalo%20optimization.pdf; http://umpir.ump.edu.my/id/eprint/37404/; https://doi.org/10.1038/s41598-022-22242-9; https://doi.org/10.1038/s41598-022-22242-9
• Institution: Universiti Malaysia Pahang

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm’s basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms