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

• Published in: Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 886 - 903-074
• Main Authors: Betania Hernández-Ocaña, Ma. Del Pilar Pozos-Parra, Efrén Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Maria Bárbara Calva-Yáñez
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01-01-2016; Hindawi Publishing Corporation; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Artificial Intelligence; Bacteria; Bacterial Physiological Phenomena; Biological Evolution; Chemotaxis - physiology; Computer Simulation; Design; Evolutionary algorithms; Forages; Genetic algorithms; Literature reviews; Mathematical models; Microbial colonies; Operator theory; Operators; Optimization; Swimming; Synthesis; Mexico
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2016/4525294

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.