scholarly journals ALGORITMOS GENÉTICOS APLICADOS NA CRIAÇÃO DE AGENDAS DE HORÁRIO SEMANAL DE AULAS

2019 ◽  
Vol 11 (4) ◽  
pp. 1-9
Author(s):  
Raildo Santos de Lima ◽  
Nilmaer Souza da Silva ◽  
Rafael Bratifich ◽  
Renato Carlos Camacho Neves
Keyword(s):  
Genetic Algorithms ◽  
Computational Cost ◽  
Complex Problem ◽  
Class Schedule

Determining a weekly class schedule is a computationally complex problem whose computational cost can increase exponentially in relation to the number of variables involved in the solution. There areseveral software on the market that build this timetable based on deterministic rules. Alternatively, we intend to build this solution using Genetic Algorithms, making use of its exploratory capacity in multidimensional spaces.

Genetic Algorithms Applied to Flow Estimation in a Two-Phase Flow With a Venturi Meter

2014 ◽  
Author(s):  
Manuel A. Borregales ◽  
Gilberto Nuñez ◽  
Jose Cappelletto ◽  
Miguel Asuaje
Keyword(s):  
Genetic Algorithms ◽  
Mass Flow ◽  
Computational Cost ◽  
Venturi Tube ◽  
Oil Reservoirs ◽  
Two Phase ◽  
Flow Estimation ◽  
Deep Waters ◽  
Rms Error ◽  
Almost All

Due to depletion of on-shore and superficial oil reservoirs, and impulsed by recent discoveries of oil reservoirs in off-shore ultra-deep waters, each of the processes and equipment in oil production required further improvements in order to save costs, space and to reduce weight off-shore. One way to accomplish this is without separators and with the use of online multiphase flowmeters. The most used flowmeter is the Venturi tube. Despite Venturi flowmeters having been used in almost all commercial multiphase flowmeters, there is not a single correlation that provides good results for predicting mass flow in each phase, for any flow pattern, mass quality, void fraction and/or fluids properties. Instead, many correlations have been published, based on experimental and/or field data, but the use of these correlations outside multiphase range conditions is doubtful. This study proposes a new methodology that uses genetic algorithms to find correlations that better fit a set of data, which allow determining the mass flow of a two-phase mix through a Venturi tube. For that purpose, binary trees and Prüfer encoding are used to accomplish this implementation. The correlations found in this new methodology provide lower values of RMS error, 1–3%, against correlations proposed by previous authors that show an RMS error range of 5–10%. This technique allows finding further correlations, regardless the number of parameters to be used, at a low computational cost, and it does not require previous information on the behaviour of the data.

An Improved Kriging Assisted Multi-Objective Genetic Algorithm

2010 ◽  
Author(s):  
Mian Li
Keyword(s):  
Genetic Algorithms ◽  
Computational Cost ◽  
Population Based ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Kriging Metamodel ◽  
Metamodeling Technique ◽  
Important Challenge ◽  
Estimated Error ◽  
Adaptive Metamodeling

Although Genetic Algorithms (GAs) and Multi-Objective Genetic Algorithms (MOGAs) have been widely used in engineering design optimization, the important challenge still faced by researchers in using these methods is their high computational cost due to the population-based nature of these methods. For these problems it is important to devise MOGAs that can significantly reduce the number of simulation calls compared to a conventional MOGA. We present an improved kriging assisted MOGA, called Circled Kriging MOGA (CK-MOGA), in which kriging metamodels are embedded within the computation procedure of a traditional MOGA. In the proposed approach, the decision as to whether the original simulation or its kriging metamodel should be used for evaluating an individual is based on a new objective switch criterion and an adaptive metamodeling technique. The effect of the possible estimated error from the metamodel is mitigated by applying the new switch criterion. Three numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed approach. The results show that, on the average, CK-MOGA outperforms both a conventional MOGA and our developed Kriging MOGA in terms of the number of simulation calls.

A Metamodeling Technique for Variable Geometry Trusses Design via Equivalent Parametric Macroelements

2009 ◽  
Vol 131 (10) ◽  
Author(s):  
Josu Aguirrebeitia ◽  
Carlos Angulo ◽  
Luis M. Macareno ◽  
Rafael Avilés
Keyword(s):  
Genetic Algorithms ◽  
Finite Elements ◽  
Elastic Energy ◽  
Computational Cost ◽  
Least Square ◽  
Optimization Process ◽  
Fe Model ◽  
Variable Geometry ◽  
Optimum Parameters ◽  
Metamodeling Technique

