基于自适应猴群算法的传感器优化布置方法研究

针对猴群算法中爬过程和望过程的搜索方式较为机械,以及跳过程的方式较为单一的问题,提出了一种用于传感器优化布置的自适应猴群算法。采用双重编码的方式,克服了原猴群算法只能解决连续变量优化问题的缺陷;对爬过程和望过程进行了改进,使其能够自适应选择这两个搜索方式以提高算法的局部搜索能力和效率;提出了两种全新的跳过程,即反射跳和变异跳,来增强算法的全局搜索能力。文末以大连国贸大厦为例,进行了参数敏感性分析以及传感器优化布置方案的选择,结果表明自适应猴群算法的搜索效率较原猴群算法有了大幅提高,能较好地解决传感器优化布置问题。     

基于小生境猴群算法的传感器优化布置方法研究

将小生境进化理论引入到猴群算法中,提出了一种用于传感器优化布置的小生境猴群算法。采用双重编码的方式,克服了原猴群算法只能解决连续变量优化问题的缺陷,并采用混沌搜索的方式产生初始猴群位置,使猴群均匀分布;将猴群分为多个小生境猴群系统,形成各自独立的搜索空间;基于共享适应度的方法对小生境猴群进行末尾淘汰,并随机初始化产生新的猴子,提高小生境猴群的多样性;利用各个小生境猴群的平均适应度替换机制使得优良猴子位置信息能够得到共享,提高算法的整体搜索性能和收敛效率。文末以大连国贸大厦为例,进行了参数敏感性分析以及传感器优化布置方案的选择,结果表明小生境猴群算法的搜索效率较原猴群算法有了大幅提高,能较好地解决传感器优化布置问题。     

基于自适应引力算法的桥梁监测传感器优化布置

针对桥梁健康监测中传感器布置优化问题,提出了一种基于自适应引力算法的传感器优化布置方法。以模态置信准则为基础,构造满足传感器优化布置的适应度函数;针对引力搜索算法开发能力不足,对衰减因子α进行了自适应改进。搜索初期α较小,粒子以较大步长进行全局搜索,增强了算法的搜索效率;搜索后期α较大,粒子以较小的步长进行局部搜索,提高了算法的搜索能力,避免落入局部极值点。改进后的自适应引力算法通过双重编码的方式,使算法可以解决离散型的传感器布置问题;以马水河大桥为例,验证算法的可行性。结果表明,改进后的算法有很好的寻优能力,能够准确高效的确定传感器优化位置。     

利用分布式狼群算法进行三维传感器优化布置的方法研究

考虑传感器优化布置中的模态置信准则MAC只能对三维传感器的某一方向进行优化,而不能确保在三个方向同时实现优化布置的问题,提出将节点的三个平动自由度作为一个单元,通过节点的Fisher信息阵来获取结构所布置传感器的Fisher信息阵,并借鉴传统一维模态置信准则的思想,构建了一种全新的三维模态置信准则TMAC。为提高算法的求解效率,提出了一种分布式狼群算法,采用双重编码方式,克服原狼群算法只能求解连续变量优化的问题;采用狼群分组的方法,通过组内狼个体的信息交流,提高了算法的搜索效率。文末以中佛罗里达大学建立的基准模型为例,进行了参数敏感性分析以及三维传感器优化布置方案的选择,结果表明:分布式狼群算法的搜索能力较原狼群算法有了大幅提高,能较好地解决传感器优化布置问题。     

多族群粒子群优化算法导弹结构模型修正

本文针对粒子群优化（PSO）算法极易陷入局部最优的缺陷,提出了一种多族群粒子群优化算法（MRPSO）,该算法具有较强的全局搜索能力,能极大地降低搜索陷入局部最优的概率。并将该算法引入到有限元模型修正中,对某型号导弹全弹结构进行了优化修正,修正后结构的固有频率都有了非常明显的改善,证实了MRPSO算法的有效性及工程应用价值。     

基于混合遗传算法的桥梁监测传感器优化布置

利用混合遗传算法(Hybrid Genetic Algorithm,HGA)对南京长江大桥的监测传感器进行优化布置,得到了考虑不同的振型阶数和不同传感器数量的优化布置方案.优化结果表明,HGA高效、稳定,具有较强的全局搜索能力,在桥梁健康监测中有着广阔的应用前景.     

