基于自适应ε支配的快速多目标遗传算法

多目标遗传算法(MOGA)大体上可以分为两个步骤:构造非支配集和保持解分布度。而ε支配能将两者有机地结合起来,具有良好的时间效率及分布度。但是采用ε支配时,其ε参数难以设定,为此文章提出了一种基于自适应ε支配的快速多目标遗传算法(AEMOGA)。通过与其它的2个多目标遗传算法NSGA2和SPEA2比较,实验结果表明该文提出的算法具有良好的时间效率分布性、收敛性及时间效率。     

非支配排序均匀遗传算法*

根据第二代非支配排序遗传算法（NSGA Ⅱ）的不足之处,提出了一种新的多目标遗传算法——非支配排序均匀遗传算法（NSUGA）。新算法采用了多父本多点交叉方式,同时将均匀设计的思想用于算法的交叉操作;新算法还对拥挤距离的计算过程和算法的终止条件进行了改进。通过两个多目标优化测试函数的仿真计算对比,显示NSUGA算法在求解精度、计算效率和避免算法陷于局部最优解方面均优于NSGA II算法。     

多目标差分进化在热连轧负荷分配中的应用

提出一种基于差分进化算法的多目标进化算法, 该算法个体的选择是通过非支配排序和拥挤度距离进行综合考虑. 保证了算法收敛到Pareto最优解集的同时, 提高了最优解个体分布的多样性. 通过与非支配排序遗传算法Ⅱ(NSGA Ⅱ)算法进行仿真对比, 结果显示基于拥挤度排序的多目标差分进化算法在收敛性和Pareto最优解集分布的多样性上均优于NSGA Ⅱ算法. 最后将其引入到热连轧负荷分配优化计算中, 给出了目标函数的表达方式, 对多目标进化算法在热连轧负荷分配计算中的应用进行了研究.     

基于随机算子的快速多目标遗传算法

在NSGA-Ⅱ算法的基础上,对NSGA-Ⅱ构造非支配集的方法进行了改进,用擂台赛法则构造非支配集,当非支配集小于种群大小时,采用随机算子在可行域内随机产生新的解个体填充到下一代父种群中,形成了一种新的多目标遗传算法。在实验部分将改进后的算法和NSGA-II进行了性能比较,实验结果表明改进后的算法具有良好的分布性,算法运行效率也较高。     

基于支配强度的NSGA2改进算法

NSGA2是一种简单、高效且被广泛使用的多目标进化算法(Multi-objective Evolutionary Algorithm,MoEA),但在求解实际工程领域中的高维、复杂非线性多目标优化问题(Multi-objective Optimization Problems,MOP)时,存在无法有效识别伪非支配解、计算效率低、解集收敛性和分布性较差等设计缺陷。对此,文中提出一种基于支配强度的NSGA2改进算法(INSGA2-DS)。新算法采用快速支配强度排序法构造非支配集,引入了考虑方差的拥挤距离公式,并通过自适应精英保留策略动态调整精英保留规模。基于标准测试函数的仿真实验表明,INSGA2-DS算法较好地改善了NSGA2算法的收敛性和分布性。     

一种基于多亲遗传机制的多目标优化算法

给出了进化个体之间的关系和非支配集中不同个体之间的相关性质,参考快速排序的思想,提出了一种有效的构造非支配集的算法.在此基础上,将多亲遗传算法与改进的快速排序构造非支配集的算法相结合,提出了一种基于多亲遗传机制的多目标优化算法.最后对提出算法进行了分析,采用了测试函数进行了仿真实验,获得了理想的实验结果.     

改进多目标进化算法NSGA-Ⅱ运行效率的方法

NSGA-Ⅱ是一种性能优良的多目标进化算法,近年来非常流行.为了进一步改进NSGA-Ⅱ的运行效率,采用了擂台赛法则作为构造非支配集的方法,提出了按需分层的策略.实验结果表明,提出的方法与NSGA-Ⅱ相比具有更少的非支配集层数,支配比较次数和运行时间.     

SOC软硬件划分系统中的关键算法

设计并实现了SOC软硬件划分系统,搭建了软硬件协同设计的平台并描述了软硬件协同设计的流程。运用多目标遗传算法对目标系统的价格、功耗、速度进行优化,采用了基于Pareto支配的适应值赋值、精英保持和密度计算截断操作的方法进行多目标寻优。针对单任务图描述多CPU系统结构的不足,提出采用多任务图来描述的方法,并提出了MTLS性能评估算法,验证系统软硬件划分的优劣。在对比实验中将NSGA2算法运用到本系统中,结果证明论文的多目标寻优算法获得的非支配解80%比NSGA2的非支配解优。     

