基于人工免疫算法的多目标函数优化

提出了一种新型的人工免疫算法用来解决多目标函数优化问题。基于自然免疫系统固有的优良特性对算法进行了设计和分析。最后,算法对3个较复杂的多目标问题进行了优化,优化结果能很好地覆盖问题的Paret。最优面,并且把算法与某些混合遗传算法进行了对比实验,表明人工免疫算法在解决多目标优化问题上具有可观的研究前景。     

采用混合遗传算法的有限推力轨道拦截优化

在轨道拦截问题中,研究了天基动能拦截器(KKV)的有限推力轨道拦截优化问题.针对在有限推力条件下,确定速度增益变轨方案,提出建立了轨道拦截优化数学模型,并将复形调优算法加入到遗传算法中,利用该混合遗传算法,以发动机燃料消耗质量最小、拦截时间最短和拦截脱靶量最小为综合优化指标,对轨道拦截进行了优化.以太阳同步轨道上的天基KKV拦截GPS卫星轨道上的目标点为例,分析了混合遗传算法用于轨道拦截优化的性能进行了仿真.仿真结果表明,混合遗传算法能有效解决轨道拦截这一复杂非线性多目标优化问题,同时增强了局部搜索能力,提高了计算效率.     

多线路准快速公交调度优化及混合遗传禁忌算法仿真

针对多线路准快速公交社会效益及企业运营效益最大化的多目标调度问题建立了优化模型。根据问题的特点设计了组合优化调度问题的混合遗传—禁忌算法,结合深圳市龙岗区公交调查数据对该模型进行了验证,计算结果及分析表明该算法比遗传算法及禁忌算法在求解此类问题时有更高的效率。     

混合遗传算法求解高超音速飞行器弹道优化

高超音速巡航导弹最优上升轨道设计问题是终端时刻未定、终端约束苛刻的最优控制问题,经典算法求解这类问题时对初值选取敏感、局部收敛等问题表现得比较突出.针对上述问题,将具有良好全局收敛性的遗传算法应用到导弹最优上升段设计问题求解中,为了提高遗传算法的收敛速度和克服早熟问题,结合单纯形和Powell算法的优点,设计了两种混合遗传算法.通过所设计的两种混合遗传算法的求解结果和分别用单纯形以及Powell算法的求解结果进行比较,得出所设计的混合遗传算法是更有效的求解高超声速巡航导弹轨迹优化的方法.     

一种新的求解多目标优化问题的混合遗传算法

分析了遗传算法和单纯形算法的特点，充分利用二者的优点，把单纯形算法作为一种局部搜索方法与变权系数遗传算法有机地结合起来，提出了一种求解多目标优化问题的混合遗传算法。数值实验表明该混合遗传算法能求得问题的数量更多、分布更广的Pareto最优解。     

运输方式选择多目标优化问题的混合遗传算法

多式联运运输方式选择问题直接关系到货物运输的费用、时间和运输质量。首先分析了多式联运运输方式选择多目标优化问题的数学模型及虚拟运输网络图;其次,将基于信息熵的多属性决策方法引入适应度函数的设计中,提出了一种求解多式联运运输方式选择多目标优化问题的混合遗传算法,给出了染色体编码、遗传算子设计、染色体有效性判断和修正的方法;最后用示例对算法的有效性进行了验证。     

一种求解全局优化问题的新混合遗传算法

把简化的二次插值法融入实数编码遗传算法,构成适于求解全局优化问题的混合遗传算法,该混合算法可以较好解决遗传算法的早熟收敛问题,提高了收敛速度,改善了解的质量,并减少了计算量.由于该混合遗传算法对目标函数的性质没有要求,适合求解大规模问题和工程实际问题.通过对23个标准测试函数的仿真实验,并和已有算法的比较,结果表明本文提出的混合遗传算法是非常有效的.     

