Differential Evolution Algorithm Based on Ensemble of Constraint Handling Techniques and Multi-Population Framework

Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.

Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution（DE） algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

Massive Molecular Parallel Evolution of the HSP90AA1 Gene between High-elevation Anurans

HSP90 AA1 is part of the heat shock protein 90 gene family and has important functions against heat stress. We report a case of molecular level parallel evolution of the HSP90 AA1 gene in high elevation amphibians. HSP90 AA1 gene sequences of four high-elevation anurans, Bufo gargarizans, Nanorana parkeri, Rana kukunoris, and Scutiger boulengeri, were compared along with five of their low-elevation relatives. A total of 16 amino-acid sites were identified as parallel evolution between N. parkeri and R. kukunoris. We generated both model based（Zhang and Kumar＇s test） and empirical data based（parallel/divergence plotting） null distributions for non-parallel evolution, and both methods clearly determined that the observed number of parallel substitutions were significantly more than the null expectation. Furthermore, on the HSP90 AA1 gene tree, N. parkeri and R. kukunoris formed a strongly supported clade that was away from their respective relatives. This study provides a clear case of molecular parallel evolution, which may have significant implications in understanding the genetic mechanisms of high-elevation adaptation.

Hybrid Support Vector Regression with Parallel Co-Evolution Algorithm Based on GA and PSO for Forecasting Monthly Rainfall

Accurate and timely monthly rainfall forecasting is a major challenge for the scientific community in hydrological research such as river management project and design of flood warning systems. Support Vector Regression (SVR) is a very useful precipitation prediction model. In this paper, a novel parallel co-evolution algorithm is presented to determine the appropriate parameters of the SVR in rainfall prediction based on parallel co-evolution by hybrid Genetic Algorithm and Particle Swarm Optimization algorithm, namely SVRGAPSO, for monthly rainfall prediction. The framework of the parallel co-evolutionary algorithm is to iterate two GA and PSO populations simultaneously, which is a mechanism for information exchange between GA and PSO populations to overcome premature local optimum. Our methodology adopts a hybrid PSO and GA for the optimal parameters of SVR by parallel co-evolving. The proposed technique is applied over rainfall forecasting to test its generalization capability as well as to make comparative evaluations with the several competing techniques, such as the other alternative methods, namely SVRPSO (SVR with PSO), SVRGA (SVR with GA), and SVR model. The empirical results indicate that the SVRGAPSO results have a superior generalization capability with the lowest prediction error values in rainfall forecasting. The SVRGAPSO can significantly improve the rainfall forecasting accuracy. Therefore, the SVRGAPSO model is a promising alternative for rainfall forecasting.

Simulation Test for Evolution Laws of Tensile Fractures in a Coal Mining Area

In order to study evolution laws of tensile fractures in a coal mining area, based on the classification of the fractures formed by mining, a physical simulation test was carried out to simulate the dynamic evolution process of tensile fractures in coal mining areas. The results showed that after the coal in the mining area was mined, the mining area underwent obvious movement and deformation and forms tensile fractures. As the min-ing working face was advanced, the tensile fractures underwent the dynamic process of generation, development and closure. The changing curves of density of tensile fractures with the increase of mining length of the working face liked a ladder （it increased slowly and then rapidly） and then had two peaks （the second peak was higher than the first peak）.

Numerical Investigation of the Multiphase Flow Originating from the Muzzle of Submerged Parallel Guns

A two-dimensional model,employing a dynamic mesh technology,is used to simulate numerically the transient multiphaseflowfield produced by two submerged parallel guns.After a grid refinement study ensuring grid inde-pendence,five different conditions are considered to assess the evolution of cavitation occurring in proximity to the gun muzzle.The simulation results show thatflow interference is enabled when the distance between the par-allel barrels is relatively small;accordingly,the generation and evolution of the vapor cavity becomes more com-plex.By means of the Q criterion for vorticity detection,it is shown that cavitation causes the generation of vorticity and the evolution of the vapor cavity can result in an asymmetric distribution of vorticity for a certain distance of the barrels.In particular,the evolution of the vapor cavity can hinder the expansion of the gas and force it toflow outward,while an asymmetric distribution of vorticity can lead to a gas jetflowing outward and rotating simultaneously.

