Improved Modified Bacterial Foraging Optimization Algorithm to Solve Constrained Numerical Optimization Problems

Hernández-Ocaña, Betania; Pozos-Parra, Ma. Del Pilar; Mezura-Montes, Efrén

doi:10.18576/amis/100220

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…The user-defined parameter of TS-MBFOA is shown in Table 4 . The parameter values for MBFOA and IMBFOA were taken from [ 29 ] and [ 32 ], respectively.…”

Section: Results and Analysismentioning

confidence: 99%

“…TS-MBFOA is inspired by the ideas of IMBFOA, a recently proposed BFOA-based algorithm to solve CNOPs by using two-swim operators, a skew mechanism for the initial swarm of bacteria, a second-order local search operator, and a limited usage of the reproduction step [ 32 ]. To get a self-contained paper, in the next subsections, MBFOA, IMBFOA's base algorithm, is presented.…”

Section: Two-swim Modified Bacterial Foraging Optimization Algoritmentioning

confidence: 99%

“…Further modifications were proposed in [ 30 , 31 ] to solve multiobjective CNOPs, where some four-bar mechanisms were tackled. More recently, MBFOA was further improved to solve CNOPs in the so-called Improved MBFOA (IMBFOA for short) [ 32 ] where the idea of two-swim movements within the chemotaxis process was initially explored. However, IMBFOA heavily depends on a local search based on sequential quadratic programming.…”

Section: Introductionmentioning

confidence: 99%

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Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

Hernández-Ocaña

Pozos-Parra

Mezura-Montes

et al. 2016

Computational Intelligence and Neuroscience

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This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

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“…The user-defined parameter of TS-MBFOA is shown in Table 4 . The parameter values for MBFOA and IMBFOA were taken from [ 29 ] and [ 32 ], respectively.…”

Section: Results and Analysismentioning

confidence: 99%

Section: Two-swim Modified Bacterial Foraging Optimization Algoritmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

Hernández-Ocaña

Pozos-Parra

Mezura-Montes

et al. 2016

Computational Intelligence and Neuroscience

Self Cite

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“…IMBFOA implements a skew mechanism to create the initial population, two swim operators, dynamic step sizes, and the Sequential Quadratic Programming (SQP) local search engine. This search engine aims to bring or introduce bacteria into the feasible region or take bacteria out of a local optimum and move them to another place within the feasible region [10].…”

Section: Bacterial Foraging Algorithm (Bfoa)mentioning

confidence: 99%

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

Hern醤dez-Oca馻¹,

Garc韆-L髉ez²,

Jos�Hern醤dez-Torruco³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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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.

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“…It is noted that swarming is an optional mechanism. Some studies do not consider the swarming operation to keep the algorithm computationally simple without seriously impacting the solution accuracy [9,28].…”

Section: Swarmingmentioning

confidence: 99%

Stability analysis of chemotaxis dynamics in bacterial foraging optimization over multi-dimensional objective functions

Yang

2019

Soft Comput

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Bacterial foraging optimization (BFO) has been proved to be an efficient optimization method and successfully applied to a variety of fields in the real world. In BFO, the chemotaxis process is a complex and close combination of swimming and tumbling, and plays a crucial role in searching better solutions. A previous study has modeled the dynamics of the chemotaxis mechanism mathematically, and investigated the stability and convergence behavior of the chemotaxis dynamics over the one-dimensional objective function by Lyapunov stability theorem. However, this study appears to be very limited from a practical point of view, and how to extend their study to the multidimensional objective function is a challenge. To solve it, we present a stability analysis of chemotaxis dynamics in BFO over the multi-dimensional objective function in this paper. First, the general mathematical model of the chemotaxis mechanism over the multi-dimensional objective function is created. Secondly, this paper uses the general descent search to analyze the general mathematical model, and points out two necessary conditions for avoiding the bacterium to trap into a non-optimal solution. And then, the stability and convergence of the chemotaxis dynamics, represented by the general mathematical model, is proved by using Lyapunov stability theorem. Finally, empirical research is conducted to validate the above theoretical analysis.

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Improved Modified Bacterial Foraging Optimization Algorithm to Solve Constrained Numerical Optimization Problems

Cited by 6 publications

References 26 publications

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

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

Stability analysis of chemotaxis dynamics in bacterial foraging optimization over multi-dimensional objective functions

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