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Framework for field optimization to maximize asset value

SPE RESERVOIR EVALUATION & ENGINEERING 2005年第1期8卷 7-21页

作者： Bailey, WJ Couët, B Wilkinson, D Schlumberger Doll Res Ctr Risk & Optimizat Program Ridgefield CT USA

In this paper, we propose an optimization framework for maximizing asset value, both with and without uncertainty. We first present the methodology to treat a general control optimization in the presence of uncertainty, followed by a brief section on the optimization algorithms used. We then describe the field model example used to illustrate the application of the methodology. Through a systematic analysis of various deterministic and stochastic cases, we address the various objectives sought. Using net present value (NPV) as a measure, we also explore the valuation of advanced completions along with the returns gained from expanding surface gas-handling facilities. The method also generates an efficient frontier that can be used for risk and decision analysis. The results clearly demonstrate the value of such a framework for value maximization in planning both near- and long-term time horizons as well as providing the necessary foundation for maximizing asset value.

关键词： cost accounting Frameworks deterministic Maximize Completion optimization algorithms System analyses Uncertainty Gas surfacing control optimization Decision analysis

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Simplified Design, optimization and Comparative Study of SPM, BLDC and Halbach Machines for Microsatellite Electro-Mechanical Batteries

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2015年第1期51卷 31-39页

作者： Abdi, B. Milimonfared, J. Moghani, J. S. Islamic Azad Univ Damavand Branch Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

Electro-mechanical batteries (EMBs) have many important advantages compared with chemical batteries, especially in low Earth orbit (LEO) satellite applications. Different slotless, external rotor, permanent magnet (PM) machines are suggested and used in these systems as motor/generator. Synchronous permanent magnet (SPM), brushless dc (BLDC) and Halbach (HB) machines are three types that are most commonly used. In this paper, the best machine will be introduced using a comparative study. Proper material and structure for space applications are introduced and simplified. The analytic design methods are given for three types of machines. Then, all three types of machines are optimized in order to have a fair comparison. Particle swarm optimization (PSO) is used as the optimization algorithm and the finite element-based simulations are used to confirm the design and optimization process. Finally, the best machine is introduced based on the important parameters in a microsatellite.

关键词： BLDC Optimal design optimization Procedure Batteries electric machines Space applications machinery machine optimization algorithms Microsatellite Repeats Scanning Probe Microscopy

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optimization-Based Fuzzy System Application on Deformation of Geogrid-Reinforced Soil Structures

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-21页

作者： Dou, Huiru Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Hubei Peoples R China

When it comes to the geosynthetic-reinforced soil structures' structural design, the analysis of deformation is of the highest relevance. In spite of this, the academic literature lays a substantial amount of attention on the potential of artificial intelligence approaches in efficiently addressing the many challenges that are faced in geotechnical engineering. The investigation of the possible use of approaches based on machine learning for the purpose of forecasting the geogrid-reinforced soil structures' deformation ( Dis \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Dis}}$$\end{document} ) was the major focus of this study. These efforts are made to reduce the time and cost of numerical modeling. This study aimed to enable computers to learn patterns and insights from data to make accurate predictions or decisions about unseen data. This paper introduces novel systems that coupled the Beluga whale optimizer ( BWH \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{BWH}}$$\end{document} ), Henry gas solubility optimization ( HGSO \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{HGSO}}$$\end{document} ), gannet optimization algorithm ( GOA \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{GOA}}$$\end{document} ), and Harris haw

关键词： Geosynthetic-reinforced soil Geogrid Deformation Fuzzy system optimization algorithms

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A coral-reef optimization algorithm for the optimal service distribution problem in mobile radio access networks

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2014年第11期25卷 1057-1069页

作者： Salcedo-Sanz, S. Sanchez-Garcia, J. E. Portilla-Figueras, J. A. Jimenez-Fernandez, S. Ahmadzadeh, A. M. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain WIK Consult GmbH Dept Cost Modeling & Internet Econ D-53604 Bad Honnef Germany

