scholarly journals A hybrid bacterial foraging and differential evolution algorithm for congestion management

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
V. Ravikumar Pandi ◽  
Arijit Biswas ◽  
Sambarta Dasgupta ◽  
B K. Panigrahi
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Congestion Management ◽  
Bacterial Foraging ◽  
Evolution Algorithm

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

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Betania Hernández-Ocaña ◽  
Ma. Del Pilar Pozos-Parra ◽  
Efrén Mezura-Montes ◽  
Edgar Alfredo Portilla-Flores ◽  
Eduardo Vega-Alvarado ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Mechanical Design ◽  
Differential Evolution Algorithm ◽  
Fine Tuning ◽  
Local Optimum ◽  
Bacterial Foraging Optimization ◽  
Planar Mechanisms ◽  
Bacterial Foraging ◽  
Evolution Algorithm ◽  
Parameter Values

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.

Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse)

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Jia Xing Yeoh ◽  
Chuii Khim Chong ◽  
Mohd Saberi Mohamad ◽  
Yee Wen Choon ◽  
Lian En Chai ◽  
...  
Keyword(s):  
Kinetic Parameter ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Signal Transduction Pathway ◽  
Janus Kinase ◽  
Global Optimisation ◽  
Unknown Parameters ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Evolution Algorithm

The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimisation method implemented to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in Musmusculus (mouse) by using a dataset, the JAK/STAT(Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimisation is a method to identify the optimal kinetic parameter in ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters, and commonly, the parameter is in nonlinear form. Global optimisation method includes differential evolution algorithm, which will be used in this research. Kalman Filter and Bacterial Foraging algorithm helps in handling noise data and convergences faster respectively in the conventional Differential Evolution. The results from this experiment show estimated optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms. 

Modified differential evolution algorithm for semi-supervised fuzzy clustering

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO
Keyword(s):  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm
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