基于分级免疫萤火虫算法的桥梁振动传感器优化布置研究

针对结构健康监测系统设计的振动传感器优化布置问题,在基本萤火虫算法的基础上引入等级划分策略和免疫机制,提出了一种分级免疫萤火虫算法。采用二重结构编码,弥补了基本萤火虫算法只能用于连续优化问题的不足;建立等级划分制度,使不同等级种群内部形成独立的搜索空间,维持了种群多样性,并让优质个体得以保留;引进免疫机制,进行萤火虫的选择、记忆、交叉和变异,增强了算法的全局搜索能力和局部寻优能力;文末利用足尺Benchmark桥梁模型,对算法参数进行了敏感性分析,并开展了振动传感器优化布置方案的选择。结果表明,与基本离散型萤火虫算法相比,分级免疫萤火虫算法的计算效率和寻优结果均有显著提升,能够很好地解决振动传感器优化布置问题。     

基于并行混沌和复合形法的桁架结构形状优化

针对多工况下受应力、位移和局部稳定性约束的桁架形状优化问题,提出了基于并行混沌优化算法和复合形法的混合优化算法。该算法综合利用了并行混沌的全局搜索能力,复合形法的快速局部搜索能力和混沌细搜索。首先,利用并行混沌优化算法快速搜索到全局最优解附近,然后应用改进复合形法以并行混沌的优化解为初始复形进行搜索,提高了最优解的搜索速度,最后应用混沌细搜索策略提高最优解的精度。两个典型数值算例验证了该混合优化方法对桁架形状优化问题的有效性和稳定性。     

基于混合粒子群算法的物流配送路径优化问题研究

针对物流配送路径优化问题,提出了一种融合Powell局部寻优算法和模拟退火算法的混合粒子群算法,以克服单用粒子群算法求解问题早熟收敛的不足,增加算法的开发能力,提高算法的全局搜索能力,并进行了实验计算.计算结果表明,用混合粒子群算法求解物流配送路径优化问题,可以在一定程度上提高粒子群算法在局部搜索能力和搜索全局最优解概率,从而得到质量较高的解.     

自适应耦合局部最优法及其在模型修正中的应用

耦合局部最优法作为一种新型的优化技术,既具有高效的搜索速度又具有全局搜索能力.然而,对于大规模优化问题,该方法容易陷入局部最优;另外,梯度信息在该项技术中起着重要作用,而对于复杂问题往往不能得到精确的梯度信息,从而使得该算法的全局搜索能力下降.本文分别从初始种群的确定、变步长搜索、自调节种群三方面对原算法进行了改进,提出了自适应耦合局部最优法,使之具备解决多变量复杂优化问题的能力.通过两个测试函数验证了改进算法比原有算法更易于得到全局最优解并保持较高的计算效率.最后,采用一个试验算例验证了自适应耦合局部最优法的有效性.     

A problem space algorithm for single machine weighted tardiness problems

We propose a problem space genetic algorithm to solve single machine total weighted tardiness scheduling problems. The proposed algorithm utilizes global and time-dependent local dominance rules to improve the neighborhood structure of the search space. They are also a powerful exploitation (intensifying) tool since the global optimum is one of the local optimum solutions. Furthermore, the problem space search method significantly enhances the exploration (diversification) capability of the genetic algorithm. In summary, we can improve both solution quality and robustness over the other local search algorithms reported in the literature.     

Modelling and solving algorithm for two-stage scheduling of construction component manufacturing with machining and welding process

This paper focuses on a two-stage machining and welding scheduling problem based on an investigation at a structural metal manufacturing plant, aiming to minimise the total makespan. Several parts processed at Stage one according to classical job-shop scheduling are grouped into a single construction component at the second welding stage. Fabrication of the construction component cannot begin until all comprising parts have been completed at Stage one. This paper establishes a novel mathematic model to minimise the total makespan by mainly considering the dominance relationship between the construction component and the corresponding parts. In order to solve this two-stage problem, we propose an improved harmony search algorithm. A local search method is applied to the best vector at each iteration, so that a more optimal vector can be subsequently realised. The average value, minimum value, relative percentage deviation and standard deviation are discussed in the experimental section, and the proposed local best harmony search algorithm outperforms the genetic algorithm, immune algorithm and harmony search algorithm without local search. Moreover, six optimal solutions are given as Gantt charts, which vividly illustrate that the mathematical model established in this paper can facilitate the development of a better scheduling scheme.     

