Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures

This work develops an augmented particle swarm optimization (AugPSO) algorithm using two new strategies,: boundary-shifting and particle-position-resetting. The purpose of the algorithm is to optimize the design of truss structures. Inspired by a heuristic, the boundary-shifting approach forces particles to move to the boundary between feasible and infeasible regions in order to increase the convergence rate in searching. The purpose of the particle-position-resetting approach, motivated by mutation scheme in genetic algorithms (GAs), is to increase the diversity of particles and to prevent the solution of particles from falling into local minima. The performance of the AugPSO algorithm was tested on four benchmark truss design problems involving 10, 25, 72 and 120 bars. The convergence rates and final solutions achieved were compared among the simple PSO, the PSO with passive congregation (PSOPC) and the AugPSO algorithms. The numerical results indicate that the new AugPSO algorithm outperforms the simple PSO and PSOPC algorithms. The AugPSO achieved a new and superior optimal solution to the 120-bar truss design problem. Numerical analyses showed that the AugPSO algorithm is more robust than the PSO and PSOPC algorithms.     

Optimization of large‐scale truss structures using sparse SAND formulations

Sparsity features of simultaneous analysis and design (SAND) formulations are studied and exploited for optimization of large‐scale truss structures. Three formulations are described and implemented with an existing analysis code. SAND formulations have large number of variables; however, gradients of the functions and Hessian of the Lagrangian are quite sparsely populated. Therefore, this structure of the problem is exploited and an optimization algorithm with sparse matrix capability is used to solve large‐scale problems. An existing analysis software is integrated with an optimizer based on a sparse sequential quadratic programming (SQP) algorithm to solve sample problems. The formulations and algorithms work quite well for the sample problems, and their performances are compared and discussed. For all the cases considered, the SAND formulations out perform the conventional formulation except one case. Further research is suggested to fully study and utilize sparse features of the alternative SAND formulations and to develop more efficient sparse solvers for large‐scale and more complex applications. Copyright © 2006 John Wiley & Sons, Ltd.     

基于天牛须-粒子群优化算法的大型中央空调系统节能控制

大型公共建筑中央空调系统送风末端数量多,负荷需求变化大,常用的控制方法虽能满足末端需求,却存在能耗巨大的问题。为此本文构建了一个空调系统送风和冷冻水系统的优化控制模型,以系统能耗为优化目标,使用天牛须搜索-粒子群优化（BAS-PSO）混合算法求解该问题,提高系统节能率,改善了传统PSO的缺陷。同时将该模型用于上海市某公共建筑集中式空调系统的空气调节子系统进行优化控制,结果表明：BAS-PSO与原有控制方案——定送风温度控制相比,最大节能量达252.02 kW,节能率为20%,而现场测试显示,使用该优化控制能在负荷率为0.55时达到14.6%的节能率,节能153.15 kW,证明了该优化控制模型及优化算法有可靠的应用前景。     

Optimality Condition and Branch and Bound Algorithm for Constrained Redundancy Optimization in Series Systems

This paper considers the constrained redundancy optimization problem in series systems. This problem can be formulated as a nonlinear integer programming problem of maximizing the overall systems reliability under limited resource constraints. By exploiting special features of the problem, we derive a new necessary condition for optimal redundancy assignments. This condition leads to a new fathoming condition in the branch and bound method that may result in a significant reduction of computational efforts, as evidenced in our numerical calculation for linearly constrained redundancy optimization problems.     

遗传算法及其在材料设计中的应用

简要简述了遗传算法的基本原理,综述了它在复合材料优选、功能梯度材料设计、合金设计及材料制造工艺参数优化等材料设计领域中的应用。     

橡胶悬置元件结构参数优化设计方法

由于橡胶悬置元件的结构比较复杂,截面不规则,无法用传统的优化方法对其结构优化.利用遗传算法和神经网络相结合的策略对橡胶悬置元件的几何结构参数进行优化,即用神经网络学习算法建立橡胶悬置元件几何结构参数与其三个方向刚度的非线性全局映射关系,获得遗传算法求解结构优化问题所需的目标函数,用遗传算法进行优胜劣汰的寻优搜索运算,求出最优解.优化结果表明,橡胶悬置元件结构参数优化设计方法是可行的.     