无伴随运动2PRU-PRUR并联机构高维多目标优化设计

设计了一种无伴随运动的面对称一移动两转动2PRU-PRUR并联机构,应用旋量理论分析了机构自由度和运动特性。将末端执行器输出轴设置为偏置形式,可提高姿态能力。采用球面姿态角(方位角和倾摆角)描述该输出轴姿态,并推导出机构逆向位置符号解。以旋量理论为数学工具,给出局部和全域传递性能指标的定义和计算方法。在机构性能评价和工作空间分析的基础上,建立了机构尺度参数昂贵约束高维多目标优化模型,并采用多目标进化算法求解该问题。同时,为提高高维多目标优化算法在解决此模型时的性能,提出了一种矢量角高维多目标差分进化(Vector-angle-based many-objective Differential Evolution,VaDE)算法,可综合平衡个体的收敛性和分布性。应用VaDE求解机构高维多目标优化问题,得到多组Pareto备选解。研究结果可为机构尺度综合和实际应用提供参考。

3-SRR腿履式调姿救援机器人优化设计与试验

为实现在复杂环境下对处于不同位姿伤员的精准施救及稳定转运,文中提出了一种3-SRR腿履式调姿救援机器人机构,并进行运动学分析、机构优化设计及试验研究。首先,基于闭环矢量法对其进行运动学分析,得到救援机器人运动学反解;然后,基于迭代搜索算法,得到其救援作业空间与姿态空间,并基于单一变量法分析救援机器人机构尺寸参数对救援作业空间的影响;然后,以救援作业空间最大、姿态能力最强为优化目标函数,基于差分进化算法对机构尺寸参数进行优化;最后,研制原理样机并进行调姿能力试验,试验结果证明了方案的可行性与理论分析的正确性,为灾难救援提供一种可行的解决方案。

大规模定制家具加工中心多钻头并行作业的优化

大规模定制家具超高的生产效率与高度个性化的产品两大特征决定了其需要依赖高水平的信息化与自动化组织生产,是家具制造业智能制造的前沿领域。运用数控加工中心开展钻孔作业是调和个性化产品制造过程中的矛盾、实现柔性生产的重要手段。当前,数控钻孔工序因其作业时间长且板件之间差异较大而往往成为制造过程中的瓶颈。为切实提高生产效率,本研究立足生产实际,从钻头与孔的位置关系中寻求突破口,提出了数控加工中心多钻头并行作业优化问题。以优化钻头排列为主要途径,减少下钻次数为核心目标,基于制造大数据与工艺规则挖掘信息并简化建模,采用差分进化算法求解钻头排列方案,进一步通过聚类算法探索出面向未来高自动化水平下的差异化钻头排列,形成了一套具有实际意义与普适性的优化方法。通过理论与实践验证了该方法的有效性,达到了缩减作业时间、提升加工效率的目的。对打通大规模定制家具制造瓶颈、推动定制家具智能制造具有一定的指导意义。

超空泡射弹异步并联倾斜入水空泡演化与运动特性分析

基于VOF多相流模型与重叠网格技术建立了超空泡射弹异步并联倾斜入水数值方法,利用单发射弹倾斜入水实验对数值方法的有效性进行了验证,在此基础上,分别对不同初始间距及入水顺序下前后两射弹的空泡演化与运动特性进行了数值模拟与分析。研究结果表明:对于前发射弹,在初始间距为0.5倍弹长时,由于受到后发射弹空泡剧烈挤压,前发射弹无法产生完整空泡包裹弹体,运动受到严重影响并最终失稳;同时,前发射弹自上侧入水与自下侧入水相比,其空泡受到的挤压程度更大,沾湿更早且失稳更快;随着初始间距的增大,前发射弹附近空泡受后发射弹的影响减弱,2种入水顺序下均运动平稳。对于后发射弹,由于前发射弹空泡的持续作用,其空泡始终不对称,运动稳定性受到影响,在间距为0.5倍弹长时向内侧偏转失稳,在间距为1倍弹长时运动平稳,间距为2倍和3倍弹长时向外侧偏转失稳,且2种入水顺序下运动规律基本一致。

反常电子粘滞性引起的双撕裂模非线性演化

[目的]在常规电阻撕裂模方程的基础上加入反常电子粘滞性,用圆柱位形的磁流体动力学,对由反常电子粘滞性引起的双撕裂模的非线性发展做了数值研究。[方法]通过对非线性发展阶段磁岛和磁力线的演化分析,详细研究了两个有理面间距分别为较大、中等、较小3种情况下,由反常电子粘滞性引起的双撕裂模非线性发展的不同阶段的磁场拓扑结构的变化以及相关动力学特征,探讨了非线性发展的驱动问题。[结果]研究表明,两个有理面的间距的大小,对双撕裂模非线性发展有很大影响。间距中等时,会发生快速磁重联,对位形破坏最快最大。[结论]研究结果对托卡马克的位形设计和运行时的控制提供了参考。

