Comparison between Genetic Algorithms and Differential Evolution for Solving the History Matching Problem

Amorim, Elisa Portes dos Santos; Xavier, Carolina Ribeiro; Campos, Ricardo Silva; Santos, Rodrigo Weber dos

doi:10.1007/978-3-642-31125-3_48

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…We will use a differential evolution (DE) algorithm to achieve Multi-Objectives optimization of cost, response time and SLA violation. Using a DE algorithm will provide better outcomes than the genetic algorithm (GA) (CDOXplorer) [5] that used the same CloudMIG tool of cloud migration optimization for many reasons; DE is more efficient than GA in exploring the decision space of Multi-Objectives optimization [34] , [35], and DE offers more accuracy and stability and better convergence speed than GA for solving various optimization problems of applications [36], [37].…”

Section: The Proposed Frameworkmentioning

confidence: 98%

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Abdelmaboud

Jawawi

Ghani

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. Cloud computing has become more attractive for consumers to migrate their applications to the cloud environment. However, because of huge cloud environments, application customers and providers face the problem of how to assess and make decisions to choose appropriate service providers for migrating their applications to the cloud. Many approaches have investigated how to address this problem. In this paper we classify these approaches into non-evolutionary cloud migration optimization approaches and evolutionary cloud migration optimization approaches. Criteria including cost, QoS, elasticity and degree of migration optimization have been used to compare the approaches. Analysis of the results of comparative evaluations shows that a Multi-Objectives optimization approach provides a better solution to support decision making to migrate an application to the cloud environment based on the significant proposed criteria. The classification of the investigated approaches will help practitioners and researchers to deliver and build solid approaches.

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Section: The Proposed Frameworkmentioning

confidence: 98%

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Abdelmaboud

Jawawi

Ghani

et al. 2014

Advances in Intelligent Systems and Computing

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“…DE performs the mutation by creating a mutant vector of three randomly selected vectors and performs the crossover by creating a trial vector of the mutant vector and target vector [118]. Then, the fitness of the trial and target vectors is evaluated, and the best is kept for the next generation [119]. In DE, the selection of the parent solution is not based on fitness.…”

Section: Optimizationmentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

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“…It has some distinguishing properties like simple structure, speed, ease of use, and robustness [34]. Differential evolution mainly relies on mutation while GAs rely on both crossover and mutation [35,36].…”

Section: Problem Formulationmentioning

confidence: 99%

Between-host HIV model: stability analysis and solution using memetic computing

Ahmed¹,

Qureshi²,

Saleem³

et al. 2019

Turk J Elec Eng & Comp Sci

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HIV poses a great threat to humanity for two major reasons. First it attacks the immunity system of the body and second, it is epidemic in nature. Mathematical models of HIV have been instrumental in understanding and controlling the infection. In this paper, we solve the between host epidemic model of HIV, described by nonlinear coupled differential equations, by using memetic computing. Under this model, the sexually active population is divided into four classes and we investigate the transfer of individuals from one class to another. The solution consists of Bernstein polynomials whose parameters have been optimized by using differential evolution as global and sequential quadratic programming as local optimizer. Our second contribution is the stability analysis of this model. The disease-free equilibrium is stable while endemic equilibrium is unstable within the practical range of the values of parameters.

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Comparison between Genetic Algorithms and Differential Evolution for Solving the History Matching Problem

Cited by 11 publications

References 7 publications

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Between-host HIV model: stability analysis and solution using memetic computing

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