改进的最低水平线搜索算法求解矩形排样问题

矩形优化排样问题是一个在制造业领域生产实践中普遍遇到的问题,采用了一种改进的最低水平线搜索算法求解此类问题.首先分析了原始的最低水平线搜索算法在排样中存在的缺陷,并针对该缺陷为其设计了一个评价函数,排样时对所有未排零件进行评价,选择评价值最高的零件排入当前位置,从而克服了算法在搜索过程中的随机性,优化了算法的搜索方向.实验仿真的结果表明,提出的算法可以得到较好的排样效果,并且其解决问题的规模越大,优化性能越好,适合于求解大规模排样问题.     

Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

The reliability and real time of industrial wireless sensor networks (IWSNs) are the absolute requirements for industrial systems, which are two foremost obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliability, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolution harmony search (MOBDEHS) is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deployment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOBDEHS is an effective tool for multi-objective node placement problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II (NSGA-II) and modified NSGA-II.     

基于改进蚁群算法的码垛机器人路径规划应用研究

目的为解决蚁群算法在码垛机器人路径规划中存在的收敛速度慢、容易陷入局部最优等问题,提出一种人工势场和蚁群算法相结合的方法。方法首先,根据码垛机器人机械手在人工势场中不同节点所受到的合力,对初始信息素进行不均匀分布,以解决蚁群算法初期由于缺乏信息素导致的无效路径搜索。其次,在启发函数的设计中引入码垛机器人机械手在下一节点所受到的合力,以解决蚁群算法容易陷入局部最优的问题。最后,对信息素的更新策略进行改进。按照寻得路径的长度不同,对每次迭代完成后信息素的增量成比例进行更新,并设置最大、最小值,以解决迭代后期路径上信息素过大而使蚁群算法陷入局部最优的问题。结果改进后的蚁群算法收敛速度提升了约51%,寻找到的最短路径提升了约10%。和其他改进的蚁群算法相比,在综合性能上也有一定程度上的提高。结论改进后的蚁群算法收敛更快,寻找的最优路径更短。     

一种用于匹配场反演的遗传算法

遗传算法在接近全局最优解时,存在搜索速度变慢、过早收敛、个体的多样性减少很快、甚至陷入局部最优解等问题。通过在遗传算法中引入模拟退火因子、混沌因子和多样性测度因子,在很大程度上克服了原有遗传算法的早熟、局部搜索能力差的缺点。同时,又能发挥原有遗传算法的强大的全局搜索能力,保证了改进后的混合遗传算法能较好地收敛于其全局最优值。     

Enhanced damping of lightweight structures by semi-active joints

Summary Lightweight structures typically have low inherent structural damping. Effective vibration suppression is required, for example, in certain applications involving precision positioning. The present approach is based on friction damping in semi-active joints which allow relative sliding between the connected parts. The energy dissipation due to interfacial slip in the friction joints can be controlled by varying the normal pressure in the contact area using a piezo-stack actuator. This paper focuses on the optimal placement of semi-active joints for vibration suppression. The proposed method uses optimality criteria for actuator and sensor locations based on eigenvalues of the controllability and observability gramians. Optimal sensor/actuator placement is stated as a nonlinear multicriteria optimization problem with discrete variables and is solved by a stochastic search algorithm. At optimal locations, conventional rigid connections of a large truss structure are replaced by semi-active friction joints. Two different concepts for the control of the normal forces in the friction interfaces are implemented. In the first approach, each semi-active joint has its own local feedback controller, whereas the second concept uses a global, clipped-optimal controller. Simulation results for a 10-bay truss structure show the potential of the proposed semi-active concept. Dedicated to Professor Franz Ziegler on the occasion of his 70th birthday