Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Applying Network Flow Optimization Techniques to Improve Relief Goods Transport Strategies under Emergency Situation

Given the seriously damaged emergency situation occurring after a large-scale natural disaster, a critical and important problem that needs to be solved urgently is how to distribute the necessary relief goods, such as drinking water, food, and medicine, to the damaged area and how to transport them corresponding to the actual supply and demand situation as quickly as possible. The existing infrastructure, such as traffic roads, bridges, buildings, and other facilities, may suffer from severe damage. Assuming uncertainty related with each road segment’s availability, we formulate a transshipment network flow optimization problem under various types of uncertain situations. In order to express the uncertainty regarding the availability of each road segment, we apply the Monte Carlo simulation technique to generate random networks following certain probability distribution conditions. Then, we solve the model to obtain an optimal transport strategy for the relief goods. Thus, we try to implement a necessary and desirable response strategy for managing emergency cases caused by, for example, various natural disasters. Our modeling approach was then applied to the actual road network in Sumatra Island in Indonesia in 2009, when a disastrous earthquake occurred to develop effective and efficient public policies for emergency situations.

Motion simulation of moorings using optimized LSTM neural network

Mooring arrays have been widely deployed in sustained ocean observation in high resolution to measure finer dynamic features of marine phenomena.However,the irregular posture changes and nonlinear response of moorings under the effect of ocean currents face huge challenges for the deployment of mooring arrays,which may cause the deviations of measurements and yield a vacuum of observation in the upper ocean.We developed a data-driven mooring simulation model based on LSTM(long short-term memory)neural network,coupling the ocean current with position data from moorings to predict the motion of moorings,including single-step output prediction and multi-step prediction.Based on the predictive information,the formation of the mooring array can be adjusted to improve the accuracy and integrity of measurements.Moreover,we proposed the cuckoo search(CS)optimization algorithm to tune the parameters of LSTM,which improves the robustness and generalization of the model.We utilize the datasets observed from moorings anchored in the Kuroshio Extension region to train and validate the simulation model.The experimental results demonstrate that the model can remarkably improve prediction accuracy and yield stable performance.Moreover,compared with other optimization algorithms,CS is more efficient and performs better in simulating the motion of moorings.

Solving algorithm for TA optimization model based on ACO-SA

An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.

Estimating the macrobenthic species richness with an optimized sampling design in the intertidal zone of Changjiang Estuary

It is a challenge in the field sampling to face conflict between the statistical requirements and the logistical constraints when explicitly estimating the macrobenthos species richness in the heterogeneous intertidal wetlands. To solve this problem, this study tried to design an optimal, efficient and practical sampling strategy by comprehensively focusing on the three main parts of the entire process(to optimize the sampling method, to determine the minimum sampling effort and to explore the proper sampling interval) in a typical intertidal wetland of the Changjiang(Yangtze) Estuary, China. Transect sampling was selected and optimized by stratification based on pronounced habitat types(tidal flat, tidal creek, salt marsh vegetation). This type of sampling is also termed within-transect stratification sampling. The optimal sampling intervals and the minimum sample effort were determined by two beneficial numerical methods: Monte Carlo simulations and accumulative species curves. The results show that the within-transect stratification sampling with typical habitat types was effective for encompassing 81% of the species, suggesting that this type of sampling design can largely reduce the sampling effort and labor. The optimal sampling intervals and minimum sampling efforts for three habitats were determined: sampling effort must exceed 1.8 m^2 by 10 m intervals in the salt marsh vegetation, 2 m^2 by 10 m intervals in the tidal flat, and 3 m^2 by 1 m intervals in the tidal creek habitat. It was suggested that the differences were influenced by the mobility range of the dominant species and the habitats' physical differences(e.g., tidal water, substrate, vegetation cover). The optimized sampling strategy could provide good precision in the richness estimation of macrobenthos and balance the sampling effort. Moreover, the conclusions presented here provide a reference for recommendations to consider before macrobenthic surveys take place in estuarine wetlands. The sampling strategy, focusing on the three key parts of the sampling design, had a good operational effect and could be used as a guide for field sampling for habitat management or ecosystem assessment.

Antenna geometry strategy with prior information for direction-finding MIMO radars

The antenna geometry strategy for direction finding （DF） with multiple-input multiple-output （MIMO） radars is studied. One case, usually encountered is practical applications, is consi- dered. For a directional antenna geometry with a prior direction, the trace-optimal （TO） criterion （minimizing the trace） on the av- erage Cramer-Rao bound （CRB） matrix is employed. A qualitative explanation for antenna geometry is provided, which is a combi- natorial optimization problem. In the numerical example section, it is shown that the antenna geometries, designed by the proposed strategy, outperform the representative DF antenna geometries.