快速成型制造中的零件分层方向优化新技术

给出了快速成型制造中关于零件制作方向的3种单目标优化模型,单目标优化分别以提高原型表面质量、减少支撑和减少制作时间为目标;单目标优化模型的解集包含了此目标意义下零件的最优和较优分层方向。在此基础上,提出了零件制作方向优化的带权-p模理想点综合决策模型。文章设计了一种经改进的混合遗传算法,并用其对零件制作方向优化的3种单目标模型及综合决策模型进行了求解。     

双种群混合遗传算法求解具有预防性维护的分布式柔性作业车间调度问题

在实际生产过程中,生产调度和设备维护相互影响,因此两者应该统筹优化.为研究具有预防性维护的分布式柔性作业车间调度问题,以最小化最大完工时间为目标,提出一种双种群混合遗传算法.结合问题特性,设计三维编码以及对应的机器解码方案,采用不同的策略初始化种群以均衡一部分工厂负载,为双种群设计不同的交叉变异算子提高算法的多样性,并利用交换精英解的方法实现两个种群的协作优化,同时针对关键工厂和预防性维护操作设计相应的局部搜索.最后对比现有算法,在同构和异构工厂的算例上进行实验,使用正交试验法优化算法参数设置.实验结果验证了局部搜索以及种群协作的有效性和双种群混合遗传算法求解具有预防性维护的分布式柔性作业车间调度问题的优越性.     

求解车辆路径安排问题的混合遗传算法

讨论了具有容量限制的车辆路径安排问题,设计了一个高效混合遗传算法。针对简单遗传算法易收敛于局部最优解的缺点,算法设计了交叉规则和选择策略。只有当两个个体的评价函数值满足一定条件时,才能进行交叉操作。采用优良个体保留策略执行选择操作,设计了保留函数。算法依据顶点间的位置关系,设计了优化策略,在每代进化中按概率选择一定数量的个体执行优化操作。数据实验表明,该算法是一个有效的求解车辆路径安排问题的混合遗传算法。     

Adaptive gradient-enhanced kriging model for variable-stiffness composite panels using Isogeometric analysis

A vibration isolation system is designed using novel hybrid optimization techniques, where locations of machines, locations of isolators and layout of supporting structure are all taken as design variables. Instead of conventional parametric optimization model, the 0-1 programming model is established to optimize the locations of machines and isolators so that the time-consuming remeshing procedure and the complicated sensitivity analysis with respect to position parameters can be circumvented. The 0-1 sequence for position design variables is treated as binary bits so as to reduce the actual number of design variables to a great extent. This way the 0-1 programming can be solved in a quite efficient manner using a special version of genetic algorithm(GA) that has been published by the authors. The layout of supporting structure is optimized using SIMP based topology optimization method, where the fictitious elemental densities are taken as design variables ranging from 0 to 1. Influence of different design variables is firstly investigated by numerical examples. Then a hybrid multilevel optimization method is proposed and implemented to simultaneously take all design variables into account.     

A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem.A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.     

区间数型多式联运路线优化问题的混合遗传算法*

多式联运路线优化问题直接关系到货物运输的费用、时间和运输质量。首先分析了多式联运路线优化问题的数学模型及虚拟运输网络图;其次,将区间数排序的思想引入适应度函数的设计中,提出了一种求解区间数型多式联运路线优化问题的混合型遗传算法,给出了染色体编码、遗传算子设计、约束判断与调整及群体多样性控制的方法;最后用示例对算法的有效性进行了验证,算法的提出可为多式联运经营者的决策提供数据参考。     

A hybrid surrogate and pattern search optimization method and application to microelectronics

A hybrid method for robust and efficient optimization process is developed by integrating a new response surface method and pattern search algorithm. The method is based on: (1) multipoint approximations of the objective and constraint functions, (2) a multiquadric radial basis function (RBF) for the zeroth-order function approximation and a new RBF plus polynomial-based moving least-squares approximation for the first-order enhanced function approximation, and (3) a pattern search algorithm to impose a descent condition and applied adaptive subregion management strategy. Several numerical examples are presented to illustrate accuracy and computational efficiency of the proposed method for both function approximation and design optimization. To demonstrate the effectiveness of the proposed hybrid method, it is applied to obtain optimum designs of a microelectronic packaging system. A two-stage optimization approach is proposed for the design optimization. The material properties of microelectronic packaging system and the shape parameters of solder ball are selected as design variables. Through design optimization, significant improvements of durability performances are obtained using the proposed hybrid optimization method.     

基于复杂昂贵仿真的体系效能多目标优化

效能优化是实现体系结构设计、多方案配置等工作的重要途径.体系仿真系统通常具有组成结构复杂、连续离散混合、输入输出变量多、运行开销大等特点,导致效能优化面临多目标、混合变量、多峰值、低效率等问题,提出一种基于复杂昂贵仿真的体系效能优化算法.针对昂贵仿真问题,提出基于聚类与空间填充准则相结合的开发-探索序贯元模型策略;引入...     

