改进的蚁群算法在溶质运移参数识别问题中的应用

非饱和土壤水分和溶质运移参数(扩散率、导水率和水动力弥散系数)取值范围较大,往往跨越几个数量级。采用传统离散化蚁群算法求解此类问题,所需节点较多,这会造成算法收敛时间较长。该文在传统蚁群算法基础上,对蚂蚁搜索路径进行改进,改进后的蚁群算法寻优路径由参数精度位数(整数位和小数位)、参数个数以及0–9十个数字构成,并将路径解码公式修改为具有判别参数正负功能的解码公式。采用改进的连续蚁群算法对非饱和溶质运移参数识别优化模型进行求解。数值模拟表明相同迭代次数下改进的蚁群算法比传统蚁群算法耗时少,算法计算时间与迭代次数满足线性关系,含水率和溶质浓度实测值与计算值吻合较好、相关性较高。     

On reactive routing protocols in ZigBee wireless sensor networks

ZigBee is the primary standard solution for wireless sensor networks, implementing the Ad hoc On‐Demand Distance Vector (AODV) protocol in the network layer and supported by the standard IEEE 802.15.4. This study is focused on mesh topologies and the critical problems encountered when AODV is executed in conjunction with the Carrier Sense Multiple Access with Collision Avoidance protocol. These problems are mainly related to the packet overhead required to carry out route creation. To perform preliminary experiments to be able to implement AODV in a real network, a new metric is proposed herein. This metric uses fuzzy logic to help in the decision‐making process. The objective of the fuzzy routine is to determine, during the route‐discovery process, the best node to forward request/reply packets, with the aim of reducing packet overhead and energy consumption. Moreover, minor changes are also added to the discovery procedure of AODV to improve the performance of the route‐creation process. This intelligent version of AODV has provided promising experimental results, greatly reducing the number of packets required, with the consequent energy saving while selecting the best nodes to be part of the routes.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

Occurrence patterns of alien freshwater turtles in a large urban pond ‘Archipelago’ (Rome,Italy): Suggesting hypotheses on root causes

The patterns of the occurrence and distribution of alien freshwater turtle species in an urban pond archipelago (Rome, Italy) were analysed, with the aim of exploring the role of a set of factors (type of ponds, landscape context, size area, distance from the nearest road) with a generalized linear model approach. A total of 311 ponds subdivided in three types (fountains, small basins, lakes) embedded in different landscape contexts (public parks, private parks, urban areas) at differing distances from the nearest road were sampled. Six non‐native freshwater turtle species in 31 sites were recorded (9.97%). Lakes exhibited the highest occurrence rates of alien freshwater turtles, compared to small basins and fountains. Freshwater turtle species in urbanized areas were only observed in parks (both public and private). In both the public and private parks, the lakes exhibited the highest percentage of occupied sites, with fountains being the lowest. A direct and significant relationship was observed between pond size and species richness. The distance from the nearest road did not appear to affect species richness. A first interpretation of the data from this study facilitated the postulation of two a posteriori hypotheses that should be tested, as follows: (i) the causal process of turtle release is random, and the rate of extinction (and recapture) is higher in smaller ponds, thus producing the observed pattern; and (ii) the turtle release is not random, and people actively select the ponds they consider more suitable for their pet animals. In this study, it appears the lakes were perceived by those who abandon their pets as the most ecologically suitable habitats among other pond types to accommodate the different species of turtles. Knowledge of people's attitudes in regard to releasing pet animals also might assist managers of public green spaces to develop strategies aimed to preserve local biodiversity, and to educate the public about the conservation issue represented by the alien species.     

生物陶瓷人工骨制备的新方法

主要介绍了近年来发展起来的两种新技术 :快速原型制造技术和放电等离子烧结技术。概括了两种技术的原理、特点以及在人工骨制备中的应用 ,提出了用这两种技术制备人工骨的设想。     

Development of large-scale stacked-type electrostatic actuators for use as artificial muscles

Electrostatic actuators have the advantages of light weight, flexibility, and high energy efficiency, which make them suitable for use as artificial muscles. However, a traditional electrostatic actuator cannot generate long strokes and a high force density at the same time because such actuator would excessively widen the gap between the electrodes because of its structure. This paper presents a newly developed large-scale stacked-type electrostatic actuator (LSEA) intended for use as an artificial muscle for robots. LSEA is a multi-stacked electrostatic actuator that can be linearly contracted by the application of a voltage. It has a unique structure that prevents overextension of the gap between the electrodes. It can therefore generate a large force. The spring characteristics and the relationship between the contractive force and the stroke were experimentally determined. The findings showed that LSEA prevents the overextension of the gap between the electrodes and has a high contraction ratio that is equivalent to that of a mammalian skeletal muscle.     

化学反应蚁群优化算法

针对化学反应优化对反馈信息利用不足导致后期求解效率低的问题,提出化学反应蚁群优化算法.该算法利用化学反应优化生成较优解,通过信息素转换策略将较优解转换为蚁群算法的初始信息素,最后由蚁群算法累积更新信息素得到最优解.以TSP为例进行仿真,结果表明,与化学反应优化、蚁群算法、模拟退火算法相比,所提算法具有更高的寻优能力、收敛效率和计算效率.     

一种混合的离散细菌菌落优化算法

为了利用细菌算法解决组合优化问题, 提出了一种混合的离散细菌菌落优化算法。根据现有细菌优化算法, 设计一种新的个体编码方式及进化模式, 通过设计种群的自适应调整因子增强个体活力, 并融合禁忌搜索算法, 克服算法易于陷入过早收敛的不足, 并与其他算法在Taillard标准调度测试问题集上比较实验, 验证了算法的有效性。仿真结果表明, 该算法可以搜索到问题的最优组合, 能够有效避免算法陷入局部最优, 取得了满意的结果。     

人工鱼群—粒子群混合算法优化进港航班排序

针对空中交通管理中的进港航班排序问题, 提出了人工鱼群—粒子群混合算法(AFPSO)这一航班排序算法来优化进港航班排序, 使时段内进港航班队列总延误时间最少。算法结合了基本人工鱼群算法(AFSA)和基本粒子群算法(PSO)各自的优点, 先以AFSA在全局寻找满意的解域, 再以PSO算法在这些解域中进行快速的局部搜索获得精确解, 最终使算法提高收敛速度和搜索精度。仿真结果表明, 在单跑道和双跑道情况下, AFPSO算法使得航班队列总延误时间比FCFS调度方法减少了20. 9%和34. 4%, 比基本AFSA减少了3. 2%和3. 5%。算法得到的满意解能够为自动化空中交通管理提供实时支持。     

云中基于蚁群算法改进的负载均衡策略

针对云计算虚拟化资源中,提高资源利用率、负载均衡度的问题,在蚁群算法的基础上,提出云中节点间负载均衡的改进算法。前向蚂蚁检测节点的类型、记录节点信息,遇到负载节点时留下觅食信息素;后向蚂蚁依据循迹信息素追溯回负载节点,合理分配超载节点任务。所有蚂蚁不再更新自己的结果集,而是致力于更新单个结果集,在搜索过程中依据节点类型动态地修改路径信息素。在Cloudsim平台下进行的仿真实验验证了改进算法的有效性。