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

被引:15
作者
Hernandez-Ocana, Betania [1 ]
Del Pilar Pozos-Parra, Ma. [1 ]
Mezura-Montes, Efren [2 ]
Alfredo Portilla-Flores, Edgar [3 ]
Vega-Alvarado, Eduardo [3 ]
Babara Calva-Yanez, Maria [3 ]
机构
[1] Univ Juarez Autonoma Tabasco, Div Acad Informat & Sistemas, Cunduacan 86690, Tab, Mexico
[2] Univ Veracruzana, Ctr Invest Inteligencia Artificial, Sebastian Camacho 5, Xalapa 91000, Veracruz, Mexico
[3] IPN, CIDETEC, Mexico City 07700, DF, Mexico
关键词
DIFFERENTIAL EVOLUTION; OPTIMIZATION; SYNERGY;
D O I
10.1155/2016/4525294
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:18
相关论文
共 45 条
[21]  
Huang HC., 2010, J INF HIDING MULTIME, V1, P51
[22]   Spiral bacterial foraging optimization method: Algorithm, evaluation and convergence analysis [J].
Kasaiezadeh, Alireza ;
Khajepour, Amir ;
Waslander, Steven L. .
ENGINEERING OPTIMIZATION, 2014, 46 (04) :439-464
[23]  
Kennedy J., 1995, 1995 IEEE International Conference on Neural Networks Proceedings (Cat. No.95CH35828), P1942, DOI 10.1109/ICNN.1995.488968
[24]  
Kim DH, 2006, 2006 INTERNATIONAL CONFERENCE ON HYBRID INFORMATION TECHNOLOGY, VOL 1, PROCEEDINGS, P293
[25]   A hybrid genetic algorithm and bacterial foraging approach for global optimization [J].
Kim, Dong Hwa ;
Abraham, Ajith ;
Cho, Jae Hoon .
INFORMATION SCIENCES, 2007, 177 (18) :3918-3937
[26]  
Korani W., 2008, GECCO 08 P GEN EV CO, P1823, DOI [10.1145/1388969.1388980, DOI 10.1145/1388969.1388980]
[27]   Constrained real-parameter optimization with generalized differential evolution [J].
Kukkonen, Saku ;
Lampinen, Jouni .
2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, :207-+
[28]  
Kushwaha N., 2012, IJCA P NAT C FUT ASP, P11
[29]  
Martínez-Alfaro H, 2008, NAT COMPUT SER, P23, DOI 10.1007/978-3-540-72960-0_2
[30]  
Mezura-Montes E., 2009, Proc. Artif. Neural Netw. Eng. Confer, V19, P357, DOI [10.1115/1.802953.paper45, DOI 10.1115/1.802953.PAPER45]