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Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

Betania Hernández-Ocaña, Adrian García-López, José Hernández-Torruco, Oscar Chávez-Bosquez*

División Académica de Ciencias y Tecnologías de la Información, Universidad Juárez Autónoma de Tabasco, Cunduacán, Tabasco, 86690, México

* Corresponding Author: Oscar Chávez-Bosquez. Email:

(This article belongs to this Special Issue: Soft Computing Methods for Intelligent Automation Systems)

Intelligent Automation & Soft Computing 2022, 32(3), 1797-1813.


The Bacterial Foraging Algorithm (BFOA) is a well-known swarm collective intelligence algorithm used to solve a variety of constraint optimization problems with wide success. Despite its universality, implementing the BFOA may be complex due to the calibration of multiple parameters. Moreover, the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the BFOA which may lead to solutions close to the optimal but with more parameters than the original BFOA. That is why in this paper we present the design using the Unified Modeling Language (UML) and the implementation in the MATLAB platform of a front-end for the TS-MBFOA algorithm to calibrate the algorithm parameters faster and with no need for editing lines of code. To test our proposal, we solve a numerical optimization problem with constraints known as tension/compression spring, where 30 independent executions were conducted using the TS-MBFOA and then compared with an earlier version called MBFOA. The runtime configuration and the parameter tuning were fluent using our front-end, and the TS-MBFOA obtained the better results. To date, there is no other user-friendly implementation of this specific algorithm in an open-source code, and the front-end is flexible enough to include other numerical optimization problems with minimal effort.


Cite This Article

B. Hernández-Ocaña, A. García-López, J. Hernández-Torruco and O. Chávez-Bosquez, "Bacterial foraging based algorithm front-end to solve global optimization problems," Intelligent Automation & Soft Computing, vol. 32, no.3, pp. 1797–1813, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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