Genetic Algorithm-pattern Search Research Articles

A hybrid real coded genetic algorithm – Pattern search approach for selective harmonic elimination of PWM AC/AC voltage controller

Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package. Finally, these results are verified by means of an experimental prototype.

Estimation of bubble point pressure from PVT data using a power-law committee with intelligent systems

Bubble point pressure is the most crucial pressure–volume–temperature (PVT) property of reservoir fluid, which plays a critical role in almost all tasks related to reservoir and production engineering. Therefore, an accurate, quick, and easy way of predicting bubble point pressure from available PVT parameters is desired. In this study, an improved methodology is followed for making a quantitative formulation between bubble point pressure (target) and some available PVT data (inputs) such as proportion of solution gas–oil-ratio over gas gravity, temperature, and stock-tank oil gravity. At the first stage of this research, bubble point pressure was predicted from PVT data using different intelligent systems, including neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a power-law committee with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search tool. The proposed methodology, power-law committee with intelligent systems, comprises a parallel framework that produces a final output by combining the results of individual intelligent systems. To achieve this objective, a power-law formula structure was designated to integrate outputs of intelligent systems. A hybrid genetic algorithm-pattern search tool was then employed to find the optimal coefficients of this formula. A database of 361 worldwide data points was employed in this study, while 282 data points were used for model construction (i.e., training data), and 79 data points were employed to assess the reliability of the model (test data). Results showed that outputs of intelligent systems are in good agreement with reality. However, by little additional computation, power-law committee with intelligent systems is capable of significantly improving the accuracy of target prediction.

Improving the accuracy of flow units prediction through two committee machine models: An example from the South Pars Gas Field, Persian Gulf Basin, Iran

Intelligent reservoir characterization is a prerequisite study for development of oil and gas fields. Hydraulic flow units are mappable portions of hydrocarbon-bearing rocks that control fluid flow, and their modeling allows an accurate understanding of reservoir quality within a hydrocarbon field. The current study presents two committee machines based on intelligent models to make a quantitative formulation between flow units and conventional log responses in the South Pars Gas Field, Iran. First, a committee machine with intelligent systems (CMIS) is constructed using a genetic algorithm-pattern search technique. The overall mean squared error and coefficient of determination between the measured and predicted flow zone indicators (FZI) using the CMIS for the test data are 0.1468 and 0.775, respectively. Afterwards, a committee fuzzy inference system (CFIS) is constructed. For this purpose, the training data are divided into two individual clusters based on the FZI values. The two FZI clusters are trained with the individual fuzzy inference systems, and a classifier network assigns appropriate weights to each cluster. The MSE and coefficient of determination of the CFIS are 0.1233 and 0.812, respectively. The CFIS shows some improvement in the accuracy of predictions in comparison with the CMIS.The results of this study demonstrate the higher performance of the committee machines compared to the individual expert systems for estimating reservoir properties. Moreover, a primary clustering of the model data and their training with the individual expert systems can lead to a considerable improvement in the accuracy of committee machines.

Classification of anti-HIV compounds using counterpropagation artificial neural networks and decision trees

The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure–activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm–pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities. Some general parameters such as molecular weight, average molecular weight, number of hydrogen atoms and number of hydroxyl groups were found to be important for describing the inhibition behaviour of anti-HIV agents. The developed classifier models in this work can be used to screen large libraries of compounds to identify those likely to display activity as anti-HIV agents.

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems

This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem.

Digital Pattern Search and Its Hybridization with Genetic Algorithms for Bound Constrained Global Optimization

In this paper, we present a recently developed pattern search method called Genetic Pattern Search algorithm (GPSA) for the global optimization of cost function subject to simple bounds. GPSA is a combined global optimization method using genetic algorithm (GA) and Digital Pattern Search (DPS) method, which has the digital structure represented by binary strings and guarantees convergence to stationary points from arbitrary starting points. The performance of GPSA is validated through extensive numerical experiments on a number of well known functions and on robot walking application. The optimization results confirm that GPSA is a robust and efficient global optimization method.