Global optimization method using SLE and adaptive RBF based on fuzzy clustering

High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function(RBF) based on fuzzy clustering(ARFC) is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration(SLE) is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method(ARSM) proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.     

Interior search algorithm (ISA): A novel approach for global optimization

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune.     

基于多组混合元模型方法的汽车轻量化设计

针对工程中复杂耗时的黑匣子问题,提出一种基于多组混合元模型的全局最优化方法。该方法同时应用三组元模型进行搜索,每组都包含克里金、二阶多项式及径向基函数的元模型。应用该优化方法对某车型的后车架进行轻量化设计后,后车架子系统的质量减少7.2kg,并提高了该子系统的刚度。研究结果表明,与HAM法相比,新方法在搜索效率和精度上都有显著提高。     

设计空间差别处理优化方法及其在汽车轻量化设计中的应用

提出了一种设计空间差别处理方法,改进了传统的设计空间移除方法会移除全局最优的弱点。首先应用昂贵点构建一个逐渐缩小的重点空间,同时将在设计空间移除方法中被删除的空间定义为其他空间,然后每次迭代都应用二阶多项式响应面（QF）同时搜索这两类空间,并分别从中选取数目不同的新的昂贵点参与QF的更新和重建。该方法采用在其他空间中选择少量新的昂贵点来代替移除空间,有效地避免了局部最优的陷阱。多个标准函数算例的验证表明,新的方法具有较高的精度和效率。将该方法应用于某款车的后车架轻量化设计中,经过优化,后车架系统的质量减小了7.67kg,即整个系统质量减小了10.4%,且其刚度性能得到提高。与以前提出的混合自适应元模型方法相比,新方法的精度和效率都有显著提高。     

New optimization method, the algorithms of changes, for heat exchanger design

Heat exchangers are widely used in the process engineering such as the chemical industries,the petroleum industries,and the HVAC applications etc.An optimally designed heat exchanger cannot only help the optimization of the equipment size but also the reduction of the power consumption.In this paper,a new optimization approach called algorithms of changes (AOC) is proposed for design and optimization of the shell-tube heat exchanger.This new optimization technique is developed based on the concept of the book of changes (I Ching) which is one of the oldest Chinese classic texts.In AOC,the hexagram operations in I Ching are generalized to binary string case and an iterative process,which imitates the I Ching inference,is defined.Before applying the AOC to the heat exchanger design problem,the new optimization method is examined by the benchmark optimization problems such as the global optimization test functions and the travelling salesman problem (TSP).Based on the TSP results,the AOC is shown to be superior to the genetic algorithms (GA).The AOC is then used in the optimal design of heat exchanger.The shell inside diameter,tube outside diameter,and baffles spacing are treated as the design (or optimized) variables.The cost of the heat exchanger is arranged as the objective function.For the heat exchanger design problem,the results show that the AOC is comparable to the GA method.Both methods can find the optimal solution in a short period of time.     

改进鲸鱼优化算法及涡轮盘结构优化

涡轮盘是航空发动机的核心零件,通过智能优化算法对涡轮盘结构进行优化,降低涡轮盘质量,有助于提升推重比。研究并改进了新型鲸鱼优化算法对涡轮盘截面进行结构尺寸优化。改进的鲸鱼优化算法中创新引入差分变异策略,交叉操作和常规变异策略以增强鲸鱼优化算法跳出局部最优解的能力。特别地,以改进的鲸鱼优化算法（Decomposition evolution based whale optimization algorithm,DEWOA）为例对Isight平台进行二次开发,与Isight平台中的五种算法和基本鲸鱼优化算法就八个单目标测试函数进行了对比,在均值和方差等指标验证了改进算法的稳定性和寻优性能。而且,将结合改进鲸鱼优化算法的Isight优化模块组件与有限元分析方法在Isight平台中集成为全自动化优化流程,对航空发动机涡轮盘进行结构优化,并与其他四种算法的涡轮盘结构优化结果进行对比。实验结果表明改进的鲸鱼优化算法对涡轮盘减重达26.09%,超过自适应模拟退火算法减重比2.24%,超过多岛遗传算法减重比5.29%,超过鲸鱼优化算法减重比1.94%,落后于指针自动优化算法减重比0.39%,但改进的鲸鱼优化算法收敛速度更快,达到最优解的代价更小,表明了改进鲸鱼优化算法在工程实际问题上的实用性和通用性。     

