GREEDY NON-DOMINATED SORTING IN GENETIC ALGORITHM-ⅡFOR VEHICLE ROUTING PROBLEM IN DISTRIBUTION

Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.

Artificial Bee Colony Algorithm-based Parameter Estimation of Fractional-order Chaotic System with Time Delay

It is an important issue to estimate parameters of fractional-order chaotic systems in nonlinear science, which has received increasing interest in recent years. In this paper, time delay and fractional order as well as system's parameters are concerned by treating the time delay and fractional order as additional parameters. The parameter estimation is converted into a multi-dimensional optimization problem. A new scheme based on artificial bee colony ABC algorithm is proposed to solve the optimization problem. Numerical experiments are performed on two typical time-delay fractional-order chaotic systems to verify the effectiveness of the proposed method. © 2014 Chinese Association of Automation.

A Genetic-Algorithm-based Neural Network Approach for Radioactive Activity Prediction

In this paper,a genetic-algorithm-based artificial neural network(GAANN)model radioactivity prediction is proposed,which is verified by measuring results from Long Range Alpha Detector(LRAD).GAANN can integrate capabilities of approximation of Artificial Neural Networks(ANN)and of global optimization of Genetic Algorithms(GA)so that the hybrid model can enhance capability of generalization and prediction accuracy,theoretically.With this model,both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation.The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation(BP)neural network,showing the feasibility and validity of the proposed approach.

A genetic-algorithm-based neural network approach for EDXRF analysis

In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm(GA) and back propagation(BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.

Quantitative structure-property relationship study of the solubility of thiazolidine-4-carboxylic acid derivatives using ab initio and genetic algorithm-partial least squares

A quantitative structure-activity relationships （QSAR） study is suggested for the prediction of solubility of some thiazolidine-4- carboxylic acid derivatives in aqueous solution. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the solubility of thiazolidine- 4-carboxylic acid derivatives as a function of molecular structures was established by means of the partial least squares （PLS）. The subset of descriptors, which resulted in the low prediction error, was selected by genetic algorithm. This model was applied for the prediction of the solubility of some thiazolidine-4-carboxylic acid derivatives, which were not in the modeling procedure. The relative errors of prediction lower that -4% was obtained by using GA-PLS method. The resulted model showed high prediction ability with RMSEP of 3.8836 and 2.9500 for PLS and GA-PLS models, respectively.

Repulsive firefly algorithm-based optimal switching device placement in power distribution systems

To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.

Hyaluronic acid algorithm-based models for assessment of liver fibrosis： translation from basic science to clinical application

BACKGROUND： The estimation of liver fibrosis is usually dependent on liver biopsy evaluation. Because of its disadvantages and side effects, researchers try to find non-invasive methods for the assessment of liver injuries. Hyaluronic acid has been proposed as an index for scoring the severity of fibrosis, alone or in algorithm models. The algorithm model in which hyaluronic acid was used as a major constituent was more reliable and accurate in diagnosis than hyaluronic acid alone. This review described various hyaluronic acid algorithm-based models for assessing liver fibrosis.DATA SOURCE： A Pub Med database search was performed to identify the articles relevant to hyaluronic acid algorithmbased models for estimating liver fibrosis.RESULT： The use of hyaluronic acid in an algorithm model is an extra and valuable tool for assessing liver fibrosis.CONCLUSIONS： Although hyaluronic acid algorithm-based models have good diagnostic power in liver fibrosis assessment, they cannot render the need for liver biopsy obsolete and it is better to use them in parallel with liver biopsy. They can be used when frequent liver biopsy is not possible in situations such as highlighting the efficacy of treatment protocol for liver fibrosis.

Genetic algorithm-finite element method inversion of the factors determining the recent tectonic stress field of part of East Asia area

Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to deduce the boundary forces and investigate controlling factors. It is suggested that the continent continent collision is the dominant factor controlling the Chinese tectonic stress field. The ocean continent convergence along the subduction zone is an important factor. There exists tensile boundary force along the marginal sea.

A Genetic-Algorithm-Based Information Evolution Model for Social Networks

the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ‘new' and ‘comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content.A series of experiments tested on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; 2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.

Genetic-algorithm-based balanced distribution of functional characteristics for machines

In order to make reconfigurable manufacturing system （RMS） adapt to the fluctuations of production demand with the minimum number of reconflgurations in its full life cycle, we presented a method to design RMS based on the balanced distribution of functional characteristics for ma- chines. With this method, functional characteristics were classified based on machining functions of cutting-tools and machining accuracy of machines. Then the optimization objective was set as the to- tal shortest mobile distance that all the workpieces are moved from one machine to another, and an improved genetic algorithm （GA） was proposed to optimize the configuration. The elitist strategy was used to enhance the global optimization ability of GA, and excellent gene pool was designed to maintain the diversity of population. Software Matlab was used to realize the algorithm, and a case study of simulation was used to evaluate the method.

