Scheduling multi-objective open shop scheduling using a hybrid immune algorithm

Despite their importance, hardly ever have multi-objective open shop problems been the topic of researches. This paper studies the mentioned problem and proposes some novel multi-objective solution methods centered on the idea behind artificial immune and simulated annealing algorithms incorporating with powerful and fast local search engines. First, the algorithms are tuned and then carefully evaluated for their performance by means of multi-objective performance measures and statistical tools. An available ant colony optimization is also brought into the experiment. Among the proposed algorithms, the results show that the variant of enhanced artificial immune algorithm outperforms the others.     

免疫模拟退火算法及其在柔性动态Job Shop中的应用

针对车间作业调度问题，在深入分析免疫算法和模拟退火算法的基础上，将两种算法巧妙结合，提出免疫模拟退火算法。该算法引入了免疫记忆、抽取疫苗和接种疫苗等免疫机制，有助于优良个体和基因的保留和利用，提高了算法收敛性，而且其基于概率突跳特性的爬山性能可以避免早熟现象。针对西安航空发动机（集团）有限公司的柔性动态Job Shop，分别用模拟退火算法、免疫算法和免疫模拟退火算法进行了仿真和比较，研究结果表明，免疫模拟退火算法比单一算法性能更优，是求解柔性动态Job Shop问题的有效实用算法。     

Parameter optimization of machining processes using teaching–learning-based optimization algorithm

The optimum selection of process parameters plays a significant role to ensure quality of product, to reduce the machining cost and to increase the productivity of any machining process. This paper presents the optimization aspects of process parameters of three machining processes including an advanced machining process known as abrasive water jet machining process and two important conventional machining processes namely grinding and milling. A recently developed advanced optimization algorithm, teaching–learning-based optimization (TLBO), is presented to find the optimal combination of process parameters of the considered machining processes. The results obtained by using TLBO algorithm are compared with those obtained by using other advanced optimization techniques such as genetic algorithm, simulated annealing, particle swarm optimization, harmony search, and artificial bee colony algorithm. The results show better performance of the TLBO algorithm.     

基于组合优化策略的动车组悬挂参数优化设计

为快速获得改善车辆横向平稳性的最优悬挂参数,提出基于自适应模拟退火算法和非线性序列二次规划算法的组合优化策略对动车组悬挂参数进行优化设计。建立动车组动力学模型,利用最优拉丁超立方抽样方法选取对横向平稳性影响较大的悬挂参数作为设计变量;以横向平稳性为目标函数构建Kriging代理模型,并利用可决系数检验代理模型精度;采用自适应模拟退火算法对代理模型进行全局范围内初步寻优,在初步最优解的基础上采用非线性序列二次规划算法进行局部空间精确求解。研究结果表明,基于Kriging代理模型和组合优化策略的优化效率明显提高,车辆横向平稳性得到显著改善,并且优化前后运行稳定性均满足要求。     

多目标优化方法在汽车盘式制动器设计中的应用

工程问题优化一般是多目标的优化设计,本文论述的多目标模拟退火方法是基于Pareto理论,用于解决多目标问题的一种实用方法。优化得到均匀分布的Pareto最优解集后,依据不同的设计要求,从其中选择最满意的设计结果。将此方法应用于盘式制动器优化设计,目的是在保证足够制动力矩的前提下,尽量减小摩擦副升温和制动器的尺寸,以提高制动器工作的可靠性并给整车的设计留下更多的空间。     

Digitized die forming system for sheet metal and springback minimizing technique

The design of the runner and gating systems is of great importance to achieving a successful injection moulding process. The subjects of this study are the finite element and abductive neural network methods applied to the analysis of a multi-cavity injection mould. In order to select the optimal runner system parameters to minimize the warp of an injection mould, FEM, Taguchi’s method and an abductive network are used. These methods are applied to train the abductive neural network. Once the runner and gate system parameters are developed, this network can be used to accurately predict the warp of the multi-injection mould. A simulated annealing (SA) optimization algorithm with a performance index is then applied to the neural network in order to search the gate and runner system parameters. This method obtains a satisfactory result as compared with the corresponding finite element verification.     