A metamodeling methodology is applied to reduce the large computational cost required in the design of variable geometry trusses (VGTs). Using this methodology, submodels of finite elements within a complete FE model of the VGT are substituted by groups of fewer elements called equivalent parametric macroelements (EPMs). The EPM optimum parameters are obtained using equivalence criteria based on elastic energy and inertial properties. The optimization process is performed using nonlinear least square minimization and genetic algorithms.

scholarly journals Scheduling Courses using Genetic Algorithms

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Schedule ◽  
Heuristic Method ◽  
Biological Evolution ◽  
Time Data ◽  
Fitness Evaluation ◽  
Class Schedule ◽  
Student College ◽  
Crossover And Mutation

Preparation of courses at every university is done by hand. This method has limitations that often cause collisions schedule. In lectures and lab scheduling frequent collision against the faculty member teaching schedule, collisions on the class schedule and student, college collision course with lab time, the allocation of the use of the rooms were not optimal. Heuristic method of genetic algorithm based on the mechanism of natural selection; it is a process of biological evolution. Genetic algorithms are used to obtain optimal schedule that consists of the initialization process of the population, fitness evaluation, selection, crossover, and mutation. Data used include the teaching of data, the data subjects, the room data and time data retrieved from the database of the Faculty of Computer Science, Universitas Pembangunan Panca Budi. The data in advance through the stages of the process of genetic algorithms to get optimal results The results of this study in the form of a schedule of courses has been optimized so that no error occurred and gaps.

Hybrid Approach to Tomographic Reconstruction of Bubbles in Two-Phase Flows Using Algebraic Reconstruction Technique (ART), Genetic Algorithms (GA) and Simplex Method

1999 ◽  
Author(s):  
A. Rajesh ◽  
K. D. Kihm ◽  
L. Yang ◽  
J. Yen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simplex Method ◽  
Tomographic Reconstruction ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Reconstruction Technique ◽  
Algebraic Reconstruction Technique ◽  
Two Phase ◽  
Algebraic Reconstruction

Abstract Main hurdles in the application of genetic algorithms to complex problems are two fold. One is the high computational cost due to their slow convergence rate. The other is to reduce the number of input parameters of a conventional genetic algorithm as in the case of tomographic reconstruction of bubbles. In our present work great strides have been made to alleviate both the problems by using a Hybrid model of Algebraic Reconstruction Technique (ART), Simplex Method and Genetic Algorithm (GA). Our results showed that the hybrid approach is an effective and robust optimization image reconstruction technique.

Efficient Dynamic Analysis of a Nonlinear Wave Energy Harvester Model

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Pol D. Spanos ◽  
Felice Arena ◽  
Alessandro Richichi ◽  
Giovanni Malara
Keyword(s):  
Monte Carlo ◽  
Linear System ◽  
Wave Energy ◽  
Alternative Energy ◽  
Energy Harvester ◽  
Single Point ◽  
Computational Cost ◽  
Complex Problem ◽  
Statistical Linearization ◽  
Monte Carlo Techniques

In recent years, wave energy harvesting systems have received considerable attention as an alternative energy source. Within this class of systems, single-point harvesters are popular at least for preliminary studies and proof-of-concept analyses in particular locations. Unfortunately, the large displacements of a single-point wave energy harvester are described by a set of nonlinear equations. Further, the excitation is often characterized statistically and in terms of a relevant power spectral density (PSD) function. In the context of this complex problem, the development of efficient techniques for the calculation of reliable harvester response statistics is quite desirable, since traditional Monte Carlo techniques involve nontrivial computational cost. The paper proposes a statistical linearization technique for conducting expeditiously random vibration analyses of single-point harvesters. The technique is developed by relying on the determination of a surrogate linear system identified by minimizing the mean square error between the linear system and the nonlinear one. It is shown that the technique can be implemented via an iterative procedure, which allows calculating statistics, PSDs, and probability density functions (PDFs) of the response components. The reliability of the statistical linearization solution is assessed vis-à-vis data from relevant Monte Carlo simulations. This novel approach can be a basis for constructing computationally expeditious assessments of various design alternatives.

Linkage Identification by Fitness Difference Clustering

2006 ◽  
Vol 14 (4) ◽  
pp. 383-409 ◽  
Author(s):  
Miwako Tsuji ◽  
Masaharu Munetomo ◽  
Kiyoshi Akama
Keyword(s):  
Genetic Algorithms ◽  
Perturbation Methods ◽  
Computational Cost ◽  
Building Blocks ◽  
Estimation Of Distribution Algorithms ◽  
Novel Approach ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Class Of Functions ◽  
Dependency Detection

Genetic Algorithms perform crossovers effectively when linkage sets — sets of variables tightly linked to form building blocks — are identified. Several methods have been proposed to detect the linkage sets. Perturbation methods (PMs) investigate fitness differences by perturbations of gene values and Estimation of distribution algorithms (EDAs) estimate the distribution of promising strings. In this paper, we propose a novel approach combining both of them, which detects dependencies of variables by estimating the distribution of strings clustered according to fitness differences. The proposed algorithm, called the Dependency Detection for Distribution Derived from fitness Differences (D5), can detect dependencies of a class of functions that are difficult for EDAs, and requires less computational cost than PMs.