基于改进粒子群算法的分布式天线系统跨层功率分配方案

结合数据链路层的队列状态信息和物理层的信道状态信息定义了系统的吞吐量系数和公平性系数,建立了分布式天线系统跨层功率分配的离散速率集优化模型。对粒子群优化算法的初始群体产生、粒子更新等步骤进行了改进,形成了改进粒子群算法(IMPSO)。利用IMPSO进行了动态功率分配和跨层优化。数值仿真结果表明,IMPSO能够取得很好的队列时延性能,选取不同的权重可对系统吞吐量性能和公平性性能产生重要影响,IMPSO获得的系统吞吐量性能和公平性性能较之功率固定分配算法(AP)均有较大幅度的提高。     

Voigt–Reuss topology optimization for structures with nonlinear material behaviors

This work is directed toward optimizing concept designs of structures featuring inelastic material behaviours by using topology optimization. In the proposed framework, alternative structural designs are described with the aid of spatial distributions of volume fraction design variables throughout a prescribed design domain. Since two or more materials are permitted to simultaneously occupy local regions of the design domain, small-strain integration algorithms for general two-material mixtures of solids are developed for the Voigt (isostrain) and Reuss (isostress) assumptions, and hybrid combinations thereof. Structural topology optimization problems involving non-linear material behaviours are formulated and algorithms for incremental topology design sensitivity analysis (DSA) of energy type functionals are presented. The consistency between the structural topology design formulation and the developed sensitivity analysis algorithms is established on three small structural topology problems separately involving linear elastic materials, elastoplastic materials, and viscoelastic materials. The good performance of the proposed framework is demonstrated by solving two topology optimization problems to maximize the limit strength of elastoplastic structures. It is demonstrated through the second example that structures optimized for maximal strength can be significantly different than those optimized for minimal elastic compliance. © 1997 John Wiley & Sons, Ltd.     

A hybrid optimization method of harmony search and opposition-based learning

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optimum in an efficient way. In this article, a hybrid optimization approach is proposed and studied, in which the HS is merged together with the opposition-based learning (OBL). The modified HS, namely HS-OBL, has an improved convergence property. Optimization of 24 typical benchmark functions and an optimal wind generator design case study demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.     

蜂窝微穿孔吸声体的宽频吸声性能优化设计

针对蜂窝微穿孔吸声体结构,利用粒子群优化(Particle Swarm Optimization,PSO)算法优化吸声体结构的吸声性能.以三蜂窝芯的蜂窝微穿孔吸声体结构中微穿孔板的孔径、穿孔率和背腔深度为优化设计变量,以吸声体的吸声系数和吸声频带为优化目标,得到吸声体宽频范围吸声时的优化结构参数.将三参数优化、两参数优...     

基于TRIZ-GA老龄服务机器人人因系统功能配置与设计优化

目的 基于TRIZ理论对老龄服务机器人用户需求、配置原理及人机关系进行分析,并运用GA算法进行优化。方法 首先利用五阶问卷、层次分析法对老年人行为特征进行剖析,得出老年人具体的功能需求。结合TRIZ理论推导出老龄服务机器人的功能结构模型。其次利用物-场模型建立较为完善的老龄服务机器人新型人机系统设计模型。在功能模型与系统模型的基础上,利用物理矛盾原理推导出老龄服务机器人的配置原理,并运用技术矛盾原理对其进行设计改进,最后利用GA算法寻求最优解,将其具象化并用主成分综合评价验证研究成果。结果 在满足老年人需求的基础上,提出了功能模型、新型人机模型及配置模型。结论 提出了老龄服务机器人配置原则及优化方法与设计指标,以期为后续老龄服务机器人交互设计及人机界面设计提供理论依据。     

Fast simulation-driven design of microwave structures using improved variable-fidelity optimization technique