基于“仿真—半实物仿真—实物”三阶段开发流程的DAB变换器实验探索与研究

该文以双有源桥DC-DC变换器为对象,详细分析了变换器在三重移相调制下的两种数学模型,又基于这两种数学模型提出了对应的电感电流优化控制策略,搭建了包含MATLAB/Simulink仿真平台、StarSim半实物仿真平台和实物平台的三阶段实验平台,在平台中通过实验验证了优化控制策略的可行性和有效性。所设计的三阶段开发流程能够加深学生对电力电子领域相关知识的理解,逐步提升学生的理论分析能力、仿真验证能力和实践操作能力。

某酒店制冷机房运行策略优化

对华东地区某酒店制冷机房运行策略进行了优化,利用能耗模拟软件对多种运行方案进行了仿真分析。结果表明:原方案仅对冷水泵采用变频控制,制冷机房能效比为4.62;优化方案1采取传统变频方式,对冷水泵、冷却水泵、冷却塔风机采用变频控制,制冷机房能效比达到5.32,与原方案相比提升了15.1%;优化方案2通过对冷水机组、冷水泵、冷却水泵、冷却塔风机进行优化控制,制冷机房能效比为5.48,与原方案相比能效比提升了18.6%,且每年可减少约79 t碳排放量,节能效果显著。

基于深度强化学习的立体投送策略优化方法研究

基于深度强化学习算法在策略优化问题中的良好表现,以立体投送作战行动为主要研究对象,提出了一种深度强化学习框架与仿真推演实验协同的作战行动策略优化方法。在分析策略优化研究现状的基础上,根据研究问题对深度学习框架进行了分析比较,构建了基于A3C算法的深度强化学习立体投送策略模型,并通过仿真推演和分布式计算,实现深度强化学习模型与“人不在回路”仿真推演的交互学习,获得优化后的立体投送策略,验证了深度强化学习框架与仿真推演实验协同优化策略的有效性。

自适应状态转移模拟退火算法及其应用

状态转移模拟退火算法(STASA)作为解决复杂优化问题的有效方法,其搜索效率依赖于搜索算子和参数值的选择,在一些高维复杂问题上出现效率低下的问题。提出一种自适应状态转移模拟退火算法(ASTSA),通过自适应算子和参数选择策略来提高算法的适用性和求解效率;借鉴群智能算法的均值更新方法对平移算子进行改进,增强算子的搜索特性。通过23个基准测试函数和8个工程设计问题进行实验验证并与其他算法对比,证明了ASTSA算法和改进策略的有效性。

参数化性能模拟的福清传统民居光环境优化策略

为提升传统民居光环境品质,对福清城区岑兜村代表性传统民居进行实测,结果表明该民居大部分室内的采光性能不满足设计标准需求.利用参数化模拟工具对传统民居的光环境建立数字模型进行分析,分别从玻璃透射率、室内反射比、增设内墙开窗和顶部采光口进行单项优化,得出均满足不了采光需求的结论.进而在对整体建筑风貌影响最小化的前提下,提出综合优化设计策略以改善民居室内光环境,为地方传统民居改造更新提供借鉴.

求解动态旅行商问题的蚁群优化算法新策略

动态旅行商问题是标准旅行商问题的一个扩展,由于其现实应用广泛,吸引了大量研究者的兴趣。蚁群优化算法可以转化历史环境信息,天然具有适应动态改变的能力,可以解决动态旅行商问题。使用蚁群优化算法解决优化问题时,算法探索能力和利用能力的权衡是一个关键问题。传统的思路是在搜索前期侧重探索能力,使蚁群充分获取搜索空间的信息,随着搜索过程的进行逐渐增强利用能力,使蚁群逐渐收敛。然而,以上思路不利于在动态场景中快速获得质量较高的解。针对动态旅行商问题,提出了一种新的探索-利用权衡策略,在环境变化后,首先使用模拟退火算法增强利用能力以快速获得质量较高的解,在解质量难以提高时再使用自适应性轮盘赌选择方法帮助算法跳出局部极值。在权重变化的动态旅行商问题上的实验证明,所提新策略优于其它蚁群优化算法及变体。

航线网络仿真模型在远洋运输中的应用

针对航运业长期存在的提升船舶运输效率和控制船舶运营成本的问题,基于AnyLogic仿真平台,结合代理人基模型(Agent-Based Model,ABM)和离散事件模拟(Discrete Event Simulation,DES)方法,考虑船舶出港、海上航行、到港装卸货和燃料加注等环节,建立航线网络仿真模型。通过单船-单线案例试验和船队-航线网络优化试验验证该模型的有效性,结果表明:通过优化航速和燃料策略,能显著降低船舶运输成本及其对环境的影响;该模型能模拟和应对市场波动及突发事件,提升航运公司的突发事件应急响应能力和市场适应性。研究结果可为航运企业解决船舶营运问题提供新的视角和方案,对航运企业决策模式的发展产生积极影响。