DISCRETE OPTIMIZATION APPROACH FOR 3-D SPACE PLATE-SYSTEM STRUCTURE

ＤＩＳＣＲＥＴＥＯＰＴＩＭＩＺＡＴＩＯＮＡＰＰＲＯＡＣＨＦＯＲ３－ＤＳＰＡＣＥＰＬＡＴＥ－ＳＹＳＴＥＭＳＴＲＵＣＴＵＲＥ￥ＮｉｅＳｈａｏｍｉｎ；ＪｉｎＭｉａｏ（ＹａｎｓｈａｎＵｎｉｖｅｒｓｉｔｙ）Ａｂｓｔｒａｃｔ：Ａｎｅｗｎｏｎｌｉｎｅａｒｏｐｔｉｍ...     

变可信度近似模型及其在复杂装备优化设计中的应用研究进展

变可信度近似模型通过融合不同精度分析模型的数据,可有效平衡近似模型预测性能和建模成本之间的矛盾,在复杂装备优化设计中受到广泛的关注。综述变可信度近似模型及其在复杂装备优化设计中的应用研究进展。概述三类常用变可信度近似建模方法的基本思想,并介绍变可信度近似建模方法研究的最新进展。回顾面向变可信度近似模型试验设计方法的发展现状,包括一次性试验设计方法和序贯试验设计方法。综述直接影响变可信度近似模型优化设计求解精度和效率的两类近似模型管理策略,探讨基于变可信度近似模型的智能优化和可靠性/稳健性优化这两个领域前沿问题。归纳总结变可信度近似模型应用于复杂装备优化设计的现状。针对变可信度近似建模及其优化设计给出了一些应用建议,并指出未来值得深入研究的方向。     

扇形采样约束多目标差分进化算法及工程应用

工程实践中存在大量约束多目标优化问题(Constrained multi-objective optimization problems, CMOPs),多目标进化算法是求解这类问题的一类有效方法。引入扇形采样技术,将二次变异双种群差分进化算法和约束处理方法相结合,设计求解CMOPs的进化算法——基于扇形采样的约束多目标差分进化算法(Sector-sampling-based constrained multi-objective differential evolution algorithm, SS-CMODE)。扇形采样可避免耗时的非劣操作,且能保证Pareto最优解集的良好逼近性和多样性。通过3个典型CMOPs的对比测试,表明SS-CMODE的解集均匀性和计算效率明显优于对比算法。以J23-80机械压力机使用的双曲柄串联机构多目标优化为例,研究新算法求解工程问题的有效性。以锻冲工作阶段平均速度波动最小和力传动性能最优为目标,建立机构的约束多目标优化模型,再应用SS-CMODE求解该问题。结果表明,该算法能求出多组满足约束条件的Pareto最优解,且解集均匀性良好。     

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling problem. The new algorithm is based on the principles of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, is developed. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems and comparing results with other algorithms in literature. Comparing results indicate that PSO-based a     

Fast engineering optimization: A novel highly effective control parameterization approach for industrial dynamic processes

Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes.     

基于均匀布点的全局优化方法

把实验设计中的均匀设计思想引入优化设计,提出了一种将确定性和随机性相结合的优化方法。该方法根据均匀设计原理在优化模型的设计变量空间内均匀分布一系列点,然后,将可行域内的上述系列布点作为优化模型的一系列局部最优点。最后,比较所有局部最优点的最优值,即认为在一定程度上获得了该优化问题的全局最优解。该算法可求取非线性多峰函数的全局最优解。编制了计算程序,给出了计算实例,计算结果表明该设计方法是可行的。     

Improved Differential Evolution with Shrinking Space Technique for Constrained Optimization