A mixed GA-PSO-based approach for performance-based design optimization of 2D reinforced concrete special moment-resisting frames

A mixed genetic algorithm and particle swarm optimization in conjunction with nonlinear static and dynamic analyses as a smart and simple approach is introduced for performance-based design optimization of two-dimensional (2D) reinforced concrete special moment-resisting frames. The objective function of the problem is considered to be total cost of required steel and concrete in design of the frame. Dimensions and longitudinal reinforcement of the structural elements are considered to be design variables and serviceability, special moment-resisting and performance conditions of the frame are constraints of the problem. First, lower feasible bond of the design variables are obtained via analyzing the frame under service gravity loads. Then, the joint shear constraint has been considered to modify the obtained minimum design variables from the previous step. Based on these constraints, the initial population of the genetic algorithm (GA) is generated and by using the nonlinear static analysis, values of each population are calculated. Then, the particle swarm optimization (PSO) technique is employed to improve keeping percent of the badly fitted populations. This procedure is repeated until the optimum result that satisfies all constraints is obtained. Then, the nonlinear static analysis is replaced with the nonlinear dynamic analysis and optimization problem is solved again between obtained lower and upper bounds, which is considered to be optimum result of optimization solution with nonlinear static analysis. It has been found that by mixing the analyses and considering the hybrid GA-PSO method, the optimum result can be achieved with less computational efforts and lower usage of materials.     

An ant colony optimization approach to multi-objective optimal design of symmetric hybrid laminates for maximum fundamental frequency and minimum cost

An ant colony optimization algorithm for optimum design of symmetric hybrid laminates is described. The objective is simultaneous maximization of fundamental frequency and minimization of cost. Number of surface and core layers made of high-stiffness and low-stiffness materials, respectively, and fiber orientations are the design variables. Optimal stacking sequences are given for hybrid graphite/epoxy-glass/epoxy laminated plates with different aspect ratios and number of plies. The results obtained by ant colony optimization are compared to results obtained by a genetic algorithm and simulated annealing. The effectiveness of the hybridization concept for reducing the weight and keeping the fundamental frequency at a reasonable level is demonstrated. Furthermore, it is shown that the proposed ant colony algorithm outperforms the two other heuristics.     

A new hybrid optimization algorithm

We develop a new optimization algorithm that combines the genetic algorithm and a recently proposed global optimization algorithm called the nested partitions method. The resulting hybrid algorithm retains the global perspective of the nested partitions method and the local search capabilities of the genetic algorithm. We also present a detailed application of the new algorithm to a NP-hard product design problem and it is found empirically to outperform a pure genetic algorithm implementation, particularly for large problems.     

Stacking sequence optimization with genetic algorithm using a two-level approximation

We propose a new method for laminate stacking sequence optimization based on a two-level approximation and genetic algorithm (GA), and establish an optimization model including continuous size variables (thicknesses of plies) and discrete variables (0/1 variables that represent the existence of each ply). To solve this problem, a first-level approximate problem is constructed using the branched multipoint approximate (BMA) function. Since mixed-variables are involved in the first-level approximate problem, a new optimization strategy is introduced. The discrete variables are optimized through the GA. When calculating the fitness of each member in the population of GA, a second-level approximate problem that can be solved by the dual method is established to obtain the optimal thicknesses corresponding to the each given ply orientation sequence. The two-level approximation genetic algorithm optimization is performed starting from a ground laminate structure, which could include relatively arbitrarily discrete set of angles. The method is first applied to cylindrical laminate design examples to demonstrate its efficiency and accuracy compared with known methods. The capacity of the optimization strategy to solve more complex problems is then demonstrated using a design example. With the presented method, the stacking sequence in analytical tools can be directly taken as design variables and no intermediate variables need be adopted.     

基于遗传算法的混合优化策略研究

最优化问题是工程设计、科学研究、经济管理等众多领域经常遇到的一类问题。随着待解决问题范围的不断扩大以及优化算法研究的不断深入,混合优化策略已成为解决大规模、高复杂度优化问题的一种重要而有效的方法。介绍了遗传算法、贪婪法、模拟退火算法、禁忌搜索的基本原理,阐述了各种算法的优缺点;针对各单一算法存在的缺陷和不足,对三种以遗传算法为主体框架的混合优化算法进行了分析;最后,指出了混合优化算法存在的问题及今后的发展方向。