Application of Capacitance-Resistive Model With Global Optimizer

2021 ◽  
Author(s):  
A. A. Naufal
Keyword(s):  
History Matching ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Computation Time ◽  
Dynamic Synthesis ◽  
Global Optimizer ◽  
Evolution Algorithm ◽  
Resistive Model ◽  
Control Volumes ◽  
Time Plot

The need to do a history matching in a deltaic environment with a total of 550 compartmentalized channel reservoirs of Field X brings such heavy challenges in terms of time consumed and uncertainties present. The capacitance-resistive model (CRM) rooted in signal processing between the injection and production rate was chosen to determine connectivity between injectors and producers (f_ij) and flood efficiencies for portions of the field (f_F). These constants become key insights for validating the dynamic synthesis of the reservoirs. CRM relies solely upon production and injection data. Two different control-volumes for CRM, CRMT and CRMP, were solved using a global non-deterministic solver which elevate differential evolution algorithm. The parameters’ solved was then validated with the observed liquid rate of the wells. Several techniques such as using system-wide R2 as the objective function, removal of inactive wells and distance-based weighting were used to improve the validation of the proxy model. The methods were applied to validate analyzed reservoirs with divided regions based on earlier analysis. A first CRM run was presented in this paper to test the algorithm prepared. Then another CRM run were demonstrated in this paper to show how they confirm the compartmentalization within a reservoir when compared to the reservoir’s pressure-over-time plot and earlier manual production-injection data analysis. This paper exemplified the strength of CRM itself which is to describe large-scale system in a way that circumvents geologic modeling and saturation matching with short to moderate computation time, as well as improvements applied to help the optimization process.

Phylogenetic Differential Evolution

2014 ◽  
pp. 22-40 ◽  
Author(s):  
Vinícius Veloso de Melo ◽  
Danilo Vasconcellos Vargas ◽  
Marcio Kassouf Crocomo
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Building Blocks ◽  
Large Scale Systems ◽  
Estimation Of Distribution ◽  
Evolution Algorithm ◽  
A New Technique ◽  
Distribution Algorithms ◽  
Binary Problems

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

Intrusion Detection Based on Dynamic Gemini Population DE-K-mediods Clustering on Hadoop Platform

2020 ◽  
pp. 2150001
Author(s):  
Wentie Wu ◽  
Shengchao Xu
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Differential Evolution ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection System ◽  
Differential Evolution Algorithm ◽  
Detection Algorithm ◽  
Local Optimum ◽  
Evolution Algorithm

In view of the fact that the existing intrusion detection system (IDS) based on clustering algorithm cannot adapt to the large-scale growth of system logs, a K-mediods clustering intrusion detection algorithm based on differential evolution suitable for cloud computing environment is proposed. First, the differential evolution algorithm is combined with the K-mediods clustering algorithm in order to use the powerful global search capability of the differential evolution algorithm to improve the convergence efficiency of large-scale data sample clustering. Second, in order to further improve the optimization ability of clustering, a dynamic Gemini population scheme was adopted to improve the differential evolution algorithm, thereby maintaining the diversity of the population while improving the problem of being easily trapped into a local optimum. Finally, in the intrusion detection processing of big data, the optimized clustering algorithm is designed in parallel under the Hadoop Map Reduce framework. Simulation experiments were performed in the open source cloud computing framework Hadoop cluster environment. Experimental results show that the overall detection effect of the proposed algorithm is significantly better than the existing intrusion detection algorithms.

COMPUTING TWO LINCHPINS OF TOPOLOGICAL DEGREE BY A NOVEL DIFFERENTIAL EVOLUTION ALGORITHM

2005 ◽  
Vol 05 (03) ◽  
pp. 335-350 ◽  
Author(s):  
FEI GAO ◽  
HENG-QING TONG
Keyword(s):  
Differential Evolution ◽  
Topological Degree ◽  
Large Scale ◽  
Uniform Design ◽  
Lipschitz Constant ◽  
Differential Evolution Algorithm ◽  
Initial Point ◽  
Benchmark Problems ◽  
Initial Population ◽  
Evolution Algorithm

How to detect the topological degree (TD) of a function is of vital importance in investigating the existence and the number of zero values in the function, which is a topic of major significance in the theory of nonlinear scientific fields. Usually a sufficient refinement of the boundary of the polyhedron decided by Boult and Sikorski algorithm (BS) is needed as prerequisite when the well known method of Stenger and Kearfott is chosen for computing TD. However two linchpins are indispensable to BS, the parameter δ on the boundary of the polyhedron and an estimation of the Lipschitz constant K of the function, whose computations are analytically difficult. In this paper, through an appropriate scheme that transforms the problems of computing δ and K into searching optimums of two non-differentiable functions, a novel differential evolution algorithm (DE) combined with established techniques is proposed as an alternative method to computing δ and K. Firstly it uses uniform design method to generate the initial population in feasible field so as to have the property of large scale convergence, without better approximation of the unknown parameter as iterative initial point. Secondly, it restrains the normal DE's local convergence limitation virtually through deflection and stretching of objective function. The main advantages of the put algorithm are its simplicity and its ability to work by using function values solely. Finally, details of applying the proposed method into computing δ and K are given, and experimental results on two benchmark problems in contrast to the results reported have demonstrated the promising performance of the proposed algorithm in different scenarios.

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