scholarly journals Determining of volume coefficient of gas-saturated oils

2019 ◽  
pp. 86-96 ◽  
Author(s):  
Sergey I. Perevoschikov
Keyword(s):  
Optimization Problems ◽  
Similar Technique ◽  
Oil Saturation ◽  
Hole Theory ◽  
Wide Range ◽  
Crude Oil Production ◽  
Volume Coefficient ◽  
Production Preparation ◽  
Near Abroad ◽  
Petroleum Reserves

The volumetric coefficient is widely used in various calculations related to the estimation of petroleum reserves, crude oil production, preparation and transport of oil. The existing methods of its calculation determination are mainly empirical that makes it difficult to use them in solving various tasks, primarily optimization problems. The article is devoted to the development of similar technique in the form of analytical dependence, devoid of the noted shortcomings. For this purpose, the provisions of the hole theory of the dropping liquid are used, on the basis of which a theoretically and physically justified expression acceptable for a wide range of gas-saturated oils under different conditions is obtained. The acceptability of dependence is tested on oil data from different regions of the country and near abroad. The obtained dependence makes it possible to calculate the volume coefficient at a pressure equal to the pressure of oil saturation with gas.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

An Efficient Chaotic Salp Swarm Optimization Approach Based on Ensemble Algorithm for Solving Class Imbalance Problem

2021 ◽  
Author(s):  
Rekha G ◽  
Krishna Reddy V ◽  
chandrashekar jatoth ◽  
Ugo Fiore
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Class Imbalance ◽  
Optimization Approach ◽  
Class Imbalance Problem ◽  
Swarm Optimization ◽  
Adaboost Algorithm ◽  
Wide Range

Abstract Class imbalance problems have attracted the research community but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an Adaboost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization and Adaboost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of Adaboost.

scholarly journals Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1928 ◽  
Author(s):  
Alfonso González-Briones ◽  
Fernando De La Prieta ◽  
Mohd Mohamad ◽  
Sigeru Omatu ◽  
Juan Corchado
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Energy Optimization ◽  
Sensor Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Perspectives ◽  
Wide Range ◽  
Multi Agent

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Parameter Optimization ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

scholarly journals A new entropy-variable-based discretization method for minimum entropy moment approximations of linear kinetic equations

2021 ◽  
Author(s):  
Tobias Leibner ◽  
Mario Ohlberger
Keyword(s):  
Optimization Problems ◽  
Hessian Matrix ◽  
Computational Cost ◽  
Kinetic Equations ◽  
New Method ◽  
Minimum Entropy ◽  
Reduced Basis ◽  
Simple Task ◽  
Wide Range ◽  
The Moment

In this contribution we derive and analyze a new numerical method for kinetic equations based on a variable transformation of the moment approximation. Classical minimum-entropy moment closures are a class of reduced models for kinetic equations that conserve many of the fundamental physical properties of solutions. However, their practical use is limited by their high computational cost, as an optimization problem has to be solved for every cell in the space-time grid. In addition, implementation of numerical solvers for these models is hampered by the fact that the optimization problems are only well-defined if the moment vectors stay within the realizable set. For the same reason, further reducing these models by, e.g., reduced-basis methods is not a simple task. Our new method overcomes these disadvantages of classical approaches. The transformation is performed on the semi-discretized level which makes them applicable to a wide range of kinetic schemes and replaces the nonlinear optimization problems by inversion of the positive-definite Hessian matrix. As a result, the new scheme gets rid of the realizability-related problems. Moreover, a discrete entropy law can be enforced by modifying the time stepping scheme. Our numerical experiments demonstrate that our new method is often several times faster than the standard optimization-based scheme.

scholarly journals Algorithms as Mechanisms: The Price of Anarchy of Relax and Round

2020 ◽  
Author(s):  
Paul Dütting ◽  
Thomas Kesselheim ◽  
Éva Tardos
Keyword(s):  
Optimization Problems ◽  
Price Of Anarchy ◽  
Algorithm Design ◽  
Integer Programs ◽  
Flow Problems ◽  
Pricing Rules ◽  
Wide Range ◽  
Packing Integer Programs ◽  
Unsplittable Flow ◽  
The Traveling Salesman Problem

