TY - JOUR
T1 - Two-swim operators in the modified bacterial foraging algorithm for the optimal synthesis of four-bar mechanisms
AU - Hernández-Ocaña, Betania
AU - Pozos-Parra, Ma Del Pilar
AU - Mezura-Montes, Efrén
AU - Portilla-Flores, Edgar Alfredo
AU - Vega-Alvarado, Eduardo
AU - Calva-Yáñez, Maria Bárbara
N1 - Publisher Copyright:
© 2016 Betania Hernández-Ocaña et al.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84962308804&partnerID=8YFLogxK
U2 - 10.1155/2016/4525294
DO - 10.1155/2016/4525294
M3 - Artículo
C2 - 27057156
SN - 1687-5265
VL - 2016
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 4525294
ER -