Mobile technology is currently one of the main pillars of worldwide economy. The constant evolution that mobile communications have undergone in the last decades, due to the appearance of new services and new technologies such as Universal Mobile Telecommunication Systems/High Speed Data Access and Long Term Evolution, has contributed to achieve this position in global economy. However, because of the crisis of the sector in the last 5years, mobile operator's revenues and investments have been reduced. Thus, mobile network operators tend to exploit the existing infrastructure at maximum possible, trying to use the existing network in the most efficient way. In this paper, a novel bio-inspired algorithm, the coral reef optimization algorithm (CRO) is introduced to minimise a network deployment investment cost problem. This is carried out by means of optimising the user demand of different services offered by mobile operators over the available technologies in the market, namely the optimal service distribution problem. The CRO is a recently proposed meta-heuristic based on the computer simulation of corals reproduction and reefs' formation. In this paper, this algorithm has been tested on several optimal service distribution problem scenarios in Spain, observing a significant reduction (up to 400 MEuro) on the total investment costs associated to the radio access network deployment. We compare the performance of the CRO approach with that of a classical (experience-based) services distribution, and with alternative meta-heuristics techniques, obtaining good results in all cases. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： optimization algorithms mobile communication Mobile radios Mobile Operators Investments Mobile Technologies Wireless networks Cathode ray oscilloscopes access network Long Term Evolution

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Chaotic antlion algorithm for parameter optimization of support vector machine

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APPLIED INTELLIGENCE 2018年第3期48卷 670-686页

作者： Tharwat, Alaa Hassanien, Aboul Ella Frankfurt Univ Appl Sci Fac Comp Sci & Engn Frankfurt Germany Cairo Univ Fac Comp & Informat Giza Egypt SRGE Cairo Egypt

Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel parameters and penalty parameter (C) significantly influence the classification accuracy. In this paper, a novel Chaotic Antlion optimization (CALO) algorithm has been proposed to optimize the parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed algorithm (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the CALO-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, standard Ant Lion optimization (ALO) SVM, and three well-known optimization algorithms: Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Social Emotional optimization Algorithm (SEOA). The experimental results proved that the proposed algorithm is capable of finding the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with GA, PSO, and SEOA algorithms.

关键词： optimization algorithms Support vector machine (SVM) Chaotic maps Classification Parameter optimization Ant lion optimizer (ALO)

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Proposal and validation of an optimization method using Monte Carlo method for multi-objective functions

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 215卷 146-157页

作者： Inage, Sin-ichi Ohgi, Shouki Takahashi, Yoshinori Fukuoka Univ Dept Mech Engn Fluid Engn Lab Fukuoka 8140180 Japan Fukuoka Univ Dept Mech Engn 8-19-1 NanakumaJonan Ku Fukuoka 8140180 Japan

Solving optimization problems is essential for many engineering applications and research tools. In a previous report, we proposed a new optimization method, MOST (Monte Carlo Stochastic optimization), using the Monte Carlo method, and applied it to benchmark problems for optimization methods, and optimization of neural network machine learning. While the proposed method MOST was a single objective, this study is an extension of MOST so that it can be applied to multi-objective functions for the purpose of improving generality. As the verification, it was applied to the optimization problem with the restriction condition first, and it was also applied to the benchmark problem for the multi-objective optimization technique verification, and the validity was confirmed. For comparison, the calculation by genetic algorithm was also carried out, and it was confirmed that MOST was excellent in calculation accuracy and calculation time.& COPY;2023 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： optimization algorithms Muti-objective function Monte Carlo method Genetic algorithm

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Layout and design optimization of ocean wave energy converters: A scoping review of state-of-the-art canonical, hybrid, cooperative, and combinatorial optimization methods

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ENERGY REPORTS 2022年 8卷 15446-15479页

作者： Golbaz, Danial Asadi, Rojin Amini, Erfan Mehdipour, Hossein Nasiri, Mahdieh Etaati, Bahareh Naeeni, Seyed Taghi Omid Neshat, Mehdi Mirjalili, Seyedali Gandomi, Amir H. Univ Delaware Ctr Appl Coastal Res Civil & Environm Engn Newark DE USA Univ Tehran Sch Civil Engn Coll Engn Tehran 13145 Iran Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA Stevens Inst Technol Dept Mech Engn Hoboken NJ 07030 USA Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

Ocean Wave energy is becoming a prominent technology, which is considered a vital renewable energy resource to achieve the Net-zero Emissions Plan by 2050. It is also projected to be commercialized widely and become a part of the industry that alters conventional energy technologies in the near future. However, wave energy technologies are not entirely yet developed and mature enough, so various criteria must be optimized to enter the energy market. In order to maximize the performance of wave energy converters (WECs) components, three challenges are mostly considered: Geometry, Power Take-off (PTO) parameters, and WECs' layout. As each of such challenges plays a meaningful role in harnessing the maximum power output, this paper systematically reviews applied state-of-the-art optimization techniques, including standard, hybrid, cooperative, bi-level and combinatorial strategies. Due to the importance of fidelity and computational cost in numerical methods, we also discuss approaches to analyzing WECs interactions' developments. Moreover, the benefits and drawbacks of the popular optimization methods applied to improve WEC parameters' performance are summarized, briefly discussing their key characteristics. According to the scoping review, using a combination of bio-inspired algorithms and local search as a hybrid algorithm can outperform the other techniques in layout optimization in terms of convergence rate. A review of the geometry of WECs has emphasized the indispensability of optimizing and balancing design parameters with cost issues in multimodal and large-scale problems. (c) 2022 The Authors. Published by Elsevier Ltd.

