免疫遗传算法的混合动力汽车多目标优化

以混合动力汽车传动系统参数与控制策略参数为优化变量,以最小燃油消耗和尾气排放量（CO+HC+NOx）为优化目标,以动力性能与电池荷电状态平衡作为约束条件,建立多目标优化模型,并使用权重系数法将多目标函数优化问题转化为单目标问题。提出了基于免疫遗传算法优化混合动力汽车参数的优化方法,该算法采用实数编码,通过调用ADVISOR的后台函数,建立联合优化仿真模型。仿真结果表明,该算法可有效降低车辆的燃油消耗,减少CO与HC排放量,能够较好地解决带有约束的混合动力汽车的多目标多参数优化问题,可以获得一组具有低油耗与低污染物排放的传动系统与控制策略参数,供决策者选择。     

Comparing a multiobjective optimization algorithm for discovering driving strategies with humans

When a person drives a vehicle along a route, he/she optimizes two objectives, the traveling time and the fuel consumption. Therefore, the task of driving can be viewed as a multiobjective optimization problem and solved with appropriate optimization algorithms. The comparison between the driving strategies obtained by humans and those obtained by the algorithms is interesting from several points of view. For example, it is interesting to see which strategies are better. To perform the human versus machine test, we compared the driving strategies obtained by the multiobjective optimization algorithm for discovering driving strategies (MODS) with those obtained by a group of volunteers operating a vehicle simulator. The test was performed using data from three real-world routes. The results show that MODS always finds better driving strategies than the volunteers, especially when the fuel consumption is to be reduced. Moreover, the results show that some volunteers always drive similarly in terms of traveling time and fuel consumption while others significantly vary their driving strategies.     

Energy-efficient operation of diesel–electric locomotives using ahead path data

Diesel–electric locomotives have significant fuel consumption. In this study, fuzzy look-ahead control is considered as an online approach for fuel consumption optimization. A fuzzy controller will modify the desired speed profile by accounting for the gradient and speed limits of the path ahead. Journey time increment is used as an optimization constraint. The existing models for train motion simulation are calculating the fuel consumption by an indirect index. A new model for train-movement simulation is proposed to calculate fuel consumption more accurately. This model considers the locomotive subsystems and satisfies the experimental fuel consumption data specified in the locomotive's catalog. Simulation of a train with a GM Sd40-2 on three local tracks showed considerable reduction in fuel consumption along with an acceptable journey time increment. Simulation results also showed that fuzzy look-ahead controller has very faster calculations in comparison with the controller based on the dynamic programming method.     

A fast evaluation strategy for evolutionary algorithms

Evolutionary algorithms (EAs) are a popular and robust strategy for optimization problems. However, these algorithms may require huge computation power for solving real problems. This paper introduces a “fast evolutionary algorithm” (FEA) that does not evaluate all new individuals, thus operating faster. A fitness and associated reliability value are assigned to each new individual that is only evaluated using the true fitness function if the reliability value is below a threshold. Moreover, applying random evaluation and error compensation strategies to the FEA further enhances the performance of the algorithm. Simulation results show that for six optimization functions an average reduction of 40% in the number of evaluations was observed while obtaining similar solutions to those found using a traditional evolutionary algorithm. For these same functions, by completion, the algorithm also finds a 4% better fitness value on average for the same number of evaluations. For an image compression system, the algorithm found on average 3% (12%) better fitness values or compression ratios using only 58% (65%) number of evaluations needed by an EA in lossless (lossy) compression mode.     

Model Predictive Control of Hybrid Electric Vehicles for Improved Fuel Economy

This brief proposes a model predictive control method using preceding vehicle information within hybrid electric vehicles' (HEVs') predictive cruise control system to improve car following performance and reduce fuel consumption. This paper adds two original contributions to the related literature. First, a real‐time optimization approach using Pontryagin's minimum principle with analytical methods rather than numerical iteration methods is proposed. Second, to compute the desired battery state of charge trajectory as a function of vehicle position, only the topographic profile of the future road segments must be known. Both the fuel economy and the driving profile are optimized using the proposed approach. Simulation results show that fuel economy using the proposed method is improved significantly.     

无线传感器网络的多目标覆盖控制策略

针对无线传感器网络中能耗、能耗均衡和覆盖率互相矛盾的问题,提出了一种多目标优化覆盖控制策略.在构建网络模型的基础上,以覆盖率、能耗和网络能耗均衡为优化目标,设计针对自由搜索算法的适应值函数和编码方案.仿真结果表明,该覆盖控制策略在保证高覆盖率的同时,能有效地降低能耗和保持网络能量均衡.     