求解Job Shop问题的一种免疫模拟退火算法

针对以最小化加权拖期和为优化目标的Job Shop调度问题,提出了一种基于瓶颈工件识别的免疫模拟退火算法。为描述各工件对最终调度性能影响的关键程度,定义了工件瓶颈特征量并提出基于人工调度经验的模糊推理系统以计算该特征量值。根据瓶颈工件需优先调度这一思路设计了一种有效利用工件瓶颈特征信息的免疫机制。在模拟退火过程中引入该免疫算法,并进行了大量数值计算实验。对不同规模问题的计算实例表明,该算法能够加快优化过程的收敛速度,取得较好的优化结果。     

主动悬架和电动助力转向系统机械与控制参数集成优化

在建立汽车悬架和转向动力学模型的基础上,设计主动悬架和电动助力转向的集成控制系统。针对传统先设计结构参数后设计控制器易造成系统失去全局最优性能的特点,提出一种基于模拟退火的结构和控制器集成优化设计方法,将主动悬架和电动助力转向系统的主要机械结构参数和控制器的部分参数作为设计变量,以汽车的综合动力学指标为目标函数,进行同时优化。仿真计算和试验结果表明此方法与传统设计方法相比,对提高汽车操纵稳定性、行驶平顺性、操纵轻便性和安全性等综合性能具有较好的效果。     

一种基于小生境的混合遗传退火算法

分析遗传算法和模拟退火算法混合优化策略的构造出发点 ,融合小生境技术的思想 ,提出一种以遗传算法和模拟退火算法为子算法的基于小生境技术的混合遗传退火算法———NGSA算法 ,并对该算法的特点和优化性能作了定性分析。结合典型多峰值测试函数———Shubert函数的求解实验 ,说明NGSA算法具有较强的全局和局部搜索能力 ,能够高效地寻找到多个全局极值 ,且参数选择不必过分严格 ,是一种优化能力、效率和可靠性较高的多峰值优化方法。最后 ,讨论了该算法在机械学科的广泛应用背景。     

基于SA—PGA的复杂网络质量成本优化问题研究

以城市输中网络建设项目为背景,以大型复杂网络工程建设成本优化为目的,提出了基于模拟退火单亲遗传算法的工程质量成本优化方法.在单亲遗传算法的基础上,研究了模拟退火算法和单亲遗传算法的结合方式.单亲遗传算法用于全局搜索,模拟退火用于引导微调.同时,引入保优操作有效地防止种群整体退化.在此基础之上,形成了模拟退火单亲遗传算法(SAPGA).根据系统可靠度原理,在质量成本关系模型的基础上,运用模拟退火单亲遗传算法对典型算例的质量成本进行了优化,取得了较好的效果.     

A novel particle swarm optimization approach for product design and manufacturing

This paper presents a novel optimization approach that is a new hybrid optimization approach based on the particle swarm optimization algorithm and receptor editing property of immune system. The aim of the present research is to develop a new optimization approach and then to apply it in the solution of optimization problems in both the design and manufacturing areas. A single-objective test problem, tension spring problem, pressure vessel design optimization problem taken from the literature and two case studies for multi-pass turning operations are solved by the proposed new hybrid approach to evaluate performance of the approach. The results obtained by the proposed approach for the case studies are compared with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, and integration of simulated annealing and Hooke-Jeeves pattern search.     

响应面模型与混合优化算法相结合的锯片参数优化设计

针对传统算法在锯片声学特征优化中的局限性,提出了一种将二阶响应面模型与混合算法相结合的优化设计方法。在设计区域内应用D-optimal试验设计法抽取样本点,分别通过显式算法获取锯片声学、应力响应以及隐式算法获取锯片变形量,并用试验验证了数值模型的准确性,然后建立了由6个变量参数所决定的锯片的声学、应力以及刚度的二阶响应面模型;利用自适应模拟退火法和蛙跳混合算法对响应面模型进行循环逼近,获得了设计变量影响度以及最优结果。结果表明,在保证刚度和应力许可条件下,通过有限次数值分析,经全局优化后的最佳结构可降低空载噪声4~7dB。数字算例表明,该方法适用于旋转类刀具的声学性能优化设计。     

电动轮自卸车动力总成悬置系统分析与优化

针对电动轮自卸车动力总成结构特点,根据能量解耦理论和最优化理论,建立了电动轮自卸车动力总成悬置系统六自由度动力学模型,以其六自由度解耦率最大为优化目标,采用优化拉丁方法,分析出影响解耦的主要参数,结合遗传算法、模拟退火法对电动轮自卸车动力总成悬置系统进行了优化设计。然后将优化结果视为满足正态分布的随机变量,运用Monte Carlo方法对其进行稳健性分析,以考察设计值的变化对目标函数的影响。结果表明,运用此优化方法对悬置系统进行优化能有效提高解耦率,优化结果稳定可靠,基本满足稳健性要求。     

基于遗传模拟退火算法的机器人全局路径规划

研究了机器人在已知环境下用遗传模拟退火算法进行最优路径搜索的方法，此算法兼备了遗传算法和模拟退火算法的优点，还对路径的转折节点处进行了光滑性的改进，并且通过仿真实验证明了此方法能够快速得到最优路径。     