移动机器人差分进化PID控制仿真研究

这里提出了一种两轮差速底盘的并联式PID控制策略,基于提出的补偿跟踪器,采用差分进化算法搜索最优PID参数,从而快速且准确地控制底盘轨迹跟踪运动。这里首先建立了两轮差速底盘的数学模型,针对其中的未知非线性部分,采用补偿跟踪器进行观测。然后基于差分进化算法,搜索最优PID参数,利用两个并联的PID控制器同时控制底盘的位置与旋转角度,实现给定轨迹的追踪。通过对控制器仿真分析,差分进化算法可以获取最优PID参数,并得出x,y方向位置阶跃响应的响应时间为0.339s、0.312s,稳态误差为0.0018m、0.0013m,回转角度正弦响应平均误差为0.045rad。最后,对随机轨迹进行跟踪仿真,结果表明底盘可以准确而快速地跟踪轨迹,该控制策略可以有效改善系统性能,具有良好的跟踪特性。

基于CFD的并列超空泡射弹高速斜入水流体动力特性研究

在对水下目标连续、密集的打击过程中常常涉及到多体间的运动干扰和空泡非定常耦合等问题,使得流场结构和运动特性变得复杂。为分析并行超空泡射弹高速入水过程中流场的相互干扰规律,采用CFD方法建立并列弹高速斜入水流体动力特性研究数值模型,同时将并列弹与单个弹的计算结果进行对比,明确并列弹高速入水空泡形态和流场分布与单个弹的差异,并进一步给出弹间干扰特性随并列弹轴线间距的变化规律。计算结果表明:较单个弹入水过程,随着轴线间距的减小,并列弹的流体动力特性变化复杂;当轴线间距增大至G=4时,并列弹的流体动力特性与单个弹差异不再显著。并列弹高速入水时,双空泡形态呈现镜面对称特性,内侧空泡受挤压贴近弹体表面,随轴线间距的增加,空泡间的干扰作用逐渐减弱;高压区和低速区主要存在于射弹头部,且轴线间距较小时,出现高压区重叠现象;随着轴线间距减小,内侧空泡压力、速度分布对空泡演化的影响程度在入水深度较深位置更为明显。由于射弹表面内外侧压差的作用,使射弹姿态产生显著变化,表现为弹头相互排斥,弹尾靠拢。

中国“并联式”现代化的深圳实践及演进逻辑

深圳经济特区在推进改革开放和分步走实现社会主义现代化战略安排的各个阶段,分别扮演着“探路”“领跑”“冲刺”的角色。同时,与我国工业化、城镇化、农业现代化和信息化叠加发展的“并联式”现代化模式相适应,深圳现代化实践的叠加发展及阶段性特征更为明显,其“起步”“跨越”“加速”的演进逻辑也较为清晰。基于此视角的分析,有助于充分了解深圳对全国改革开放和社会主义现代化建设的重要窗口和示范带动作用。

Parallel Processing Based on Ship Maneuvering in Identification of Interaction Force Coefficients

The parallel processing based on the free running model test was adopted to predict the interaction force coefficients （flow straightening coefficient and wake fraction） of ship maneuvering. And the multipopulation genetic algorithm （MPGA） based on real coding that can contemporarily process the data of free running model and simulation of ship maneuvering was applied to solve the problem. Accordingly the optimal individual was obtained using the method of genetic algorithm. The parallel processing of multiopulation solved the prematurity in the identification for single population, meanwhile, the parallel processing of the data of ship maneuvering （turning motion and zigzag motion） is an attempt to solve the coefficient drift problem. In order to validate the method, the interaction force coefficients were verified by the procedure and these coefficients measured were compared with those ones identified. The maximum error is less than 5%, and the identification is an effective method.