Most of the current evolutionary algorithms for constrained optimization algorithm are low computational efficiency. In order to improve efficiency, an improved differential evolution with shrinking space technique and adaptive trade-off model, named ATMDE, is proposed to solve constrained optimization problems. The proposed ATMDE algorithm employs an improved differential evolution as the search optimizer to generate new offspring individuals into evolutionary population. For the constraints, the adaptive trade-off model as one of the most important constraint-handling techniques is employed to select better individuals to retain into the next population,which could effectively handle multiple constraints. Then the shrinking space technique is designed to shrink the search region according to feedback information in order to improve computational efficiency without losing accuracy.The improved DE algorithm introduces three different mutant strategies to generate different offspring into evolutionary population. Moreover, a new mutant strategy called ‘‘DE/rand/best/1' is constructed to generate new individuals according to the feasibility proportion of current population. Finally, the effectiveness of the proposed method is verified by a suite of benchmark functions and practical engineering problems. This research presents a constrained evolutionary algorithm with high efficiency and accuracy for constrained optimization problems.     

Decomposition method of complex optimization model based on global sensitivity analysis

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.     

基于计算试验设计与代理模型的飞行器近似优化策略探讨

现代飞行器设计优化中广泛应用高精度分析模型以提高设计可信度与综合性能,但是也带来了计算复杂性问题。为了有效缓解计算耗时的问题,基于计算试验设计与代理模型的飞行器近似优化策略成为研究热点。近似优化策略通过构造合理的近似模型引导优化过程快速收敛到最优解,从而达到降低计算成本,缩短设计周期的目的。根据广泛的文献调研,对飞行器近似优化策略的发展现状进行详细探讨。给出近似优化策略的定义、求解流程、特点以及关键技术。对计算试验设计方法、代理模型方法、精度校验与代理模型选择方法等技术进行综述。围绕静态与自适应两类近似优化策略,重点讨论典型的代理模型管理与更新策略与收敛准则。针对飞行器多学科设计优化问题,探讨近似优化策略与分解策略在求解效率与收敛性方面的技术特点。通过数值算例对各项关键技术的特点进行较详尽的对比分析与总结,并依托飞行器设计优化工程实例对近似优化策略的综合性能进行探讨,指出不同近似优化策略的适用范围。研究结果表明,飞行器近似优化策略在优化效率、全局收敛性以及鲁棒性等方面体现出较显著的优势,具有良好的工程应用前景。指出飞行器近似优化策略的未来研究方向。     

Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for an...     

面向工程的优化算法性能实验研究

针对10种工程常用的优化算法进行实验研究,选取12个优化算法Benchmark测试函数进行算法评价,包括连续的和不连续的、凸的和非凸的、单峰的和多峰的、二次的和非二次的、低维的和高维的、有约束的和无约束的测试函数。对实验结果进行分析,从解空间的角度对算法的特性进行分析总结,给出了算法选取的准则。     

大型机械结构的分层动态优化方法

针对大型机械结构动态优化设计维数高,同时涉及外形尺寸和截面尺寸两类变量,采用整体优化策略,存在收敛困难的问题,提出了采用分层优化结合子结构方法的机械结构动态优化策略。以有限元方法为基础,将外形尺寸和截面尺寸分离到两个相对独立的设计空间,从而将整体优化问题分解为整体层优化和局部层优化两个子优化问题。在整体层以整体结构动态特性最优为目标,完成对外形尺寸的优化;在局部层以子结构动态特性最优为目标,完成对截面尺寸的优化;两层优化交替进行直至问题最后收敛。某型大跨自动扶梯金属结构的动态优化工程实例表明,该方法优化效果良好且优化效率高。     

改进遗传算法在减速器优化设计中的应用

针对简单遗传算法的早熟现象及不能处理带有复杂约束的优化问题,提出一种基于乘子法与伪并行遗传算法的改进遗传算法,并将其应用于斜齿轮减速器优化设计中。计算结果表明改进遗传算法,全局寻优能力强。     

基于改进PSO的可靠性稳健优化计算方法

基于现代设计方法和可靠性设计理论,建立了多目标约束优化数学模型,根据动态加速常数和速度自适应的改进粒子群算法(PSO),用编写的程序代码实现了数学模型的数值化求解。同时研究了加速常数和粒子速度的变化对优化计算结果的影响规律。算例表明,提出的可靠性稳健优化计算方法与传统方法相比,具有简便易行、能迅速得到结构可靠性稳健优化设计信息的优点,适合工程应用。