Swarm intelligence for mixed-variable design optimization

INTRODUCTION Most nonlinear optimization methods assumethat objective function variables are continuous.However, many practical engineering designproblems frequently encounter discrete variables aswell as continuous variables. Discrete variables areused in many ways such as the representation of theset of standard sized components, the decision onthe number of identical parts or the choice betweendifferent design options. For example, the numberof the teeth of a gear must be chosen …     

A pooled-neighbor swarm intelligence approach to optimal reactive power dispatch

INTRODUCTION The reactive power dispatch is aimed at mini- mizing the active power loss in the transmission network by allocating the reactive power generation under several security constraints. The reactive power dispatch problem has significant influence on secure and economic operation of power systems. The reac- tive power generation affects the overall generation cost via transmission loss. A procedure which allo- cates the reactive power generation so as to minimize the transmissio…     

Multiobjective extremal optimization with applications to engineering design

In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the constrained multiobjective optimization problems (MOPs). The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.     

一种改进的粒子群优化算法

为了克服传统粒子群算法（Pso）的早熟和局部最优问题,通过分析基于惯性权重的粒子群优化在粒子寻优过程中的可行性,提出了一种变惯性权重的改进PSo算法,并对经典的测试函数进行TN试。实验结果证明,与传统PSO算法以及基于惯性权重的PSO相比,改进算法的寻优效果较好,全局搜索能力有显著提高,并能有效地避免早熟收敛问题。     

群智能算法及其应用

群智能算法是一种新兴的演化计算技术,它已经成功地被运用于许多领域。实践证明,群智能方法是一种能够解决许多全局优化问题的有效方法。本文介绍了群智能理论的产生和发展过程,并着力阐述群智能理论中的两个主要算法（粒子群算法和蚁群算法）的算法思想以及研究现状。     

MIS门诊挂号系统数据库设计与优化方案

依据规范化理论给出了MIS门诊挂号系统中的各数据模式详细设计过程,并据实际需求给出了库结构优化方案。结果表明,该优化方案设计合理、使用方便。     

粒子群与差异演化的混合进化算法研究

将标准粒子群（PSO）算法和差异演化（DE）算法混合进行进化,把差异演化算法的优势带入到粒子群算法中,利用差异演化算法其本身具备的对粒子个体的交叉和变异操作使标准粒子群算法种群保持寻优所需的多样性．并将这种算法与标准粒子群算法、G-PSO算法等进行了对比研究,通过混合进化的方法使粒子群种群保持多样性,实验结果表明新算法避免陷入“早熟”或“停滞”的能力大大提高．     

群智能优化算法及其在生物信息学问题中的应用

生物信息学研究需要使用先进的计算工具处理大量生物的模糊的和不确定的数据。群智能优化算法以低成本、快速和准确合理地解决复杂的搜索问题的优点,使其成为一族能用以较好地解决生物信息学中的问题的启发式算法。综述群智能优化算法及其在生物信息学问题中的应用。     

基于混沌振荡粒子群优化的FCM文本聚类方法

针对模糊C均值聚类算法易于陷入局部极值的问题,设计了一种基于混沌振荡粒子群优化的模糊C均值聚类方法。该方法在标准PSO算法中设计了一个振荡环节并引入混沌理论以增加算法的多样性和收敛性,接着把优化后的PSO算法和模糊C均值聚类算法相结合。文本聚类的仿真实验表明,相对于PSO-FCM算法和FCM算法,CCPSO-FCM算法具有良好的全局搜索能力和收敛速度,聚类效果良好。     

基于QPSO的二级齿轮减速器的优化设计

基于量子粒子群算法理论,对二级齿轮减速器进行优化设计,并利用MATLAB软件对量子粒子群算法程序进行了设计验证。从实验结果来看,运用量子粒子群算法很好地实现了减小齿轮中心距的要求,收敛速度快,得到的结果令人较为满意。     

Adaptive multi-objective optimization based on feedback design

The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions.In this paper,a feedback structure for AMOO is designed.Moreover,the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions.Finally,the prop...     

On the developing role of physical models in engineering design education

Recent research, undertaken using participative observation methods within the Leonardo Da Vinci project ‘Open-Dynamic-Design’, provides evidence that EU industrial practice continues to value the flexibility of physical models across a range of disciplines. This research is placed within the philosophical educational framework established by Schon and examples of implementation within the Mechanical and Aerospace Engineering disciplines at the University of Glasgow are discussed. It is argued that physical models support the concurrent optimisation of design and manufacturing processes and that they represent a continuing generic tool to support engineering design education.     