Improving Ontology Alignment through Genetic Algorithms

2010 ◽  
pp. 240-259 ◽  
Author(s):  
José Manuel Vázquez Naya ◽  
Marcos Martínez Romero ◽  
Javier Pereira Loureiro ◽  
Cristian R. Munteanu ◽  
Alejandro Pazos Sierra
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Semantic Similarity ◽  
Common Knowledge ◽  
Complex Problem ◽  
Initial Population ◽  
Ontology Alignment ◽  
Alignment Quality ◽  
Single Measure ◽  
Similarity Metric

Ontology alignment is recognized as a fundamental process to achieve an adequate interoperability between people or systems that use different, overlapping ontologies to represent common knowledge. This process consists of finding the semantic relations between different ontologies. There are different techniques conceived to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple measures into a single similarity metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or calculated through general methods that does not provide optimal results. In this chapter, a genetic algorithm based approach to find out how to aggregate different similarity metrics into a single measure is presented. Starting from an initial population of individuals, each one representing a specific combination of measures, the algorithm finds the combination that provides the best alignment quality.

scholarly journals UAV flight coordination for communication networks: genetic algorithms versus game theory

2021 ◽  
Author(s):  
Alexandros Giagkos ◽  
Elio Tuci ◽  
Myra S. Wilson ◽  
Philip B. Charlesworth
Keyword(s):  
Game Theory ◽  
Genetic Algorithms ◽  
Unmanned Aerial Vehicles ◽  
Communication Networks ◽  
Large Scale ◽  
Complex Problem ◽  
Communication Model ◽  
Aerial Vehicles ◽  
Long Endurance ◽  
Non Cooperative Games

AbstractThe autonomous coordinated flying for groups of unmanned aerial vehicles that maximise network coverage to mobile ground-based units by efficiently utilising the available on-board power is a complex problem. Their coordination involves the fulfilment of multiple objectives that are directly dependent on dynamic, unpredictable and uncontrollable phenomena. In this paper, two systems are presented and compared based on their ability to reposition fixed-wing unmanned aerial vehicles to maintain a useful airborne wireless network topology. Genetic algorithms and non-cooperative games are employed for the generation of optimal flying solutions. The two methods consider realistic kinematics for hydrocarbon-powered medium-altitude, long-endurance aircrafts. Coupled with a communication model that addresses environmental conditions, they optimise flying to maximising the number of supported ground-based units. Results of large-scale scenarios highlight the ability of genetic algorithms to evolve flexible sets of manoeuvres that keep the flying vehicles separated and provide optimal solutions over shorter settling times. In comparison, game theory is found to identify strategies of predefined manoeuvres that maximise coverage but require more time to converge.

Hybridized Genetic Algorithms in the Optimization of a PIFA Antenna Using Fitness Characterization and Clustering

2012 ◽  
Vol 622-623 ◽  
pp. 40-44
Author(s):  
Mohammad Riyad Ameerudden ◽  
Harry C.S. Rughooputh
Keyword(s):  
Genetic Algorithms ◽  
Mobile Communications ◽  
High Performance ◽  
Clustering Algorithm ◽  
Optimization Technique ◽  
Fdtd Method ◽  
Computational Cost ◽  
Evaluation Process ◽  
Low Profile ◽  
Low Profile Antennas

With the exponential development of mobile communications and the miniaturization of radio frequency transceivers, the need for small and low profile antennas at mobile frequencies is constantly growing. Therefore, new antennas should be developed to provide both larger bandwidth and small dimensions. The aim of this project is to design and optimize the bandwidth of a Planar Inverted-F Antenna (PIFA) in order to achieve a larger bandwidth in the 2 GHz band. This paper presents an intelligent optimization technique using a hybridized Genetic Algorithms (GA) coupled with the intelligence of the Binary String Fitness Characterization (BSFC) technique. The optimization technique used is based on the Binary Coded GA (BCGA) and Real-Coded GA (RCGA). The process has been further enhanced by using a Clustering Algorithm to minimize the computational cost. Using the Hybridized GA with BSFC and Clustering, the bandwidth evaluation process has been observed to be more efficient combining both high performance and minimal computational cost. During the optimization process, the different PIFA models are evaluated using the finite-difference time domain (FDTD) method.

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