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. 

A novel method of global optimisation for wavefront shaping based on the differential evolution algorithm

2021 ◽  
Vol 481 ◽  
pp. 126541
Author(s):  
Yingzi Hua ◽  
Xiubao Sui ◽  
Shenghang Zhou ◽  
Qian Chen ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Global Optimisation ◽  
Novel Method ◽  
Wavefront Shaping ◽  
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.

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

Parameter Estimation By Using An Improved Bee Memory Differential Evolution Algorithm (Ibmde) To Simulate Biochemical Pathways

2013 ◽  
Vol 8 (999) ◽  
pp. 1-6
Author(s):  
Chuii Khim Chong ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Mohd Shahir Shamsir ◽  
Lian En Chai ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Biochemical Pathways ◽  
Evolution Algorithm

The Economic Dispatch of Wind Integrated Power System based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 13 (3) ◽  
pp. 384-395
Author(s):  
Haiqing Liu ◽  
Jinmeng Qu ◽  
Yuancheng Li
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Bee Colony ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch ◽  
Integrated Power System

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

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