Many algorithms that are originally designed without explicitly considering incentive properties are later combined with simple pricing rules and used as mechanisms. A key question is therefore to understand which algorithms, or, more generally, which algorithm design principles, when combined with simple payment rules such as pay your bid, yield mechanisms with a small price of anarchy. Our main result concerns mechanisms that are based on the relax-and-round paradigm. It shows that oblivious rounding schemes approximately preserve price of anarchy guarantees provable via smoothness. By virtue of our smoothness proofs, our price of anarchy bounds extend to Bayes–Nash equilibria and learning outcomes. In fact, they even apply out of equilibrium, requiring only that agents have no regret for deviations to half their value. We demonstrate the broad applicability of our main result by instantiating it for a wide range of optimization problems ranging from sparse packing integer programs, over single-source unsplittable flow problems and combinatorial auctions with fractionally subadditive valuations, to a maximization variant of the traveling salesman problem.

scholarly journals A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2147 ◽  
Author(s):  
Zhihang Yue ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Optimal Search ◽  
Fireworks Algorithm ◽  
Wide Range ◽  
Search Capability

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

scholarly journals A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem

2011 ◽  
Vol 19 (3) ◽  
pp. 345-371 ◽  
Author(s):  
Daniel Karapetyan ◽  
Gregory Gutin
Keyword(s):  
Local Search ◽  
Population Size ◽  
Assignment Problem ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Multidimensional Assignment Problem ◽  
Local Searches ◽  
Wide Range ◽  
Local Search Procedure

Memetic algorithms are known to be a powerful technique in solving hard optimization problems. To design a memetic algorithm, one needs to make a host of decisions. Selecting the population size is one of the most important among them. Most of the algorithms in the literature fix the population size to a certain constant value. This reduces the algorithm's quality since the optimal population size varies for different instances, local search procedures, and runtimes. In this paper we propose an adjustable population size. It is calculated as a function of the runtime of the whole algorithm and the average runtime of the local search for the given instance. Note that in many applications the runtime of a heuristic should be limited and, therefore, we use this bound as a parameter of the algorithm. The average runtime of the local search procedure is measured during the algorithm's run. Some coefficients which are independent of the instance and the local search are to be tuned at the design time; we provide a procedure to find these coefficients. The proposed approach was used to develop a memetic algorithm for the multidimensional assignment problem (MAP). We show that our adjustable population size makes the algorithm flexible to perform efficiently for a wide range of running times and local searches and this does not require any additional tuning of the algorithm.

MANAGEMENT OF UNCERTAINTY AND RISK IN OFFSHORE PETROLEUM DEVELOPMENT

2002 ◽  
Vol 42 (1) ◽  
pp. 113
Author(s):  
P. Behrenbruch
Keyword(s):  
Development Planning ◽  
Project Planning ◽  
Lessons Learned ◽  
Capital Expenditures ◽  
Remedial Action ◽  
Case Histories ◽  
Wide Range ◽  
Petroleum Development ◽  
Petroleum Reserves ◽  
Management Of Uncertainty

Uncertainty in petroleum development projects is most often associated with petroleum reserves. It is the limited amount of subsurface data typically available during the time of development planning that creates this situation. Risks are associated not only with reservoir uncertainty but also with wells and production facilities. Risks for offshore projects, as compared to those onshore, are further compounded by very large capital expenditures and less flexibility in catering for subsurface surprises, or remedial action in case of engineering blunders.These concepts are illustrated using case histories of successful and failed projects. Lessons learned from these and other projects are then summarised and processes for uncertainty and risk management are outlined. Risk and uncertainty cover a wide range of issues, and relate to geoscience, reservoir engineering, well technology, facilities engineering, operations, and project planning and evaluation.

Optimization of Small Wind Turbines using Genetic Algorithms

2016 ◽  
Vol 7 (4) ◽  
pp. 50-65 ◽  
Author(s):  
Mohammad Hamdan ◽  
Mohammad Hassan Abderrazzaq
Keyword(s):  
Wind Turbines ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Practical Implementation ◽  
Wind Energy Conversion ◽  
Design Alternatives ◽  
Wide Range ◽  
Small Wind Turbines ◽  
Complex Optimization ◽  
Tower Height

This paper presents a detailed optimization analysis of tower height and rotor diameter for a wide range of small wind turbines using Genetic Algorithm (GA). In comparison with classical, calculus-based optimization techniques, the GA approach is known by its reasonable flexibilities and capability to solve complex optimization problems. Here, the values of rotor diameter and tower height are considered the main parts of the Wind Energy Conversion System (WECS), which are necessary to maximize the output power. To give the current study a practical sense, a set of manufacturer's data was used for small wind turbines with different design alternatives. The specific cost and geometry of tower and rotor are selected to be the constraints in this optimization process. The results are presented for two classes of small wind turbines, namely 1.5kW and 10kW turbines. The results are analyzed for different roughness classes and for two height-wind speed relationships given by power and logarithmic laws. Finally, the results and their practical implementation are discussed.

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