Fuzzy minimum weight edge covering problem

Minimum weight edge covering problem, known as a classic problem in graph theory, is employed in many scientific and engineering applications. In the applications, the weight may denote cost, time, or opponent’s payoff, which can be vague in practice. This paper considers the edge covering problem under fuzzy environment, and formulates three models which are expected minimum weight edge cover model, α-minimum weight edge cover model, and the most minimum weight edge cover model. As an extension for the models, we respectively introduce the crisp equivalent of each model in the case that the weights are independent trapezoidal fuzzy variables. Due to the complexity of the problem, a hybrid intelligent algorithm is employed to solve the models, which can deal with the problem with any type of fuzzy weights. At last, some numerical experiments are given to show the application of the models and the robustness of the algorithm.     

A new multi-objective genetic algorithm applied to hot-rolling process

A new genetic algorithms based multi-objective optimization algorithm (NMGA) has been developed during study. It works on a neighborhood concept in the functional space, utilizes the ideas on weak dominance and ranking and uses its own procedures for population sizing. The algorithm was successfully tested with some standard test functions, and when applied to a real-life data of the hot-rolling campaign of an integrated steel plant, it outperformed another recently developed multi-objective evolutionary algorithm.     

Crowding with nearest neighbors replacement for multiple species niching and building blocks preservation in binary multimodal functions optimization

This paper introduces a novel niching scheme called the q-nearest neighbors replacement (q-NNR) method in the framework of the steady-state GAs (SSGAs) for solving binary multimodal optimization problems. A detailed comparison of the main niching approaches are presented first. The niching paradigm and difference of the selection-recombination genetic algorithms (GAs) and the recombination-replacement SSGAs are discussed. Then the q-NNR is developed by adopting special replacement policies based on the SSGAs; a Boltzmann scheme for dynamically sizing the nearest neighbors set is designed to achieve a speed-up and control the proportion of individuals adapted to different niches. Finally, experiments are carried out on a set of test functions characterized by deception, epistasis, symmetry and multimodality. The results are satisfactory and illustrate the effectivity and efficiency of the proposed niching method.     

Simultaneous microemulsion liquid chromatographic analysis of fat-soluble vitamins in pharmaceutical formulations: Optimization using genetic algorithm

An environmentally benign and simple method has been proposed for separation and determination of fat-soluble vitamins using isocratic microemulsion liquid chromatography. Optimization of parameters affecting the separation selectivity and efficiency including surfactant concentration, percent of cosurfactant (1-butanol), and percent of organic oily solvent (diethyl ether), temperature and pH were performed simultaneously using genetic algorithm method. A new software package, MLR-GA, was developed for this purpose. The results indicated that 73.6 mM sodium dodecyl sulfate, 13.64% (v/v) 1-butanol, 0.48% (v/v) diethyl ether, column temperature of 32.5 °C and 0.02 M phosphate buffer of pH 6.99 are the best conditions for separation of fat-soluble vitamins. At the optimized conditions, the calibration plots for the vitamins were obtained and detection limits (1.06–3.69 μg mL⁻¹), accuracy (recoveries > 94.3), precision (RSD < 3.96) and linearity (0.01–10 mg mL⁻¹) were estimated. Finally, the amount of vitamins in multivitamin syrup and a sample of fish oil capsule were determined. The results showed a good agreement with those reported by manufactures.     

改进遗传算法应用于超低副瓣天线阵的综合设计

提出了一种改进的遗传算法.在同一代中对各个个体采用不重复交叉和不重复变异的策略和分别在交叉和变异后采用精英保留的策略,并改进了自适应规则.将该算法应用于实现线阵超低副瓣方向图.将各个天线单元的电流幅度和单元间距作为参量进行优化.给出了线阵天线单元在不同单元个数和不同间距条件下的优化结果.结果表明,该改进的遗传算法能够优化实现低于-40dB的超低副瓣,优于已有文献中遗传算法对天线阵副瓣的综合设计结果.     

基于遗传算法和LSSVM的网络安全事件发生频率预测

Since the frequency of network security incidents is nonlinear, traditional prediction methods such as ARMA, Gray systems are difficult to deal with the problem. When the size of sample is small, methods based on artificial neural network may not reach a high degree of preciseness. Least Squares Support Vector Machines (LSSVM) is a kind of machine learning methods based on the statistics learning theory, it can be applied to solve small sample and non-linear problems very well. This paper applied LSSVM to predict the occur frequency of network security incidents. To improve the accuracy, it used an improved genetic algorithm to optimize the parameters of LSSVM. Verified by real data sets, the improved genetic algorithm (IGA) converges faster than the simple genetic algorithm (SGA), and has a higher efficiency in the optimization procedure. Specially, the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.     

Empirical pseudopotential band structure parameters of 4H-SiC using a genetic algorithm fitting routine

In this paper, the viability of using a genetic algorithm to find band structure parameters for empirical pseudopotential method (EPM) calculations is demonstrated by applying a genetic algorithm to find the EPM parameters for 4H-SiC. The form of the pseudopotential for 4H-SiC and the 19 form factors found by the genetic algorithm to fit the band structure to experimentally measured indirect energy gap and direct optical gaps are given. In addition, the effective masses for the conduction band minimum are extracted from the calculated band structure. It is shown that the genetic algorithm provides an effective, automated way to find parameters that give reasonably good fits to both the band gaps and the effective masses simultaneously.     

ZnO thin film characterization by X-ray reflectivity optimization using genetic algorithm and Fourier transformation

Zinc oxide (ZnO) thin film was fabricated by sol-gel spin coating method on glass substrate. X-ray reflectivity (XRR) and its optimization have been used for characterization and extracting physical parameters of the film. Genetic algorithm (GA) has been applied for this optimization process. The model independent information was needed to establish data analyzing process for X-ray reflectivity before optimization process. Independent information was exploited from Fourier transform of Fresnel reflectivity normalized X-ray reflectivity. This Fourier transformation (Auto Correlation Function) yields thickness of each coated layer on substrate. This information is a keynote for constructing optimization process. Specular X-ray reflectivity optimization yields structural parameters such as thickness, roughness of surface and interface and electron density profile of the film. Acceptable agreement exists between results obtained from Fourier transformation and X-ray reflectivity fitting.     

A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths

The competitive environment of global markets has forced many manufacturers to select the most appropriate supply chain network (SCN) for reduction of total costs and wasted time. Cost reduction and selection of the appropriate length of each period are two important factors in the competitive market that are often not addressed comprehensively by researchers. In our study, we proposed genetic algorithms (GAs) for optimising a novel mathematical model of the defective goods supply chain network (DGSCN). In the proposed model, we assumed that all imperfect-quality products are not repairable, whereas those considered as scrap are directly sold to customers at a low price. The objective of the proposed model is to minimise the costs of production, distribution, holding and backorder. In addition to minimising the costs, the model can determine the economic production quantity (EPQ), the appropriate length of each cycle (ALOEC) and the quantities of defective products, scrap products and retailer shortages using Just-In-Time logistics (JIT-L). We used the GAs and a Cplex solver with probability parameters and various dimensions for validation of the studied model in real-life situations, and we compared the outputs to demonstrate the performance of the model. Additionally, to identify the appropriate length of each cycle (ALOEC), we needed to solve the model using exact parameters and same dimensions and prefer to use Lingo for this application.