基于空间句法的公园滨水空间分析与优化策略——以呼和浩特市青城公园为例

滨水空间是塑造城市性格、提升城市空间价值和活力的重要元素。采用Depthmap10.0软件对呼和浩特市青城公园的滨水空间进行了人流可达性、视域可达性、人流模拟等分析,发现了公园内部特定空间应有的可达性与交通吸引力不相匹配问题,找到了其可达性与视域整合度重合度过高而导致的局部人流量过大引发的交通堵塞的动因,最终针对上述问题提出了内部空间优化的策略。

嵌套霍尔推力器的热优化策略研究

嵌套霍尔推力器的高温特性是影响其性能与可靠性的重要因素。为研究嵌套霍尔推力器的热优化策略,本文采用引入辐射传递系数的导热微分方程来描述推力器的传热过程,并在真空舱内开展50 kW级嵌套霍尔推力器的测温验证试验,验证和修正数值模型,修正后的模型计算误差在4.8%。在此基础上,利用数值模型针对三种热优化策略下的嵌套霍尔推力器温度分布进行求解,获得各个优化策略下的温度变化规律与机理。结果表明,同时采取三种优化策略下,温度最高导磁零件的温度可降低约90 K;三种优化策略中,添加散热片是最显著的降温策略,而改变放电室支撑柱厚度与更换放电室外壁表面处理的优化效果略有下降。

改进模拟退火算法的低碳车辆路径规划

在城市垃圾量急剧增加的背景下,为解决清运车辆过度使用导致碳排放持续增加问题,提出一种垃圾清运车辆低碳路径优化方法。在考虑车辆容量限制和时间窗约束的情况下,综合多个清运过程影响因素优化目标函数,建立以路径最优、成本极小化为目标的数学模型。设计了改进模拟退火算法求解模型,改进策略包括使用K-means聚类算法协助模拟退火算法,加入2-shift法、2-symmetry法和2-insert法,使算法在全局搜索和局部搜索达到平衡,在邻域搜索上引入随机均匀采样策略,避免因穷举带来的时间复杂度过高问题。多个经典算例的仿真实验结果表明,改进模拟退火算法对优化车辆低碳路径规划模型具有较好的收敛速度和鲁棒性。

基于规则和仿真的交通信号多目标优化方法

城市交通信号优化是个多目标优化问题,以城市路段和路口交通管控问题为导向,基于规则库和仿真支撑,建立一套面向不同管控策略的交通信号优化方法。首先,建立配时问题成因分析库,梳理出6类控制策略和17项控制手段,以仿真分析、电警过车数据、浮动车数据等为基础,提出各类成因诊断指标和判定条件,针对不同交通场景提出初始管控思路,形成若干套信号控制方案作为方案备选集。其次,通过微观仿真测试方案备选集,以排队长度为约束条件,总通过量、延误和停车为优化目标,得到满足安全和秩序要求的可行解集,在不同管控需求前提下选择最优方案作为战略级信号配时方案。最后,以深圳市新洲路为例,验证本研究提出的方法的可靠性和实际场景下的优化效果。

基于ISCSO-BP-PID的SMB组分纯度模糊解耦控制方法研究

针对模拟移动床色谱分离系统中存在的强耦合、多变量、非线性和时滞等问题,提出了一种基于改进沙猫群优化算法的BP神经网络自调整PID参数的模拟移动床组分纯度模糊解耦控制方法。该方法首先通过模糊解耦消除了A、B组分纯度控制回路之间的耦合,然后结合改进的沙猫群优化算法和BP神经网络,实现了PID参数的自适应调整,从而有效控制A、B组分的纯度。在改进的沙猫群优化算法中,引入了Cubic混沌映射来初始化沙猫种群,以提高种群分布的均匀性;在搜索猎物阶段加入了可变螺旋搜索策略,使沙猫群拥有更多的搜索路径来调整自身位置;同时,融合了麻雀搜索算法的警戒机制,以加速算法的收敛速度。通过对12个CEC2022测试函数进行验证,证明了改进沙猫群优化算法的有效性。仿真结果表明,所提方法不仅能够有效消除A、B组分纯度控制回路间的耦合效应,而且在各个实际应用场景中均展现出卓越的性能。与传统的PID控制方法相比,在流量突变情况下,调节时间分别缩短了75.40%和77.57%,超调量分别减少了91.84%和81.96%。该方法具备较强的抗干扰能力和良好的鲁棒性,显著改善了整个系统的控制性能。

基于行人仿真的历史文化街区步行空间优化策略研究

由于缺乏适应新时代背景的理论支持和科学指导,传统历史文化街区内步行空间规划难以完全满足当代城市发展的需求。随着行人仿真技术的不断进步,能够对步行空间与行人行为之间的关系进行更精确的定量分析,从而实现步行空间设计的优化。文章通过调研、收集昙华林历史文化街区的基础现状和公共服务设施分布等信息,并获取了行人步行特征及空间热力特征等数据。运用基于社会力模型的Legion行人仿真软件对这些数据进行建模、仿真模拟、量化分析。研究结果显示,通过优化后的步行空间设计,能够实现行人流量的均衡分布,减少拥堵点的出现,并能有效引导人流到历史建筑等重要景点。