神经网络增强学习的梯度算法研究

针对具有连续状态和离散行为空间的Markov决策问题，提出了一种新的采用多层前馈神经网络进行值函数逼近的梯度下降增强学习算法，该算法采用了近似贪心且连续可微的Boltzmann分布行为选择策略，通过极小化具有非平稳行为策略的Bellman残差平方和性能指标，以实现对Markov决策过程最优值函数的逼近，对算法的收敛性和近似最优策略的性能进行了理论分析，通过Mountain-Car学习控制问题的仿真研究进一步验证了算法的学习效率和泛化性能。     

混合动力系统能量管理策略的实时优化控制算法

依据最优控制理论得到的混合动力汽车能量管理策略与未来的驾驶需求相关联,无法解决算法的实时性问题.本文另辟蹊径,结合规则构造二次型性能指标来限制发动机功率的大幅度频繁波动,间接地降低油耗.为此,在对混合动力系统近似线性处理的基础上,利用二次型最优跟踪理论推导出定常的反馈控制律,将发动机和电机功率表示成系统当前状态和车速指令的线性函数并应用于非线性实车系统.仿真结果表明,本文提出的能量管理实时控制算法可以达到良好的节油效果, 对不同的道路工况和电池初始荷电状态有良好的适应性.     

Real‐time optimal energy management for a fuel cell/battery hybrid system

An energy management strategy is proposed for a class of fuel cell/battery hybrid systems. In such hybrid systems, a fuel cell system is the main power source, and a lithium‐ion battery is the auxiliary power source. In order to manage the system power at the next moment in a reasonable way, a load current filter with bounded estimation errors is designed to estimate the load current. Then, a real‐time optimal energy management algorithm is proposed to optimize economy consumption of the hybrid system. By taking current change rate of the fuel cell and the state of charge into consideration and taking reasonable model simplifications, the optimization problem can be described as a quadratic programming problem. Then a general purpose solver is proposed to solve the quadratic programming problem based on the alternating direction method of multipliers. The efficiency of the proposed solver is much faster than computing interior point method or active set method. Simulation results in MATLAB/SIMULINK are carried out to validate the significant effectiveness and efficiency of the proposed management strategy.     

Smart priority queue algorithms for self-optimizing event storage

Low run-time overhead, self-adapting storage policies for priority queues called smart priority queue (SPQ) techniques are developed and evaluated. The proposed SPQ policies employ a low-complexity linear queue for near-head activities and a rapid-indexing variable bin-width calendar queue for distant events. The SPQ configuration is determined by monitoring queue access behavior using cost-scoring factors and then applying heuristics to adjust the organization of the underlying data structures. To illustrate and evaluate the method, an SPQ-based scheduler for discrete event simulation has been implemented and was used to assess the resulting efficiency, components of access time, and queue usage distributions of the existing and proposed algorithms. Results indicate that optimizing storage to the spatial distribution of queue access can decrease HOLD operation cost between 25% and 250% over existing algorithms such as calendar queues.     

Discovering driving strategies with a multiobjective optimization algorithm

When driving a vehicle along a given route, several objectives such as the traveling time and the fuel consumption have to be considered. This can be viewed as an optimization problem and solved with the appropriate optimization algorithms. The existing optimization algorithms mostly combine objectives into a weighted-sum cost function and solve the corresponding single-objective problem. Using a multiobjective approach should be, in principle, advantageous, since it enables better exploration of the multiobjective search space, however, no results about the optimization of driving with this approach have been reported yet. To test the multiobjective approach, we designed a two-level Multiobjective Optimization algorithm for discovering Driving Strategies (MODS). It finds a set of nondominated driving strategies with respect to two conflicting objectives: the traveling time and the fuel consumption. The lower-level algorithm is based on a deterministic breadth-first search and nondominated sorting, and searches for nondominated driving strategies. The upper-level algorithm is an evolutionary algorithm that optimizes the input parameters for the lower-level algorithm. The MODS algorithm was tested on data from real-world routes and compared with the existing single-objective algorithms for discovering driving strategies. The results show that the presented algorithm, on average, significantly outperforms the existing algorithms.     

多移动机器人避障编队控制

研究了非完整移动机器人群的避障编队问题. 在次优化控制基础上, 通过对每个交互机器人求解指标函数存在耦合的优化问题提出了两种算法. 在终端惩罚项中加入了势场函数并且构造出相应的终端约束集. 关于系统稳定性及安全性进行了讨论. 仿真实例说明了所提算法的可行性.     

增程式电动汽车控制策略的优化研究

针对增程式电动汽车恒功率控制策略中发动机工作点难以选择的问题,运用一种基于多目标遗传算法的优化方法,以百公里油耗和排放指标为优化目标,利用AVL CRUISE和Matlab/Simulink软件联合建立增程式电动汽车整车动态性能仿真分析模型,针对NEDC工况和FTP75工况进行恒功率控制策略下发动机工作点优化,仿真结果显示,优化后的发动机工作点有效改善了百公里油耗和尾气排放量。该优化方法可以减少设计者调试和选择电动汽车增程器发动机工作点的时间,具有良好的实用价值。     