An improved variable-fidelity optimization algorithm for the simulation-driven design of microwave structures is presented. It exploits a set of electromagnetic-based models of increasing discretization density. These models are sequentially optimized with the optimum of the ‘coarser’ model being the initial design for the ‘finer’ one. The found optimum is further refined using a response surface approximation model constructed from the coarse-discretization simulation data. In this work, the computational efficiency of the variable-fidelity algorithm is enhanced by employing a novel algorithm for optimizing the coarse-discretization models. This allows reduction of the overall design time by up to 50% compared to the previous version. The presented technique is particularly suitable for problems where simulation-driven design is the only option, for example, ultra wideband and dielectric resonator antennas. Operation of the presented approach is demonstrated using two examples of antennas and a microstrip filter. In all cases, the optimal design is obtained at a low computational cost corresponding to a few high-fidelity simulations of the structure.     

An Efficient Adaptive Sequential Methodology for Expensive Response Surface Optimization

The preset response surface methodology (RSM) designs are commonly used in a wide range of process and design optimization applications. Although they offer ease of implementation and good performance, they are not sufficiently adaptive to reduce the required number of experiments and thus are not cost effective for applications with high cost of experimentation. We propose an efficient adaptive sequential methodology based on optimal design and experiments ranking for response surface optimization (O‐ASRSM) for industrial experiments with high experimentation cost, limited experimental resources, and requiring high design optimization performance. The proposed approach combines the concepts from optimal design of experiments, nonlinear optimization, and RSM. By using the information gained from the previous experiments, O‐ASRSM designs the subsequent experiment by simultaneously reducing the region of interest and by identifying factor combinations for new experiments. Given a given response target, O‐ASRSM identifies the input factor combination in less number of experiments than the classical single‐shot RSM designs. We conducted extensive simulated experiments involving quadratic and nonlinear response functions. The results show that the O‐ASRSM method outperforms the popular central composite design, the Box–Behnken design, and the optimal designs and is competitive with other sequential response surface methods in the literature. Furthermore, results indicate that O‐ASRSM's performance is robust with respect to the increasing number of factors. Copyright © 2012 John Wiley & Sons, Ltd.     

自适应免疫算法的SVME用于水下目标识别

针对用支持向量机集成提高水下目标识别正确率会使识别系统更加复杂的问题,提出了一种以自适应免疫算法(AIA)的支持向量机选择性集成(SVME)算法(即AIA-SVME算法)进行分类器优化选择,对实测水下目标声信号进行分类识别.与分类器全部集成的识别实验对比证明,该算法在选择9%的分类器后仍可以达到分类器全部集成的识别效果,不仅保证了识别精度,还使得识别系统大幅度精简,节省在线识别的时间.该研究对于水下目标分类决策优化集成的新方法探索具有重要理论价值和实际意义.     

Improved robust exponential stability for Takagi–Sugeno fuzzy uncertain systems with time-varying delays

This paper concerns managing the robust exponential stability problem of uncertain Takagi–Sugeno fuzzy systems with time-varying delay by employing a further improved integral inequality matrix approach. Based on the linear matrix inequality (LMI) approach, delay-dependent robust exponential stability criteria have been developed. By taking the relationship among the time-varying delay, its upper bound and their difference into account, some less conservative LMI-based delay-dependent robust exponential stability criteria are obtained without ignoring any useful terms in the derivative of Lyapunov–Krasovskii functionals. Maximum allowable upper bound for time-varying delays is determined. Numerical examples are provided to show that the obtained results significantly improve the allowed upper bounds of delay size over some methods existing in the literature.     

基于改进NSGA-Ⅱ算法的电-气-热综合能源系统多目标优化

随着电力、天然气和热力网络耦合紧密程度不断加深,综合能源系统协同优化成为了新的研究热点。提出一种适用于含非凸约束条件的综合能源系统多目标优化问题的改进NSGA-Ⅱ算法,通过维护全局的帕累托最优解集提升解的搜索效率,同时采用动态调整法,提高在高维等式约束下找到可行解的概率。算例分析验证了该方法的有效性。