考虑舒适性的自动驾驶轨道列车牵引电机节能控制

为提升乘客在自动驾驶轨道列车中的体验感,保证自动驾驶轨道列车的安全运行,提出考虑舒适性的自动驾驶轨道列车牵引电机节能控制技术。构建自动驾驶轨道列车运动学模型,分析牵引和制动电机能耗。依据分析结果建立列车牵引电机节能控制目标函数,并以行驶速度、舒适性、准时性和精确停车设置约束条件。利用混沌算法和蛙跳算法改进标准差分进化算法求解目标函数,获取最优解,实现自动驾驶轨道列车牵引电机节能控制。实验结果表明,所提方法在预期速度跟踪性能、节能性、舒适性、精准停车和准点中表现更为优异,验证了所提方法应用下的自动驾驶轨道列车在满足乘客舒适性要求的基础上,获取了较好的牵引电机节能控制效果。

Comprehensive phylogenetic analyses of Orchidaceae using nuclear genes and evolutionary insights into epiphytism

Orchidaceae(with>28,000 orchid species)are one of the two largest plant families,with economically and ecologically important species,and occupy global and diverse niches with primary distribution in rainforests.Among orchids,70%grow on other plants as epiphytes;epiphytes contribute up to~50%of the plant diversity in rainforests and provide food and shelter for diverse animals and microbes,thereby contributing to the health of these ecosystems.Orchids account for over two-thirds of vascular epiphytes and provide an excellent model for studying evolution of epiphytism.Extensive phylogenetic studies of Orchidaceae and subgroups have;been crucial for understanding relationships among many orchid lineages,although some uncertainties remain.For example,in the largest subfamily Epidendroideae with nearly all epiphytic orchids,relationships among some tribes and many subtribes are still controversial,hampering evolutionary analyses of epiphytism.Here we obtained 1,450 low-copy nuclear genes from 610 orchid species,including 431 with newly generated transcriptomes,and used them for the reconstruction of robust Orchidaceae phylogenetic trees with highly supported placements of tribes and subtribes.We also provide generally wellsupported phylogenetic placements of 131 genera and 437 species that were not sampled by previous plastid and nuclear phylogenomic studies.Molecular clock analyses estimated the Orchidaceae origin at~132 million years ago(Ma)and divergences of most subtribes from 52 to 29 Ma.Character reconstruction supports at least 14 parallel origins of epiphytism;one such origin was placed at the most recent common ancestor of~95%of epiphytic orchids and linked to modern rainforests.Ten occurrences of rapid increase in the diversification rate were detected within Epidendroideae near and after the K-Pg boundary,contributing to~80%of the Orchidaceae diversity.This study provides a robust and the largest family-wide Orchidaceae nuclear phylogenetic tree thus far and new insights into the evolution of epiphytism in vascular plants.

CoFeO_(x)/MoO_(2)准平行纳米阵列电极的析氢性能

利用CoFe层状双金属氢氧化物(CoFe LDH)准平行纳米片阵列作为载体前驱体,通过原位负载及煅烧方式,实现了含有氧空位的MoO_(2)纳米颗粒在纳米片阵列表面的生长。电化学研究结果表明,所得CoFeO_(x)/MoO_(2)纳米阵列电极具有高析氢反应(HER)催化活性。该电极在10和1000 mA·cm^(-2)时的HER过电位分别为40和217 mV。在50 mA·cm^(-2)的电流密度下,该电极可以稳定运行125 h。

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by rea- sonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimiza- tion problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm （MPEA） framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is pro- posed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution （CC） algorithm combined with non-dominated sorting genetic algorithm II （NSGA-II） is intro- duced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multi- objective genetic algorithm （MOGA）, multi-objective evolutionary algorithm based on decom- position （MOEA/D）, CC-based multi-objective algorithm （CCMA） as well as other two MPEAs with different migration interval strategies.

基于自适应权重调整与差分进化策略的并行式混合蛙跳算法

针对标准混合蛙跳算法(SFLA)在复杂优化问题中出现的收敛速度慢、求解精度不高和运行效率低等问题,提出了一种基于自适应权重调整与差分进化(DE)策略的并行式混合蛙跳算法(P-DE-ASFLA)。在局部搜索过程中,采用邻近学习策略更新子群中的最优个体以加快算法的收敛;采用动态蛙跳规则更新子群中的最差个体以避免算法早熟收敛;在全局搜索过程中,采用DE策略对混合后的种群进行基因更新,增强算法的全局寻优能力。同时基于主从式并行架构,采用多进程技术使子群的局部搜索过程并行化,大幅提高了算法的运行效率。实验结果表明,所提算法在6个标准测试函数中的求解质量和运行效率要远优于标准SFLA和DE算法。