Genetic algorithm-based wide-band deterministic maximum likelihood direction finding algorithm

The wide-band direction finding is one of hit and difficult task in array signal processing. This paper generalizes narrow-band deterministic maximum likelihood direction finding algorithm to the wideband case, and so constructions an object function, then utilizes genetic algorithm for nonlinear global optimization. Direction of arrival is estimated without preprocessing of array data and so the algorithm eliminates the effect of pre-estimate on the final estimation. The algorithm is applied on uniform linear array and extensive simulation results prove the efficacy of the algorithm. In the process of simulation, we obtain the relation between estimation error and parameters of genetic algorithm.

PSO/ACO Algorithm-based Risk Assessment of Human Neural Tube Defects in Heshun County,China

Abstract Objective To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. Methods The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects （NTDs）. A hybrid particle swarm optimization/ant colony optimization （PSO/ACO） algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes. Results The algorithm was easy to apply, with the accuracy of the results being 69.5%＋7.02% at the 95% confidence level. Conclusion The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.

Genetic Algorithm-Based Approaches for Optimizing S-Boxes

Multi-objective genetic algorithm is much suitable for solving multi-objective optimization problems. By use of Genetic algorithm, the optimization of S-boxes is explored in this paper. Results of the experiments show that, with heuristic mutation strategy, the algorithm has high searching efficiency and fast convergence speed. Meanwhile, we also have take the avalanche probability of S-boxes into account, besides nonlinearity and difference uniformity. Under this method, an effective genetic algorithm for 6×6 S-boxes is provided and a number of S-boxes with good cryptographic capability can be obtained.

Chaotic Genetic Algorithm-Based Forest Harvest Adjustment

Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adjustment. As intelligent optimization,chaotic genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective planning the settlement of the issue,specially in complex occasions where there are many objective functions and optimize variables. In order to solve the problem of forest harvesting adjustment,this paper introduces a genetic algorithm to the Forest Farm of Qiujia Liancheng Longyan for forest harvesting adjustment firstly. And the experimental result shows that the method is feasible and effective,and it can provide satisfactory solution for policy makers.

A Genetic Algorithm-Based Smart Antenna Technique for Anti-Collision of Multiple SAW ID-Tags

In the radio frequency identification (RFID) system based on surface acoustic wave (SAW) technique, some tags often locate in the field of a transceiver at the same time. These tags produce simultaneous echo signals which "collide" when they arrive back at the transceiver, which leads to difficult identification. In this paper, smart antenna technique is presented to implement anti-collision in SAW RFID system. The direction of arrivals (DOAs) are used to denote the locations of tags, and genetic algorithm (GA) is suggested to find the optimal estimates of the DOAs in complex multimodal search spaces. Once the DOAs are obtained, the array weights are formed and the signals of tags are recovered to implement decoding. The experimental results show that the GA-based smart antenna technique works well in some occasions.

Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

残膜回收机有效作业面积监测系统设计研究

为便于新疆棉田残膜回收的精准作业收费和补贴,设计了一种残膜回收机有效作业面积监测系统。通过仿真试验对比锁相放大器降噪前后残膜回收机作业时经纬度数据,得出锁相滤波对于噪声环境有较好的抗干扰性能;采用高斯投影正算方法将降噪后经纬度数据转化为平面坐标,得出1.5°带的高斯投影的相对误差相较于3°带降低了0.01%。同时,利用卡尔曼滤波算法和构成角方法筛选有效作业轨迹,使用缓冲区法计算重复作业面积,最终利用轨迹算法精准获取残膜回收机有效作业面积,得出所设计的残膜回收机有效作业面积监测系统对于大块农田测量时相对误差为2.6%,对于小块农田测量时相对误差为3.3%。研究结果表明:所设计的残膜回收机有效作业面积监测系统可以精确计算出残膜回收机的有效作业面积,为残膜回收机作业质量评估提供了一定的理论基础。

Fe_(n)Mo_(38-n)(n=0-38)及Fe_(n)Mo_(55-n)(n=0-55)双金属团簇的结构演化和基态能量

基于半经验的Gupta多体势,采用遗传算法及分子动力学淬火算法,系统研究了(FeMo)m(m=38及55)双金属团簇的基态结构及其能量.结果表明:对于Fe_(n)Mo_(38-n)(n=0-38)团簇,随Fe原子数的增加,Fe原子优先占据团簇表面再占据内部,其基态构型存在类O_(h)结构、类I_(h)结构和无序结构间的竞争.对于Fe_(n)Mo_(55-n)(n=0-55)团簇,随Fe原子数的增加,Fe原子优先占据团簇中心位置,再依次占据表面顶点、棱边和次外层,双金属团簇基态构型主要体现为在Mackay二十面体基础上的结构畸变.Fe_(24)Mo_(14),Fe_(13)Mo_(42)和Fe_(43)Mo_(12)为幻数结构团簇,研究发现双金属团簇幻数成因不能通过单质团簇常用的平均配位数和平均键长模型解释,它更多的归咎于组分效应导致的结构高对称性.