关键词： Wave Energy Converters Layout optimization PTO systems Geometry design optimization algorithms Evolutionary algorithms Swarm intelligence Local search methods

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Combinatorial optimization Solutions for the Maximum Quartet Consistency Problem

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FUNDAMENTA INFORMATICAE 2010年第3-4期102卷 363-389页

作者： Morgado, Antonio Marques-Silva, Joao Univ Coll Dublin Complex & Adapt Syst Lab Sch Comp Sci & Informat Dublin Ireland

Phylogenetic analysis is a widely used technique, for example in biology and biomedical sciences. The construction of phylogenies can be computationally hard. A commonly used solution for construction of phylogenies is to start from a set of biological species and relations among those species. This work addresses the case where the relations among species are specified as quartet topologies. Moreover, the problem to be solved consists of computing a phylogeny that satisfies the maximum number of quartet topologies. This is referred to as the Maximum Quartet Consistency (MQC) problem, and represents an NP-hard optimization problem. MQC has been solved both heuristically and exactly. Exact solutions forMQC include those based on Constraint Programming, Answer Set Programming, Pseudo-Booleanoptimization (PBO), and Satisfiability Modulo Theories (SMT). This paper provides a comprehensive overview of the use of PBO and SMT for solving MQC, and builds on recent work in this area. Moreover, the paper provides new insights on how to use SMT for solving optimization problems, by focusing on the concrete case of MQC. The solutions based on PBO and SMT were experimentally compared with other exact solutions. The results show that for instances with small percentage of quartet errors, the models based on SMT can be competitive, whereas for instances with higher number of quartet errors the PBO models are more efficient.

关键词： maximum quartet consistency optimization algorithms pseudo-boolean optimization satisfiability modulo theories

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optimization of broadband withdrawal weighted interdigital transducers for high selective SAW filters

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 1999年第5期46卷 1276-1282页

作者： Bausk, EV Russian Acad Sci Inst Semicond Phys Novosibirsk 630090 Russia

The problem under discussion is the design of highly selective broadband surface acoustic wave (SAW) interdigital transducers (IDT) with uniform electrodes. In most SAW filters, such transducers are used with apodized IDTs or instead of them. The proposed optimization algorithms are intended for improvement of IDT selectivity by means of a withdrawal weighting (WW) technique. Unlike the familiar methods of WW transducer optimization, these algorithms choose the best IDT structure on the basis of how well it meets the specifications, not in the time but in Frequency domain directly. This approach is more effective for broadband WW transducers. A number of SAW filters have been designed using the described algorithms. Their experimental characteristics follow: bandwidths of 0.5 to 15%, stopband rejection of 40 to 50 dB, 3 dB/40 dB shape factors of 1.07 to 1.3.

关键词： Transducers optimization algorithms interdigital transducers Bandwidth CARBON YARN Surface acoustic waves surface acoustic wave filters broadband

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UNIVERSAL HALTING TIMES IN optimization AND MACHINE LEARNING

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QUARTERLY OF APPLIED MATHEMATICS 2018年第2期76卷 289-301页

作者： Sagun, Levent Trogdon, Thomas Lecun, Yann NYU Dept Math 550 1St Ave New York NY 10012 USA Univ Calif Irvine Dept Math Irvine CA 92697 USA NYU Dept Comp Sci New York NY 10012 USA

We present empirical evidence that the halting times for a class of optimization algorithms are universal. The algorithms we consider come from quadratic optimization, spin glasses and machine learning. A universality theorem is given in the case of the quadratic gradient descent flow. More precisely, given an algorithm, which we take to be both the optimization routine and the form of the random landscape, the fluctuations of the halting time of the algorithm follow a distribution that, after centering and scaling, appears invariant under changes in the distribution on the landscape universality is present.

关键词： Quadratic optimization algorithms landscapes descent Random algorithms Machine Learning spin glasses Invariant fluctuations Scaling

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