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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 4525294, 18 pages
Research Article

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

1División Académica de Informática y Sistemas, Universidad Juárez Autónoma de Tabasco, 86690 Cunduacán, TAB, Mexico
2Centro de Investigación en Inteligencia Artificial, Universidad Veracruzana, Sebastián Camacho 5, Centro, 91000 Xalapa, VER, Mexico
3Instituto Politécnico Nacional (IPN-CIDETEC), U. Adolfo López Mateos, 07700 Ciudad de México, DF, Mexico

Received 9 November 2015; Revised 11 January 2016; Accepted 12 January 2016

Academic Editor: Saeid Sanei

Copyright © 2016 Betania Hernández-Ocaña et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.