Abstract
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.
Highlights
There are optimization problems considered complex due to their high dimensionality and their dynamic nature
The Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the Bacterial Foraging Algorithm (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 TSMBFOA algorithm to calibrate the algorithm parameters faster and with no need for editing lines of code
Summary
There are optimization problems considered complex due to their high dimensionality and their dynamic nature. The most recent adaptation is the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA), where two swims are added in the chemotaxis process: the first is the original swim (except for the step size, which is randomized), and the second swim includes the mutation operator used in evolutionary algorithms. Both swims are included with the objective of improving the exploration and exploitation capabilities of the algorithm [11]. Since the UI isolates the source code from the end user, he/she does not need of programming knowledge to interact directly with the calibration of the parameters and, in the execution of the algorithm
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