物流运输调度问题的混沌烟花算法——基于多车型供应链

为了满足供应链物流的不同需求，考虑多种车型、车辆容量、车辆油耗、车辆最大配送距离等约束条件，以最小油耗、最短配送距离为目标，建立多车型供应链物流运输调度模型（Multi-Type Vehicle Routing Problem in Supply Chain，MTVRPSC），并提出一种混沌烟花算法求解该模型。该算法以烟花算法为核心，提出一种编解码策略实现连续空间到MTVRPSC离散空间的映射，重新定义算法的适应度函数、适应度值和适应度的比较方法，并采用混沌初始化策略和混沌搜索策略来增强算法收敛效果。实验结果表明，所提出的算法在求解MTVRPSC时具有较强的寻优能力和稳定性。     

基于分组到达率的802.11 DCF性能分析

针对WLAN(wireless local area network)基础结构模式中的IEEE 802.11 DCF(distributed coordination function)机制,提出了一种基于分组到达率的性能分析模型.模型不仅考虑了终端数量、传输负载、二进制指数回退机制等因素,而且分析了MAC(mediunl access control)层有限队列对系统性能的影响.在每个终端被模型化为M/M/1/K队列的基础上,进一步利用虚拟时隙在时间上离散化终端MAC层队列状态,并采用离散时间的三维马尔可夫链对系统性能建模.基于该模型得到了归一化吞吐量、分组时延和丢包率.仿真分析结果表明,该模型能够有效地预测变化的分组到达率情形下DCF机制的性能.     

离散事件系统仿真中的优化算法

计算机仿真是研究离散事件系统的一类重要方法。在仿真环境下对离散事件系统的参数进行优化是设计高性能系统的必要保证。本文给出了随机型优化问题的分类，并综述了离散 参数和连续参数随机型优化问题中典型的求解算法。     

A hybrid heuristic queue based algorithm for task assignment in mobile cloud

This paper presents a novel algorithm for task assignment in mobile cloud computing environments in order to reduce offload duration time while balancing the cloudlets’ loads. The algorithm is proposed for a two-level mobile cloud architecture, including public cloud and cloudlets. The algorithm models each cloud and cloudlet as a queue to consider cloudlets’ limited resources and study response time more accurately. Performance factors and resource limitations of cloudlets such as waiting time for clients in cloudlets can be determined using queue models. We propose a hybrid genetic algorithm (GA) - Ant Colony Optimization (ACO) algorithm to minimize mean completion time of offloaded tasks for the whole system. Simulation results confirm that the proposed hybrid heuristic algorithm has significant improvements in terms of decreasing mean completion time, total energy consumption of the mobile devices, number of dropped tasks over Queue based Random, Queue based Round Robin and Queue based weighted Round Robin assignment algorithms. Also, to prove the superiority of our queue based algorithm, it is compared with a dynamic application scheduling algorithm, HACAS, which has not considered queue in cloudlets.     

无线感知执行网的智能协作机制研究

针对无线感知执行网中的节点协作问题,提出了多目标智能协作策略。在感知节点不完全连通的WSANs中,设计局部感知节点成簇算法,实现感知节点间的通信协作;以网络能耗均衡和信息传输时延为目标,应用离散多目标自由搜索算法对WSANs进行实时路径规划,实现簇头节点和执行节点间的协作,并通过路径惩罚策略和路径均衡策略,保证寻优结果的有效性、均衡执行节点之间的能耗。仿真实验表明:无线感知执行网智能协作机制能有效地选择多执行节点路径,实现网络协作的实时需求与能量消耗之间的平衡。     

基于二次型性能指标的混合动力汽车实时优化控制

为解决混合动力系统实时优化控制问题,本文提出了一种基于二次型性能指标最优的混合动力汽车功率分配优化方案.通过合理的假设和近似,建立了混合动力系统的线性模型,并利用二次型最优控制理论将混合动力最优控制问题转化为二次型最优调节问题进行求解,得到了一个结构简单的实时优化控制算法.5种道路工况下的仿真结果表明,本文提出的控制方法在未来道路工况未知的情况下能够实现混合动力系统的实时优化控制,且节油率与离线计算以燃油消耗最小为性能指标的全局最优控制的节油率相近.     

基于改进蝙蝠算法的柔性流水车间排产优化问题研究

为解决柔性流水车间调度问题( flexible flow shop scheduling problem,FFSP),提出了一种基于精英个体集的自适应蝙蝠算法(self-adaptive elite bat algorithm,SEBA)。针对蝙蝠算法存在求解离散问题具有局限性、易陷入局部极值、优化结果精度低等问题,该算法采用ROV(ranked order value)编码方式,使算法适用于求解离散型的FFSP问题;提出基于汉明距离的精英个体集,由多个适应度高但相似度低的精英个体轮流引导种群进化,增强种群进化活力,避免寻优过程陷入局部极值;提出自适应位置更新机制,提高算法优化精度。最后采用不同规模的标准实例对改进算法进行测试,与已有算法进行对比,实验结果验证了改进蝙蝠算法求解FFSP问题的有效性。