The minimum horizontal stress (S-hmin) is one of the three principal stresses and is required for evaluation of the hydraulic fracturing, sand production, and well stability. S-hmin is obtained using direct methods su...
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The minimum horizontal stress (S-hmin) is one of the three principal stresses and is required for evaluation of the hydraulic fracturing, sand production, and well stability. S-hmin is obtained using direct methods such as the leak-off and mini-frac tests or using some equations like the poroelastic equation. These equations require some information including the elastic parameters, shear sonic logs, core data and the pore pressure. In this study, a geomechanical model is constructed to obtain the minimum horizontal stress;then, an artificial neural network (ANN) with multilayer perceptron and feedforward backpropagation algorithm based on the conventional well logging data is applied to predict the S-hmin. Cuckoo optimization algorithm (COA), imperialist competitive algorithm, particle swarm optimization and genetic algorithm are also utilized to optimize the ANN. The proposed methodology is applied in two wells in the reservoir rock located at the southwest of Iran, one for training, and the other one for testing purposes. It is found that the performance of the COA-ANN is better than the other methods. Finally, S-hmin values can be estimated by the conventional well logging data without having the required parameters of the poroelastic equation.
Two feedforward control strategies for an experimental annealing furnace equipped with electrically powered infrared (IR) lamps are developed and compared. For the first controller, the optimal time evolution of the e...
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作者：
Claveria, OscarMonte, EnricTorra, SalvadorUniv Barcelona
AQR IREA Inst Appl Econ Res Diagonal 690 Barcelona 08034 Spain UPC
Dept Signal Theory & Commun Jordi Girona 1-3 Barcelona 08034 Spain Univ Barcelona
Dept Econometr & Stat Riskctr IREA Diagonal 690 Barcelona 08034 Spain Univ Barcelona
Fac Econ & Business Dept Econometr Stat & Appl Econ Diagonal 690 Barcelona 08034 Spain
In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about...
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In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance.
Community detection is a complex optimization problem that consists on searching homogeneous communities that belong to a given graph. This graph, which represent a network, has properties that enable the detection of...
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ISBN:
(纸本)9781450349390
Community detection is a complex optimization problem that consists on searching homogeneous communities that belong to a given graph. This graph, which represent a network, has properties that enable the detection of characteristics or functional relationships in the network. A large number of approaches have been proposed to solve this problem in different disciplines. Nevertheless, only a few research papers have applied community detection to power grids. This paper presents a new evolutionary algorithm for community detection that is applied in power grids. This evolutionary approach employs an efficient initialization strategy that only considers feasible solutions and uses two different search operators that allow the algorithm to obtain a good convergence and diversity of solutions. The preliminary results show that the proposed algorithm obtain quality results in real power grids.
The density classification task (DCT for short) is one of the most studied benchmark problems for analyzing emergent computations performed by cellular automata. Starting from the observation that the performance of t...
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The density classification task (DCT for short) is one of the most studied benchmark problems for analyzing emergent computations performed by cellular automata. Starting from the observation that the performance of the best known solutions is not stable towards initial configurations size;we propose in this paper, some new evolutionary mechanisms for designing new solutions with similar conceptual properties to the best known ones. The approach is based on varying the size of initial configurations which allows making comparisons and analysis between the different solutions. We show then through a set of numerical results that the proposed mechanism allows collecting solutions for the DCT more efficiently and with reduced efforts. Also, we show that the collected solutions are affected by configurations size variations, where only few of them are scalable.
During system restoration, it is critical to determine the start-up sequence of non-black-start generators according to post-blackout situations following a blackout. The generator start-up sequencing is a multi-objec...
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ISBN:
(纸本)9781538664612
During system restoration, it is critical to determine the start-up sequence of non-black-start generators according to post-blackout situations following a blackout. The generator start-up sequencing is a multi-objective optimization problem and the importance of different objectives varies a lot. In this paper, the corresponding optimization problem is formulated as a preference multi-objective optimization model based on preference optimization theory. The preference information of the proposed optimization model is expressed as a reference point in an a priori way. The r-NSGA-II algorithm, incorporating the reference solution-based dominance relation, is used to solve the preference multi-objective optimization model according to the specified reference point. The proposed generator start-up method can provide preferred optimal generator start-up sequences and corresponding restoration paths simultaneously. Simulation results of the New England 10-unit 39-bus power system and the western Shandong power system in north China demonstrate the basic features and the effectiveness of the proposed method.
This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolve...
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This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyze the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The paper also highlights important open research challenges in the field.
One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing us...
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One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through action demonstrations. In classical robot imitation frameworks, actions are modelled using joint or Cartesian space trajectories. They accurately describe actions where geometrical characteristics are relevant, such as fixed trajectories from one pose to another. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these conventional methods, as it encodes actions as changes of any selected feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using evolutionary algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. The current strategies involve performing evaluations in a simulated environment, transferring only the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within the CGDA framework: naive PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO). The second approach studied the introduction of geometrical and velocity constraints within the CGDA framework. The effects of both approaches were analyzed and compared in the
New allotropic forms of carbon based on D-60 and D-20 fullerenes are considered. The most stable carbon compounds are found using an evolution algorithm, and their crystal structure (X-ray diffraction spectra) and ele...
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New allotropic forms of carbon based on D-60 and D-20 fullerenes are considered. The most stable carbon compounds are found using an evolution algorithm, and their crystal structure (X-ray diffraction spectra) and electron (band structure) and mechanical (moduli of elasticity, hardness) characteristics are studied. The carbon phase with the tetragonal symmetry with mechanical properties close to those of a diamond crystal and having a narrow band gap is found.
Cuckoo search algorithm (CSA) is an evolutionary optimization algorithm, which requires an additional constraint handling mechanism. While CSA has been successfully applied for exploring the search space, it lacks goo...
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