Composition of Web Services of Multi-Population Adaptive Genetic Algorithm Based on Cosine Improvement

Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.

Analysis of the diversity of population and convergence of genetic algorithms based on Negentropy

With its wide use in different fields, the problem of the convergence of simple genetic algorithms (GAs) has been concerned. In the past, the research on the convergence of GAs was based on Holland's model theorem. The diversity of the evolutionary population and the convergence of GAs are studied by using the concept of negentropy based on the discussion of the characteristic of GA. Some test functions are used to test the convergence of GAs, and good results have been obtained. It is shown that the global optimization may be obtained by selecting appropriate parameters of simple GAs if the evolution time is enough.

The Distribution Population-based Genetic Algorithm for Parameter Optimization PID Controller

Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.

A Novel Genetic Algorithm Preventing Premature Convergence by Chaos Operator

An improved genetic algorithm (GA) is proposed based on the analysis of population diversity within the framework of Markov chain. The chaos operator to combat premature convergence concerning two goals of maintaining diversity in the population and sustaining the convergence capacity of the GA is introduced. In the CHaos Genetic Algorithm (CHGA), the population is recycled dynamically whereas the most highly fit chromosome is intact so as to restore diversity and reserve the best schemata which may belong to the optimal solution. The characters of chaos as well as advanced operators and parameter settings can improve both exploration and exploitation capacities of the algorithm. The results of multimodal function optimization show that CHGA performs simple genetic algorithms and effectively alleviates the problem of premature convergence.

Family genetic algorithms based on gene exchange and its application

Genetic Algorithms （GA） are a search techniques based on mechanics of nature selection and have already been successfully applied in many diverse areas. However, increasing samples show that GA＇s performance is not as good as it was expected to be. Criticism of this algorithm includes the slow speed and premature result during convergence procedure. In order to improve the performance, the population size and individuals＇ space is emphatically described. The influence of individuals＇ space and population size on the operators is analyzed. And a novel family genetic algorithm （FGA） is put forward based on this analysis. In this novel algorithm, the optimum solution families closed to quality individuals is constructed, which is exchanged found by a search in the world space. Search will be done in this microspace. The family that can search better genes in a limited period of time would win a new life. At the same time, the best gene of this micro space with the basic population in the world space is exchanged. Finally, the FGA is applied to the function optimization and image matching through several experiments. The results show that the FGA possessed high performance.

The Markov Chain Analysis of Premature Convergence of Genetic Algorithms

This paper discussed CGA population Markov chain with mutation probability. For premature convergence of this algorithm, one concerned, we give its analysis of Markov chain.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

A New Fuzzy Adaptive Genetic Algorithm

Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution. A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.

Generalized Self-Adaptive Genetic Algorithms

In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.

Three-Objective Programming with Continuous Variable Genetic Algorithm

The subject area of multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually, there does not exist a single solution that optimizes all functions simultaneously;quite the contrary, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision made by taking elements of nondominated set as alternatives, which is given by analysts. Since it is important for the decision maker to obtain as much information as possible about this set, our research objective is to determine a well-defined and meaningful approximation of the solution set for linear and nonlinear three objective optimization problems. In this paper a continuous variable genetic algorithm is used to find approximate near optimal solution set. Objective functions are considered as fitness function without modification. Initial solution was generated within box constraint and solutions will be kept in feasible region during mutation and recombination.

Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning

The image signal is represented by using the atomic of image signal to train an over complete dictionary and is described as sparse linear combinations of these atoms. Recently, the dictionary algorithm for image signal tracking and decomposition is mainly adopted as the focus of research. An alternate iterative algorithm of sparse encoding, sample dictionary and dictionary based on atomic update process is K-SVD decomposition. A new segmentation algorithm of brain MRI image, which uses the noise reduction method with adaptive dictionary based on genetic algorithm, is presented in this paper, and the experimental results show that the algorithm in brain MRI image segmentation has fast calculation speed and the advantage of accurate segmentation. In a very complicated situation, the results show that the segmentation of brain MRI images can be accomplished successfully by using this algorithm, and it achieves the ideal effect and has good accuracy.

A New Genetic Algorithm Applied to Multi-Objectives Optimal of Upgrading Infrastructure in NGWN

A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem.

Research on Financial Distress Prediction with Adaptive Genetic Fuzzy Neural Networks on Listed Corporations of China

To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.

改进蚁群算法的送餐机器人路径规划

蚁群算法拥有良好的全局性、自组织性、鲁棒性,但传统蚁群算法存在许多不足之处。为此,针对算法在路径规划问题中的缺陷,在传统蚁群算法的状态转移公式中,引入目标点距离因素和引导素,加快算法收敛性和改善局部最优缺陷。在带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)上,融合蚁群算法和遗传算法,并将顾客时间窗宽度以及机器人等待时间加入蚁群算法状态转移公式中,以及将蚁群算法的解作为遗传算法的初始种群,提高遗传算法的初始解质量,然后进行编码,设置违反时间窗约束和载重量的惩罚函数和适应度函数,在传统遗传算法的交叉、变异操作后加入了破坏-修复基因的操作来优化每一代新解的质量,在Solomon Benchmark算例上进行仿真,对比算法改进前后的最优解,验证算法可行性。最后在餐厅送餐问题中把带有障碍物的仿真环境路径规划问题和VRPTW问题结合,使用改进后的算法解决餐厅环境下送餐机器人对顾客服务配送问题。