A hybrid algorithm based on particle swarm optimization and simulated annealing to holon task allocation for holonic manufacturing system

Manufacturing is currently undergoing a revolutionary transition with focus shifting from mass production to mass customization. This trend motivates a new generation of advanced manufacturing systems that can dynamically respond to customer orders and changing production environments. It is becoming increasingly important to develop control architectures that are reconfigurable and fault tolerant. A holonic manufacturing system (HMS) is a system of holons that can cooperate to achieve a common goal or objective. The holonic organization enables the construction of very complex systems that are efficient in the use of resources. This paper focuses on the dynamic re-configuration and task optimization of holonic manufacturing systems (HMS). The concept of dynamic virtual clustering is extended to the control process of a holarchy or holonic organization. A task-oriented clustering mechanism and a corresponding optimization algorithm are presented as an efficient approach to the holonic control in the HMS domain. The mediator-based dynamic virtual clustering mechanism is presented firstly. Then a negotiation strategy based on the contract net protocol is proposed for cooperative action among holons. Finally, a hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for holon task allocation is described to support the optimum organization of a holarchy. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. Simulation results show that the proposed model and algorithm are effective.     

基于混合遗传模拟退火算法的离合器蝶形压紧弹簧优化设计

针对遗传模拟退火算法的局部搜索能力不足,并且可能早熟和遗失最好解;以及传统优化算法全局性较弱,容易陷入局部解的问题.提出一种将遗传模拟退火算法和共轭方向法相结合的混合遗传模拟退火算法.在此基础上对离合器蝶形压紧弹簧优化设计进行研究,并与传统优化方法进行比较,结果表明,该方法简单可行,可为蝶形压紧弹簧以及其他产品的设计提供一种新方法.     

基于过程的优化算法的研究及应用

针对传统机械优化算法所存在的问题，提出了一种基于过程的优化算法。它利用人工神经网络，构造过程系统结构空间，采用随机涨落机制，以模拟退火方式，模拟自然界演化过程进行优化，以过程的收敛性作为全局最优性判据。最终，过程的收敛点即优化问题的最优解。     

火炮身管-反后坐装置集成优化设计方法研究

为减小火炮发射时的后坐阻力,同时减小火炮身管质量,以节制杆结构参数、身管结构参数和内弹道装药参数为设计变量,以身管刚度、强度、寿命、弹丸初速等为约束,建立火炮身管-反后坐装置集成优化设计模型,并采用两种优化方法进行优化,再比较其结果。首先,采用加权组合法将两个目标函数归一为单个目标,应用模拟退火算法进行优化设计,优化后,后坐阻力减小53%,身管质量减小28%。其次,基于Pareto最优理论,采用遗传算法进行优化设计,获得了Pareto最优解集,给设计者提供了更多优化方案;根据工程经验,选取一组优化解,结果发现,对比原设计,后坐阻力减小504%,身管质量减小98%。研究表明,基于Pareto最优理论和遗传算法可以获得更好的优化方案。该研究提供的集成优化模型和算法为火炮身管-反后坐装置一体化设计提供了新方法。
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级进模工步排样中的组合优化问题研究

级进模工步排样是级进模设计的核心 ,理论上属 NP完全问题。本文在分析一般优化方法的基础上 ,将模拟退火算法应用于工步排样 ,建立了组合优化数学模型 ;同时运用层次分析法获取多目标优化函数的权值向量 ,并通过知识引导有效地提高了算法效率。实际应用表明 ,模拟退火算法在优化结果上明显优于常规方法 ,为智能级进模系统实现工步排样优化探索了一条有效途径。     

基于组合优化方法的UUV耐压壳体优化设计研究

无人水下航行器(UUV)的壳体重量大小直接影响到其整体性能。只采用一种优化方法进行耐压壳体结构优化设计很难得到真正的全局最优解,笔者以自适应模拟退火算法、多岛遗传算法和序列二次规划算法为基础,建立了两种不同的组合优化方法,在一定程度上能够确保解的全局最优性。基于该组合优化方法,利用iSIGHT软件,集成自编的肋骨加强薄壁壳体结构的应力及稳定性计算程序,对无人水下航行器(UUV)耐压壳体结构进行优化设计。通过对优化设计前后耐压壳体结构的强度、稳定性及重量进行比较分析,结果表明:采用组合优化算法优化设计得到的无人水下航行器(UUV)耐压壳体在满足壳体结构强度和抗压稳定性要求下,壳体重量极大程度地降低,实现了结构优化。