Automatic relational database compression scheme design based on swarm evolution

INTRODUCTION As the Internet develops, the volume of infor- mation increases sharply. Capacity of storage me- dium increases exponentially, while the power of CPU chips increases under Moore’s Law. However, transfer bandwidth of storage medium increases much slower. For a large relational database system (with capacity between a few giga- to multi-tera-bytes), bandwidth of the magnetic disks is always the bot- tleneck of performance. In traditional database re- search, researchers stu…     

Reliability based multiobjective optimization for design of structures subject to random vibrations

Based on a multiobjective approach whose objective function （OF） vector collects stochastic reliability performance and structural cost indices, a structural optimization criterion for mechanical systems subject to random vibrations is presented for supporting engineer＇s design. This criterion differs from the most commonly used conventional optimum design criterion for random vibrating structure, which is based on minimizing displacement or acceleration variance of main structure responses, without considering explicitly required performances against failure. The proposed criterion can properly take into account the design-reliability required performances, and it becomes a more efficient support for structural engineering decision making. The multiobjective optimum （MOO） design of a tuned mass damper （TMD） has been developed in a typical seismic design problem, to control structural vibration induced on a multi-storey building structure excited by nonstationary base acceleration random process A numerical example for a three-storey building is developed and a sensitivity analysis is carried out. The results are shown in a useful manner for TMD design decision support.     

基于改进PSO算法的酒精发酵过程补料优化

针对基本粒子群优化算法存在的早熟收敛问题,提出了一种保持粒子活性的改进PSO算法:当粒子失活时,对粒子进行变异或扰动操作,重新激活粒子,使粒子能够有效地进行全局和局部搜索,并将改进的PSO算法应用到酒精流加发酵过程的补料优化。实验结果表明,运用改进的PSO算法在进行流加发酵过程的补料优化时,它的寻优性能良好,而且寻优速度很快,可以提高最终产物14%的产量。     

Modeling and optimization of catalytic dehydration of ethanol to ethylene using central composite design

The central composite design in the modeling and optimization of catalytic dehydration of ethanol to ethylene was performed to improve the ethylene yield. A total of 20 experiments at random were conducted to investigate the effect of reaction temperature, Si/Al ratios of H-ZSM-5 catalyst and liquid hourly space velocity (LHSV) on the ethylene yield. The results show that the relationship between ethylene yield and the three significant independent variables can be approximated by a nonlinear polynomial model, with R-squared of 99.9% and adjusted R-squared of 99.8%. The maximal response for ethylene yield is 93.4% under the optimal condition of 328 °C, Si/Al ratio 85, and LHSV 3.8 h⁻¹.     

以职业能力培养为目标的高职体育教育课程优化设计

随着社会的发展,对于教育的重视程度越来越高,高等职业教育是一个以实施就业为目的学校。随着发展,其在教学理念方面已经不能适应现代社会的发展需求了,其对现在社会的发展带来了巨大的影响。要想让高职教育能够符合课程教育,一定要重视学生的职业能力培养,所以在进行课程设计上,一定要注意高职体育教育应当以职业能力为目标来进行。本文就针对,现在高职体育教育课程设计中存在的不足,提出了改进这方面不足的方法。     

建筑智能化系统专业教学综合实验创新基地的研究与实践

以培养学生综合素质和创新能力为目标,将专业综合实验室建设与企业共建产、学、研基地和利用教学实践环节,开展创新训练相结合,应用系统集成技术,开发建设将通信网络实验系统与建筑设备综合管理实验系统有机结合的综合实验操作平台,体现最新发展趋势,并可随技术发展动态更新.     

基于PSO算法和ANSYS的变截面板簧优化仿真设计

利用智能微粒群算法(PSO)优化设计少片变截面弹簧代替原等截面板簧.文章通过ANSYS对其静态和动态特性进行有限元仿真分析,结果显示,在满足悬架性能条件下,质量比原板簧减轻了约31%.     

基于多元智能理论的网络学习资源设计

阐述了多元智能理论,分析了多元智能理论对网络学习资源设计的启示,并通过案例对多元智能理论指导下的网络学习资源设计进行了研究．