基于多种群遗传算法的航天复杂系统测试任务调度

针对航天复杂系统型号较多,传统测试流程与调度设计只能人工定制化排布,效率较低且未有效优化,同时,考虑到航天复杂系统快速测试的迫切需求,提出一种基于多目标遗传算法的航天测试流程自动生成方法。该方法在测试项集合明确的前提下,将测试项抽象为离散事件,以测试总时间和测试资源均衡度为优化目标,充分考虑航天器测试的诸多约束,将其作为遗传算法执行过程中交叉或变异的禁忌项。在初始种群确定后,对测试流程和调度方案进行自动生成和优化。对算例的仿真结果表明,该方法相对于同实验条件下的传统半串行测试方法和单目标优化方法,测试总时间或资源均衡度得到了较大提升。在进一步扩展优化目标和约束项后,该方法可有效提高航天复杂系统测试过程的快速响应能力和可靠性。

基于路径相似表与个体迁移策略的多路径覆盖测试

将遗传算法用于多路径覆盖测试中是个研究热点,在新旧种群迭代过程中,旧种群中可能包含其他子种群的优秀个体,这部分个体未被充分利用,造成资源浪费;同时,种群中的个体数会远大于可达路径数,而每个个体都会经过某一条可达路径,这样会有多个个体经过同一条路径,导致重复计算个体与目标路径的相似度。基于此,提出结合路径相似表与个体迁移的多路径覆盖测试方法以提高测试效率。通过路径相似表存储已计算得到的路径相似度值,避免该值被重复计算,减少测试时间。在进化过程中,将个体路径与其他目标路径进行比较,若相似度达到阈值,则将此优秀个体迁移至该路径对应的子种群中,提高个体利用率并减少进化代数。由实验可知,该方法与其他六种同类经典方法在八个程序上的平均生成时间降低最高达44.64%,最低为2.64%,平均进化代数降低最高达35.08%,最低为6.13%,故该方法有效地提高了测试效率。

双种群混合遗传算法求解航空复合材料柔性调度问题

考虑航空复合材料柔性车间调度中的运输约束,以最小化完工时间为目标,建立调度模型,提出一种改进的双种群混合遗传算法进行求解。根据问题特点,基于工序排序、机器选择和运输约束3个子问题,设计三层实数编码以及对应解码方案。采用混合初始化提高种群质量,进化过程中采用交叉算子执行全局搜索,为双种群设计基于机器负载平衡和变邻域的局部搜索,提高全局和局部搜索能力。与对比算法相比10个测试算例中BPRD指标取得9个最优,APRD指标全部取得最优,t检验显著性有明显差异,验证算法的优越性。将算法应用于航空复合材料车间中,实现实际生产的调度,验证算法的可行性。

地下隐蔽工程地源热泵地埋管管群优化研究

地下建筑地源热泵系统向地下释放的热量常大于所提取的热量,容易造成地埋管区域的热量累积,影响其正常使用。因此,提高地埋管换热器效率,减弱冷热量失衡影响,是地下隐蔽工程应用地源热泵系统亟需解决的问题。文章基于换热器温差场均匀性原则,采用全局优化遗传算法,优化了地埋管换热器管群布置。结果表明:与传统等间距方案相比,优化布置后,16、25个钻孔方案的平均无因次温变热阻分别降低了13.8%和53.3%,平均换热效率分别提高了3.76%和5.74%,即钻孔数量越多,换热效率提升越明显。

中垂线遗传融合算法研究

为改善遗传算法的局部寻优性能和收敛速度,提出了一种将遗传算法和中垂线相算法结合的融合算法——中垂线遗传算法。中垂线遗传算法以遗传算法进行全局搜索,再以中垂线算法进行局部搜索。并将中垂线算法中的单一种群分化为双种群,将双种群中的优秀个体进行耦合交叉和变异,提升改进算法的全局搜索能力和跳出局部最优的能力。仿真实验讨论了算法转换系数的变化对改进算法性能的影响。通过与6个算法的对比实验,证明改进的中垂线遗传算法解决了传统遗传算法收敛速度慢和局部寻优能力弱的问题。并且与主流优化算法和其他遗传融合算法相比,改进的算法性能更加优越。最后,利用改进算法处理了三杆桁架的设计问题。结果表明:中垂线遗传算法在处理实际问题时具有可行性。

基于改进自适应多种群遗传算法的结构-控制系统一体化优化

提出一种改进的自适应多种群遗传算法,以更好地解决建筑结构-主动控制系统一体化优化问题,即同时对被控结构参数、控制算法参数、主动作动器布置位置进行优化。该遗传算法对编码方法、初始种群生成、选择策略、交叉概率和变异概率的自适应调整、多种群协同进化中移民策略等进行改进。研究结果表明:改进的自适应多种群遗传算法和改进的基本遗传算法优化结果总体一致,表明前者分析结果是正确的,并且具有较高的精度;改进的自适应多种群遗传算法和改进的基本遗传算法首次得到优化分析最优解的平均进化代数分别为320与730,表明前者比后者收敛速度更快;改进的自适应多种群遗传算法每次能达到或接近最优解,可有效克服基本遗传算法优化结果随机性较强的缺点;经改进的自适应多种群遗传算法优化的主动控制系统取得明显减振效果,E1 Centro波输入时,主动控制结构层间位移角峰值和绝对加速度峰值较无控时分别平均减小54.5%与46.7%。算例结果表明了改进的自适应多种群遗传算法的有效性,实现了对建筑结构-主动控制系统的一体化优化。