Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

Mathematical model and discrete artificial Bee Colony algorithm for distributed integrated process planning and scheduling

With the development of the globalization of economy and manufacturing industry, distributed manufacturing mode has become a hot topic in current production research. In the context of distributed manufacturing, one job has different process routes in different workshops because of heterogeneous manufacturing resources and manufacturing environments in each factory. Considering the heterogeneous process planning problems and shop scheduling problems simultaneously can take advantage of the characteristics of distributed factories to finish the processing task well. Thus, a novel network-based mixed-integer linear programming (MILP) model is established for distributed integrated process planning and scheduling problem (DIPPS). The paper designs a new encoding method based on the process network and its OR-nodes, and then proposes a discrete artificial bee colony algorithm (DABC) to solve the DIPPS problem. The proposed DABC can guarantee the feasibility of individuals via specially-designed mapping and switching operations, so that the process precedence constraints contained by the network graph can be satisfied in the entire procedure of the DABC algorithm. Finally, the proposed MILP model is verified and the proposed DABC is tested through some open benchmarks. By comparing with other powerful reported algorithms and obtaining new better solutions, the experiment results prove the effectiveness of the proposed model and DABC algorithm successfully.     

基于数据融合的ABC-SVM社区疾病预测方法

针对现有社区医疗服务中的疾病预测方法存在数据利用率低、疾病分析类型单一、自动化程度差、疾病预测效果不理想等不足,提出在物联网大数据环境下可用于社区医疗的健康数据融合及疾病预测方法. 通过主成分分析(PCA)和聚类分析对社区中居民的生理指标数据进行特征提取;结合人工蜂群(ABC)算法构造支持向量机(SVM)非线性分类器对数据进行特征级融合分析并预测潜在疾病. 实验结果表明,所提方法的疾病识别准确率达到93.10%,相较于传统SVM方法和BP神经网络方法分别提高17.24% 和72.41%. 该方法能够在提高数据利用率、降低计算资源消耗的前提下有效识别多种潜在疾病,可实现疾病早发现、早预防、早治疗;可广泛应用于社区健康管理、老年社区监护甚至临床医疗.     

Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation. However, it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced. In this paper, an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation. The efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique. Finally, instead of performing thresholding for each pixel independently, the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region. The final segmentation results is computed using graph cut, which produces smooth segmentation results. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases. Quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches.     

基于带约束人工蜂群算法和平均Hausdorff距离的重力匹配方法

重力匹配算法是实现重力辅助惯性导航系统的关键技术之一。但是,传统重力匹配方法存在复杂度高、应用范围小等缺陷,一般很难实现精确、快速匹配的效果。将人工蜂群算法用于重力匹配的搜索过程,并将多普勒测速仪提供的速度信息作为限制条件对蜂群搜索过程进行约束。在此基础上,利用平均Hausdorff距离对匹配结果进行筛选,在重力数据库中重力异常精度一定的条件下,可降低误配率。仿真结果表明,该匹配算法在重力特征显著的区域具有较高的匹配率,可以达到精确、快速的匹配定位,从而实现重力辅助导航。     

Mandibular gland components of european and africanized honey bee queens (Apis mellifera L.)

The composition of the five-component honey bee queen mandibular gland pheromone (QMP) of mated European honey bee queens was compared to those of virgin and drone-laying (i.e., laying only haploid unfertilized eggs that develop into males), European queens and Africanized mated queens. QMP of mated European queens showed significantly greater quantities of individual components than all queen types compared, except for a significantly greater quantity of 9-hydroxy-(E)-2-decenoic acid (9-HDA) found in Africanized queens. Glands of European drone-laying queens contained quantities intermediate between virgin and mated queens, reflecting their intermediate reproductive state and age. QMP ontogeny shifts from a high proportion of 9-keto-(E)-2-decenoic acid (ODA) in young unmated queens to roughly equal proportions of ODA and 9-HDA in mated queens. A biosynthetic shift occurs after mating that results in a greater proportion of 9-HDA, methylp-hydroxybenzoate (HOB), and 4-hydroxy-3-methoxyphenylethanol (HVA) production, accompanied by a decreased proportion of ODA. Africanized QMP proportions of ODA and 9-HDA were significantly different from European queens. A quantitative definition of a queen equivalent of QMP is proposed for the various queen types, and a standard queen equivalent for mated European honeybee queen mandibular gland pheromone is adopted as 200µg ODA, 80µg 9-HDA, 20µg HOB, and 2 µg HVA.     

受强弱关系理论启发的改进人工蜂群算法

针对原人工蜂群算法在寻优过程中存在收敛精度不高、容易陷入局部最优的问题,提出一种改进人工蜂群算法(SWT-ABC)。将社会学中强弱关系模型化并引入多子群矩阵式蜂群结构,定义了强关系个体从三个方向随机引导搜索,加快算法收敛速度和提高收敛精度;为增强算法跳出局部最优的能力,定义了弱关系个体交互以实现子群间信息交流来提升种群多样性;增加侦查蜂反向学习机制并确定合适的蜜源上限,能有效提升目标函数评价次数的利用效率。通过基准测试函数的数值实验并与12种改进算法进行对比,改进后的人工蜂群算法收敛精度更高、全局寻优能力更强,并且在高维优化问题求解中仍具备良好的收敛性能。     

基于遗传蜂群算法的运动想象BCI系统导联选择*

在运动想象脑-机接口系统中,常采用高密度导联获取脑电信号,导致实验准备时间长,系统运行速度慢,性能变差等问题。针对上述不足,本文提出了一种基于遗传算子的蜂群算法用于导联优选,引入交叉和变异算子以提高蜂群算法的邻域搜索能力。通过对第四届国际BCI 竞赛 Dataset 1中四名被试者(a,b,f和g)的59导联运动想象数据进行导联优选,用多类CSP算法和支持向量机对优选导联数据进行特征提取和分类识别。结果表明所提出的算法在大大降低了导联维数的同时,也得到了比采用全部导联更高的分类识别率,验证了本文所提算法的实用性和有效性。     

一种改进的人工蜂群算法

从经典人工蜂群算法机制出发,针对原始算法在初始种群构造、子种群分组、步长更新和种群淘汰方面的不足进行了改进.新算法运用均匀设计理论构造初始种群,提出了一种种群交叉的Z型分组方法,设计了一种对数函数自适应步长代替原来的随机步长,引入了小生境技术及时淘汰陷入局部最优的个体.实验结果表明,改进后的算法有效地解决了人工蜂群算法早熟收敛、搜索速度较慢等问题,并提高了解的精度.     

人工蜂群算法及应用新探简

人工蜂群算法是群体智能算法中新的分支.本文针对人工蜂群算法的建模思想和算法的框架结构设计方法进行了分析和研究,并针对实际问题编程完成问题的求解.实践表明,人工蜂群算法具有较高的灵活性和适应性.