Hybrid differential evolution with geometric mean mutation in parameter estimation of bioreaction systems with large parameter search space

2009 ◽  
Vol 33 (11) ◽  
pp. 1851-1860 ◽  
Author(s):  
Pang-Kai Liu ◽  
Feng-Sheng Wang
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Geometric Mean ◽  
Search Space ◽  
Large Parameter ◽  
Parameter Search ◽  
Parameter Search Space

scholarly journals Search Space Reduction for Determination of Earthquake Source Parameters Using PCA and k-Means Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Seongjae Lee ◽  
Taehyoun Kim
Keyword(s):  
Monte Carlo ◽  
Surface Deformation ◽  
Source Parameters ◽  
Search Space ◽  
Dislocation Model ◽  
Space Reduction ◽  
Parameter Search ◽  
Search Space Reduction ◽  
Data Points ◽  
Parameter Search Space

The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured and the synthetic displacement calculated from a dislocation model. Estimating source geometries in a dislocation model has been regarded as solving a nonlinear inverse problem. To avoid local minima and describe uncertainties, the Monte-Carlo restarts are often used to solve the problem, assuming the initial parameter search space provided by seismological studies. Since search space size significantly affects the accuracy and execution time of this procedure, faulty initial search space from seismological studies may adversely affect the accuracy of the results and the computation time. Besides, many source parameters describing physical faults lead to bad data visualization. In this paper, we propose a new machine learning-based search space reduction algorithm to overcome these challenges. This paper assumes a rectangular dislocation model, i.e., the Okada model, to calculate the surface deformation mathematically. As for the geodetic measurement of three-dimensional (3D) surface deformation, we used the stacking interferometric synthetic aperture radar (InSAR) and the multiple-aperture SAR interferometry (MAI). We define a wide initial search space and perform the Monte-Carlo restarts to collect the data points with root-mean-square error (RMSE) between measured and modeled displacement. Then, the principal component analysis (PCA) and the k-means clustering are used to project data points with low RMSE in the 2D latent space preserving the variance of original data as much as possible and extract k clusters of data with similar locations and RMSE to each other. Finally, we reduce the parameter search space using the cluster with the lowest mean RMSE. The evaluation results illustrate that our approach achieves 55.1~98.1% reductions in search space size and 60~80.5% reductions in 95% confidence interval size for all source parameters compared with the conventional method. It was also observed that the reduced search space significantly saves the computational burden of solving the nonlinear least square problem.

Determination of wavefront attributes by differential evolution in the presence of conflicting dips

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V229-V239 ◽  
Author(s):  
Jan Walda ◽  
Dirk Gajewski
Keyword(s):  
Differential Evolution ◽  
Model Building ◽  
Optimization Technique ◽  
Velocity Model ◽  
Search Space ◽  
Global Maximum ◽  
Local Maxima ◽  
Parameter Search ◽  
Common Reflection Surface ◽  
And Migration

The common-reflection surface (CRS) method represents a multidimensional stacking approach; i.e., the stacking surface is determined in the midpoint and offset directions. In the 2D case, three attributes span the stacking surface, thus requiring a three-parameter search contrary to a one-parameter search in the classic common-midpoint stack. However, CRS wavefront attributes use data redundancy in the midpoint direction as well, which makes them very useful in several seismic applications, e.g., data preconditioning, velocity model building, and migration. Contrary to previous works, we simultaneously estimate CRS attributes using differential evolution in subcubes of the 3D search space. Differential evolution is a global optimization technique that performs particularly well when the objective function is unknown. Because we apply DE for each subcube, we could find local maxima, additionally to the global maximum. Therefore, conflicting dips are recognized and can be used for the stack and subsequent CRS attribute-based processing, which has been an issue in the past. Our land data results from the Donbas Foldbelt in southeast Ukraine demonstrate that our method reduces coherent steep dipping noise and reveal more subsurface structures. Application of the CRS attributes for prestack data enhancement shows that velocity analysis can be carried out more reliably.

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

scholarly journals Digital nanophotonics: the highway to the integration of subwavelength-scale photonics

Nanophotonics ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Jie Huang ◽  
Hansi Ma ◽  
Dingbo Chen ◽  
Huan Yuan ◽  
Jinping Zhang ◽  
...  
Keyword(s):  
Rapid Development ◽  
Search Space ◽  
Inverse Design ◽  
Analytical Models ◽  
Nanophotonic Devices ◽  
Single Function ◽  
Intelligence Operations ◽  
Complex Functions ◽  
Parameter Search Space ◽  
Design Algorithms

AbstractNanophotonic devices with high densities are extremely attractive because they can potentially merge photonics and electronics at the nanoscale. However, traditional integrated photonic circuits are designed primarily by manually selecting parameters or employing semi-analytical models. Limited by the small parameter search space, the designed nanophotonic devices generally have a single function, and the footprints reach hundreds of microns. Recently, novel ultra-compact nanophotonic devices with digital structures were proposed. By applying inverse design algorithms, which can search the full parameter space, the proposed devices show extremely compact footprints of a few microns. The results from many groups imply that digital nanophotonics can achieve not only ultra-compact single-function devices but also miniaturized multi-function devices and complex functions such as artificial intelligence operations at the nanoscale. Furthermore, to balance the performance and fabrication tolerances of such devices, researchers have developed various solutions, such as adding regularization constraints to digital structures. We believe that with the rapid development of inverse design algorithms and continuous improvements to the nanofabrication process, digital nanophotonics will play a key role in promoting the performance of nanophotonic integration. In this review, we uncover the exciting developments and challenges in this field, analyse and explore potential solutions to these challenges and provide comments on future directions in this field.

scholarly journals Differential Evolution with Population and Strategy Parameter Adaptation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
V. Gonuguntla ◽  
R. Mallipeddi ◽  
Kalyana C. Veluvolu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Search Space ◽  
Benchmark Problems ◽  
Parameter Adaptation ◽  
De Algorithm ◽  
Probabilistic Technique ◽  
Number Of Individuals ◽  
Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

scholarly journals Convergence Analysis of Particle Swarm Optimizer and Its Improved Algorithm Based on Velocity Differential Evolution

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hongtao Ye ◽  
Wenguang Luo ◽  
Zhenqiang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Velocity ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Relationship Of ◽  
The Relationship ◽  
Improved Algorithm

This paper presents an analysis of the relationship of particle velocity and convergence of the particle swarm optimization. Its premature convergence is due to the decrease of particle velocity in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. An improved algorithm which introduces a velocity differential evolution (DE) strategy for the hierarchical particle swarm optimization (H-PSO) is proposed to improve its performance. The DE is employed to regulate the particle velocity rather than the traditional particle position in case that the optimal result has not improved after several iterations. The benchmark functions will be illustrated to demonstrate the effectiveness of the proposed method.

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