Muitiobjective pressurized water reactor reload core design by nondominated genetic algorithm search

The design of pressurized water reactor reload cores is not only a formidable optimization problem but also, in many instances, a multiobjective problem. A genetic algorithm (GA) designed to perform true multiobjective optimization on such problems is described. Genetic algorithms simulate natural evolution. They differ from most optimization techniques by searching from one group of solutions to another, rather than from one solution to another. New solutions are generated by breeding from existing solutions. By selecting better (in a multiobjective sense) solutions as parents more often, the population can be evolved to reveal the trade-off surface between the competing objectives. An example illustrating the effectiveness of this novel method is presented and analyzed. It is found that in solving a reload design problem the algorithm evaluates a similar number of loading patterns to other state-of-the-art methods, but in the process reveals much more information about the nature of the problem being solved. The actual computational cost incurred depends: on the core simulator used; the GA itself is code independent.

A self-adaptive genetic algorithm applied to multi-objective optimization of an airfoil

Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

Two-dimensional photonic crystals with a large photonic band gap designed by a rapid genetic algorithm

We used Plane Wave Expansion Method and a Rapid Genetic Algorithm to design two-dimensional photonic crystals with a large absolute band gap. A filling fraction controlling operator and Fourier transform data storage mechanism had been integrated into the genetic operators to get desired photonic crystals effectively and efficiently. Starting from randomly generated photonic crystals, the proposed RGA evolved toward the best objectives and yielded a square lattice photonic crystal with the band gap (defined as the gap to mid-gap ratio) as large as 13.25%. Furthermore, the evolutionary objective was modified and resulted in a satisfactory PC for better application to slab system.

Internal quantum efficiency analysis of solar cell by genetic algorithm

To investigate factors limiting the performance of a GaAs solar cell, genetic algorithm is employed to fit the experimentally measured internal quantum efficiency (IQE) in the full spectra range. The device parameters such as diffusion lengths and surface recombination velocities are extracted. Electron beam induced current (EBIC) is performed in the base region of the cell with obtained diffusion length agreeing with the fit result. The advantage of genetic algorithm is illustrated.

Application of uniform design and genetic algorithm in optimization of reversed-phase chromatographic separation

An optimization method based on uniform design in conjunction with genetic algorithm is described. According to the proposed method, the uniform design technique was applied to the design of starting experiments, which can reduce the number of experiments compared with traditional simultaneous methods, such as simplex. And genetic algorithm was used in optimization procedure, which can improve the rapidity of optimal procedure. The hierarchical chromatographic response function was modified to evaluate the separation equality of a chromatogram. An iterative procedure was adopted to search for the optimal condition to improve the accuracy of predicted retention and the quality of the chromatogram. The optimization procedure was tested in optimization of the chromatographic separation of 11 alkaloids in reversed-phase ion pair chromatography and satisfactory optimal result was obtained. (C) 2003 Elsevier B.V. All rights reserved.

value-based multiple software projects scheduling with genetic algorithm

Chinese Acad Sci, ISCAS Lab Internet Software Technologies

Soil differentiation using fingerprint Fourier transform infrared spectroscopy, chemometrics and genetic algorithm-based feature selection

Elliott, G. N., Worgan, H., Broadhurst, D. I., Draper, J. H., Scullion, J. (2007). Soil differentiation using fingerprint Fourier transform infrared spectroscopy, chemometrics and genetic algorithm-based feature selection. Soil Biology & Biochemistry, 39 (11), 2888-2896. Sponsorship: BBSRC / NERC RAE2008

A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation

The role of renewable energy in power systems is becoming more significant due to the increasing cost of fossil fuels and climate change concerns. However, the inclusion of Renewable Energy Generators (REG), such as wind power, has created additional problems for power system operators due to the variability and lower predictability of output of most REGs, with the Economic Dispatch (ED) problem being particularly difficult to resolve. In previous papers we had reported on the inclusion of wind power in the ED calculations. The simulation had been performed using a system model with wind power as an intermittent source, and the results of the simulation have been compared to that of the Direct Search Method (DSM) for similar cases. In this paper we report on our continuing investigations into using Genetic Algorithms (GA) for ED for an independent power system with a significant amount of wind energy in its generator portfolio. The results demonstrate, in line with previous reports in the literature, the effectiveness of GA when measured against a benchmark technique such as DSM.

A Hybrid GLR Algorithm for Parsing with Epsilon Grammars

We give a hybrid algorithm for parsing epsilon grammars based on Tomita's non-ϵ-grammar parsing algorithm ([Tom86]) and Nozohoor-Farshi's ϵ-grammar recognition algorithm ([NF91]). The hybrid parser handles the same set of grammars handled by Nozohoor-Farshi's recognizer. The algorithm's details and an example of its use are given. We also discuss the deployment of the hybrid algorithm within a GB parser, and the reason an ϵ grammar parser is needed in our GB parser.