AN OPTIMUM VEHICULAR PATH ALGORITHM FOR TRAFFIC NETWORK BASED ON HIERARCHICAL SPATIAL REASONING

Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers’ choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large_scale traffic networks.

Immune Algorithm for Selecting Optimum Services in Web Services Composition

For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an Immune Algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.

Genetic algorithm for pareto optimum-based route selection

A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.

Optimum design of carbon/carbon ablative property based on genetic algorithm

An optimum design model has been proposed for carbon/carbon ablative property based on genetic algorithm,in which the optimum parameters are the number of woven satins,K of fiber bundles,layers per unit height,the average distance of puncture fibers in Z direction and Ply Stacking angle,and the constraint conditions are the density and diameter of carbon fibers and the density of carbon matrix.The results demonstrate that after optimization,the overall height of the ablative carbon/carbon surface is reduced by 56.5%,the standard deviation is reduced by 34.9% and the surface roughness is reduced by 12.6%,which suggests the remarkable improvement of ablative homogeneity.The present investigation can provide practical methodology for the optimum design of carbon/carbon ablative property and the development of new carbon/carbon composites.

Reliability-Based Optimum Design of a Simple Offshore Platform Based on Genetic Algorithms

In this paper, the problem of reliability-based optimal design of simple offshore platform is studied, and a nonlinear fatigue damage model based on damage mechanics and genetic algorithms are used in the fatigue reliability optimum design of the structure under stochastic wave load. The fatigue damage model and the yield failure reliability analyzing model are used in the paper. The reliability of the models and the effectiveness of genetic algorithm are shown by the results of optimum design.

Short Range Top Attack Trajectory Optimum Design Based on Genetic Algorithm

A flying-body is considered as the reference model, the optimized mathematical model is established. The genetic operators are designed and algorithm parameters are selected reasonably. The scheme control signal in short range top attack flight trajectory is optimized by using genetic algorithm. The short range top attack trajectory designed meets the design requirements, with the increase of the falling angle and the decrease of the minimum range. The application of genetic algorithm to top attack trajectory optimization is proved to be feasibly and effectively according to the analyses of results.

An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.

混合策略的沙猫算法求解化工动态优化问题

化工动态优化问题的求解是实现化工过程自动控制的基础,对环保与经济效益方面均有理论和实际意义。提出一种ISCSO(混合策略的沙猫优化算法)来达到反应器高效优化的目的。首先,针对SCSO(沙猫算法)种群初始化缺乏多样性、容易聚集的问题,引入Circle混沌映射初始化种群,提高多样性。其次,针对沙猫算法在迭代后期收敛速度较慢、容易陷入局部最优的问题,引入随机游走策略。同时,采用柯西变异策略扰动全局最优解,生成新解,避免陷入局部最优,增强局部搜索能力。最后将其应用于3个具有挑战性的化学动态优化工程问题上,并与其他方法进行对比分析。结果表明,改进后的算法具有更强的寻优和求解能力,证明了其有效性和优越性。

改进蚁群算法的送餐机器人路径规划

蚁群算法拥有良好的全局性、自组织性、鲁棒性,但传统蚁群算法存在许多不足之处。为此,针对算法在路径规划问题中的缺陷,在传统蚁群算法的状态转移公式中,引入目标点距离因素和引导素,加快算法收敛性和改善局部最优缺陷。在带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)上,融合蚁群算法和遗传算法,并将顾客时间窗宽度以及机器人等待时间加入蚁群算法状态转移公式中,以及将蚁群算法的解作为遗传算法的初始种群,提高遗传算法的初始解质量,然后进行编码,设置违反时间窗约束和载重量的惩罚函数和适应度函数,在传统遗传算法的交叉、变异操作后加入了破坏-修复基因的操作来优化每一代新解的质量,在Solomon Benchmark算例上进行仿真,对比算法改进前后的最优解,验证算法可行性。最后在餐厅送餐问题中把带有障碍物的仿真环境路径规划问题和VRPTW问题结合,使用改进后的算法解决餐厅环境下送餐机器人对顾客服务配送问题。

基于神经网络与遗传算法的离心泵汽蚀性能优化设计

为了研究离心泵发生汽蚀过程时其内部流动规律和对其汽蚀性能的优化设计,在结合传统优化方法的基础上,提出神经网络与遗传算法结合的智能优化方法。通过Plackett-Burman试验设计从离心泵的叶轮进出口直径、进出口安放角、叶片数、叶片包角等7个设计参数中筛选出显著影响的3个优化设计变量。叶片出口宽度、叶片包角和叶轮出口直径3个优化设计变量,其影响汽蚀性能的显著性由大到小。运用拉丁超立方抽样方法抽取30组设计方案,分别进行数值模拟计算,得出相应的汽蚀余量值。建立神经网络模型,结合遗传算法在规定范围内进行寻优,得到最优设计变量组合及最优汽蚀余量值。取优化后的参数进行数值模拟计算,优化后的离心泵在相同工况下汽蚀余量降低了43.1%,说明优化后的离心泵抗汽蚀性能显著提高。

