Photovoltaic panels are a way of providing electricity without altering natural resources. Due to its intermittent nature and to meet consumers demand, this energy has to be stored, for example by using batteries. In ...
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ISBN:
(纸本)9781538627266
Photovoltaic panels are a way of providing electricity without altering natural resources. Due to its intermittent nature and to meet consumers demand, this energy has to be stored, for example by using batteries. In this work, an energy model for buildings with battery is developed based on electrical consumption and production data. This model takes into account the depth of discharge, state of charge and efficiency over a cycle of a lithium type battery. Three rule-based strategies are then described. This leads to our optimization problem. The optimization is applied on two time slots: one day (for different algorithm strategies) and one week. Robust multi-objective optimization is performed in order to reduce the impact of consumption prediction errors.
We present an optimization platform for turbomachineries with complex mesh configuration in a parallel computation environment. A continuous adjoint solver for 3-D viscous internal flow is coded under the same paralle...
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ISBN:
(纸本)9780791850794
We present an optimization platform for turbomachineries with complex mesh configuration in a parallel computation environment. A continuous adjoint solver for 3-D viscous internal flow is coded under the same parallel framework as the flow solver. To meet the various permitted extents of reshaping on blade surface and cut down the computation cost in grid perturbation, a localized two-level mesh deformation method is developed based on Gaussian radial basis function (RBF). This method works efficiently for both the 0 mesh surrounding the blade and the O-H mesh blocks inside tip gap. In optimization of the transonic NASA Rotor 67 for higher adiabatic efficiency with a mass flow rate constraint, an adjoint sensitivity analysis is conducted. The relations between the design sensitive regions and physical phenomena in internal flow are discussed. Flow fields before and after adjoint optimization are investigated, including shock system, tip leakage flow and flow separation.
The DE algorithm is considered as an efficient algorithm in the field of evolutionary algorithms. In case of DE the solution search process is a combination of evolutionary (evolution of population) and swarm intellig...
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The DE algorithm is considered as an efficient algorithm in the field of evolutionary algorithms. In case of DE the solution search process is a combination of evolutionary (evolution of population) and swarm intelligence (position update of individual) search process. In DE, the diversification and convergence of the population is controlled by the crossover rate (CR), the scale factor (F) and the vector differences of three randomly selected individuals. In DE algorithm, every solution is given equal chance to take part in the solution search and in case of stagnation;it is difficult to get out from this situation. Therefore, a competency based position update process is integrated in DE to boost the speed of convergence in addition to the diversification ability of the algorithm. The accuracy, efficiency, robustness, and reliability of the proposed algorithm, namely efficient competency based DE (ECDE) are analyzed over a set of 20 benchmark problems with diverse characteristics. Results are compared with DE and its two recent variants, namely FBDE, FSADE, and other algorithms, namely ABC and PSO.
In this paper, we study the impact of using a hybrid-technique approach, which is a combination of genetic algorithm (GA) and protein's free energy minimization calculations, to predict protein tertiary structure....
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ISBN:
(纸本)9781538611913
In this paper, we study the impact of using a hybrid-technique approach, which is a combination of genetic algorithm (GA) and protein's free energy minimization calculations, to predict protein tertiary structure. We compare the results with a basic approach which applies genetic algorithm only. A genetic algorithm is used to predict the protein structure using the primary structure, the amino acids sequence of a given polypeptide chain, as input. After that, we combine the GA with energy minimization feature. Finally, the outcomes of both experiments are analyzed. Results reveal that the hybrid approach outperforms the basic one.
This paper describes the design of an artificial intelligent opponent in the Empire Wars turn-based strategy computer game. Several approaches to make the opponent in the game, that has complex rules and a huge state ...
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ISBN:
(数字)9783319544724
ISBN:
(纸本)9783319544724
This paper describes the design of an artificial intelligent opponent in the Empire Wars turn-based strategy computer game. Several approaches to make the opponent in the game, that has complex rules and a huge state space, are tested. In the first phase, common methods such as heuristics, influence maps, and decision trees are used. While they have many advantages (speed, simplicity and the ability to find a solution in a reasonable time), they provide rather average results. In the second phase, the player is enhanced by an evolutionary algorithm. The algorithm adjusts several parameters of the player that were originally determined empirically. In the third phase, a learning process based on recorded moves from previous games played is used. The results show that incorporating evolutionary algorithms can significantly improve the efficiency of the artificial player without necessarily increasing the processing time.
Introduction: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the d...
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Introduction: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions. Objectives: To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain. Methods: Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery' as well as other detection methods in the previous studies. Results: The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute app
Participatory search is a population-based algorithm derived from the participatory learning paradigm. The algorithm accounts for the fact that the compatibility between individuals of the current population and the c...
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ISBN:
(纸本)9781509049172
Participatory search is a population-based algorithm derived from the participatory learning paradigm. The algorithm accounts for the fact that the compatibility between individuals of the current population and the combination of compatibles, help to improve the value of an objective function during the search for an optimum. This paper focuses on the use of participatory search as a tool to develop fuzzy linguistic rule-based models. The performance of the models produced by participatory search algorithm is compared with a state of a start of the art genetic fuzzy system approach. Experimental results suggest that the participatory search algorithm with arithmetic-like recombination performs best.
A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers w...
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A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the problem has a high degree of uncertainty. However, designing interval type-2 fuzzy controllers is more difficult because there are more parameters involved. In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which has been shown to give good results in control if the type-1 fuzzy systems can be obtained appropriately. An evolutionary algorithm is applied to find the optimal interval type-2 fuzzy system as mentioned above. The human evolutionary model is applied for optimizing the interval type-2 fuzzy controller for a particular non-linear plant and results are compared against an optimal type-1 fuzzy controller. A comparative study of simulation results of the type-2 and type-1 fuzzy controllers, under different noise levels, is also presented. Simulation results show that interval type-2 fuzzy controllers obtained with the evolutionary algorithm outperform type-1 fuzzy controllers.
In order to effectively design nearly Zero Energy Buildings, the assessment of energy performance in the early design stages through simulation is an important, although very demanding and complex, procedure. Over the...
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In order to effectively design nearly Zero Energy Buildings, the assessment of energy performance in the early design stages through simulation is an important, although very demanding and complex, procedure. Over the last decades, various tools and methods have been developed to address performance-related design questions, mostly using Multi-Objective Optimization algorithms. Technological advances have revolutionized the way Architects design and think, automating complex tasks and allowing the assessment of multiple variants at the same time. In this paper, a new nZEB design workflow methodology is proposed, integrating evolutionary algorithms and energy simulation, and its capabilities and current limitations are explored. (C) 2017 The Authors. Published by Elsevier Ltd.
HNCO consists of a C++ library, command-line tools, and scripts for the optimization of black box functions defined on fixed-length bit vectors. It aims at being flexible, fast, simple, and robust. The library provide...
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ISBN:
(纸本)9781450349390
HNCO consists of a C++ library, command-line tools, and scripts for the optimization of black box functions defined on fixed-length bit vectors. It aims at being flexible, fast, simple, and robust. The library provides classes for functions, populations, neighborhoods, and algorithms. It currently includes 22 concrete functions and 18 concrete algorithms. The command-line tools expose most of the library to the user without the need for programming. One of the goals of HNCO is to automate experiments and favor reproducible research. HNCO comes with experiments designed to tune or compare algorithms. Scripts run all the simulations in an experiment and generate a report. The source code of HNCO is published under the GNU LGPL 3 license.
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