基于MSPBO的干式变压器优化设计

针对目前变压器优化设计方法存在设计周期长、设计成本高和效率低等问题,采用一种基于改进学生心理学优化算法(SPBO)优化设计方法,通过引入退火算法准则等针对SPBO算法弱点改进。以SCB10-1600/10节能型干式变压器(干变)作为优化设计对象,从降低干变主材成本角度出发,分别应用SPBO和改进SPBO(MSPBO)对干变进行优化设计。实例优化结果表明,SPBO和MSPBO均能有效降低干变主材成本并改善其性能参数。与原SPBO相比,MSPBO优化效果更佳,寻优速度快,寻优空间大;与人工设计方法相比,主材成本节约9.19%,损耗降低3.67%。

基于遗传算法的无人机最优控制器设计

无人机纵向通道最优控制器设计中,状态变量加权矩阵Q和控制变量加权矩阵R直接影响控制系统的性能,Q阵中元素选取直接影响其对应的状态变量性能,R阵中的元素影响其对应的控制变量能量消耗。以升降舵控制输入值、迎角的输出峰值和超调量的加权为适应度函数,应用遗传算法对Q和R中的元素进行寻优,从而提高设计效率。

融合A^(*)与DWA算法的水面船艇动态路径规划

为解决水面船艇路径规划同时要求全局最优、实时避障和航迹安全可靠的问题,提出了一种基于融合A^(*)算法与动态窗口算法(DWA)的水面船艇路径规划方法。首先通过引入启发函数动态加权策略,提高A^(*)算法的搜索效率;然后综合考虑水面船艇的运动特性,采用一种路径转角节点角度削弱策略,减少转角,缩短全局路径长度;最后,基于全局因素影响与航迹安全约束对DWA算法的轨迹评价函数进行改进,并以全局路径提供子目标点引导DWA算法进行局部规划的方式完成算法融合。实验结果表明,融合算法相比于现有算法的总转向角度分别减少了45.6%、46.0%,验证了融合算法的有效性与可行性,并且相较于其他传统算法更具优越性。

一种改进鱼鹰优化算法及其应用

针对鱼鹰优化算法(osprey optimization algorithm,OOA)在运行时存在寻优精度和稳定性差的问题,提出了如下改进策略:首先,将SPM混沌映射融入种群的初始化阶段,提升种群多样性;其次,在勘探和开采阶段分别利用威布尔分布的长、短距离随机扰动策略更新鱼鹰的位置,可有效改善OOA的收敛精度;最后,提出一种“最优-随机均值”的变异策略,用于强化OOA迭代过程中跳出局部最优的能力。所提出的算法称为改进鱼鹰优化算法(improved osprey optimization algorithm,IOOA)。为了验证IOOA的寻优能力,将IOOA与其他新兴智能算法分别对12个基准函数进行寻优对比实验,结果表明:IOOA的寻优成功率、收敛速度以及稳定性显著高于其他算法。此外,将所提出的IOOA应用于混合核相关向量机的超参数寻优中,进一步用于柴油机多目标性能预测。

Research on Multi-stage Optimum Design Technology for Virtual Supply Chain Based on Cybermediary in Manufacturing Industry

Virtual supply chain based on cybermediary (VSC-CM) is an innovative VSC pattern meeting informationization development requirements in the manufacturing industry. Methods and features of customer-demand-oriented optimum VSC design adopted by CM are discussed. A customer demand goal system applying to VSC-CM design and quantifying methods of these goals are accordingly given. Then a three-stage optimum VSC design scheme based on dynamic goals is designed, which considers both the holistic optimization of VSC and individuation demands of member enterprises. To implement the scheme, an optimum algorithm synthesizing fuzzy c-means clustering algorithm and topsis comprehensive evaluation algorithm is presented. Feasibility and rapidity of this scheme is proved through a case analysis finally.

数控机床在机测量系统最佳测量区确定方法

为研究面向不同测量对象且具有普适性的数控机床在机测量系统最佳测量区确定方法,选择球作为测量对象,分析了在机测量系统的工作原理及误差来源,利用BAS-BP神经网络建立了单项几何误差白化模型,同时建立了测量系统综合误差模型和球测量误差模型。研究了用于确定最佳测量区搜索寻优的差分优化布谷鸟(DE-CS)算法,进行了不同算法搜索性能对比,确定了算法最优性能参数。搭建了确定球最佳测量区的实验装置,进行了相应实验,对比了利用算法确定和实际测量得到的最佳测量位置的一致性。实验结果表明,利用上述方法搜索计算确定的面向球最佳测量区位置与实验测量确定的最佳测量区位置一致,最佳测量区为:430.783mm≤X≤439.783mm,-145.133mm≤Y≤-136.133mm和-268mm≤Z≤-258mm,实测最大误差最小值为3.1μm,算法求解的误差也为整个测量空间的最小值0.7107μm,且可用于面向点、面等其他测量对象的最佳测量区确定,具有普适性,可用于确定在机测量系统的最佳测量区。

Genetic algorithm optimization for finned channel performance

Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem （an individual for genetic algorithm）. An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.