NSGA-Ⅱ遗传算法在抑制电网谐波中的应用

多目标优化问题在科学和工程等领域是一个热点问题．同时也是研究的一个难点。第一代非支配排序遗传算法NSGA在多目标领域中显示出较大的优势，但是随着应用范围的不断拓宽，其缺点就不断地暴露出来。为了更好地解决多目标优化问题，在NSGA的基础上，研究人员提出了带精英策略的非支配排序遗传算法NSGA-Ⅱ。本文将NSGA-Ⅱ应用在滤除电网谐波的无源滤波器的设计中。经过对某铝厂电网谐波的分析，对初期投资成本和各次单谐波支路的滤波率进行约束。结果表明，该方法不但可以有效滤去大量的电网谐波，还能对初期投资成本进行很好地控制。     

基于多目标遗传算法求解时间窗车辆路径问题

有别于传统的单目标方法,将带时间窗约束的车辆路径问题描述成为一个多目标最优化问题,并为之提出了一种多目标遗传算法。在算法中设计了擂台法则作为构造非支配集的方法,提出了可变爬山率的局部爬山法,并通过将组合种群分成多层非支配集来实现精英保留策略。实验结果表明,该算法能有效地求解车辆路径问题并且为决策者提供了强有力的决策支持。     

快速提高NSGA-Ⅱ算法双目标优化效率的方法

NSGA-Ⅱ是一种性能优良的多目标进化算法,近年来非常流行。为了进一步改进NSGA-Ⅱ在双目标优化时的效率,采取了按需分层的策略,提出了一种新的非支配前沿集分层方法以替代NSGA-II原有的分层方法。与NSGA-Ⅱ的时间复杂度O(N2)相比,新方法的时间复杂度减少为O(kN+NlogN),k为所分前沿层数(k<相似文献   

Automatic calibration of a rainfall–runoff model using a fast and elitist multi-objective particle swarm algorithm

In order to successfully calibrate a numerical model, multiple criteria should be considered. Multi-objective genetic algorithms (MOGAs) have proved effective in numerous such applications, where most of the techniques relying on the condition of Pareto efficiency to compare different solutions. In this paper, a new non-dominated sorting particle swarm optimisation (NSPSO), is proposed, that combines the operations (fast ranking of non-dominated solutions, crowding distance ranking and elitist strategy of combining parent population and offspring population together) of a known MOGA NSGA-II and the other advanced operations (selection and mutation operations) with a single particle swarm optimisation (PSO). The efficacy of this algorithm is demonstrated on the calibration of a rainfall–runoff model, and the comparison is made with the NSGA-II. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well diversity compared to the NSGA-II optimisation framework.     

一种结合多目标免疫算法和线性规划的双行设备布局方法

设备布局对于提高生产效率和降低运营成本具有重要意义. 本文针对半导体加工制造中常见的双行设备布局问题, 提出了一种结合多目标免疫算法和线性规划的双行设备布局方法来同时优化物料流成本和布局面积两个目标. 首先, 建立了问题的混合整数规划模型;其次, 针对问题既含有组合方面(机器排序)又含有连续方面(机器精确位置)的特点, 分别设计了一种多目标免疫算法来获取非支配的机器排序集合, 提出了一种基于线性规划的方法来构造任一非支配机器排序对应的连续的非支配解集;最后, 由所有连续的非支配解来构造最后Pareto解. 实验结果表明, 该方法对于小规模问题能获得最优Pareto解, 对于大规模问题能够获得具有良好分布性的Pareto解且其质量远好于NSGA-II和精确算法获得的解.     

一种改进的非支配排序多目标遗传算法

多目标进化算法的研究目标主要是使算法快速收敛,并且广泛而均匀分布于问题的非劣最优域。在NSGA-II算法的基础上,提出了一种新的构造种群的策略——按照聚集距离选取部分非支配个体,并选取部分较好的支配个体形成下一代种群。该策略与原算法相结合后的算法（NSGA-II+IMP）与原NSGA-II进行比较,结果表明新算法较好地改善了分布性和收敛性。     

Multi-objective GA with fuzzy decision making for security enhancement in power system

Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.     

量子多目标进化算法研究

本文首次将量子计算的理论用于多目标优化,提出量子多目标进化算法（QMOEA）,其采用量子位染色体表示法,利用量子门旋转策略和量子变异实现群体的进化,使用ε支配关系构造外部种群以此保持算法的较好分布性,提出基于快速排序的非劣最优解构造方法加快算法运行效率,实验表明,这种方法与经典的多目标进化算法SPEA2相比,其收敛性更好且分布更均匀     

An efficient non-dominated sorting method for evolutionary algorithms

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.     

Multi-objective topology and sizing optimization of truss structures based on adaptive multi-island search strategy

This paper uses genetic algorithm to handle the topology and sizing optimization of truss structures, in which a sparse node matrix encoding approach is used and individual identification technique is employed to avoid duplicate structural analysis to save computation time. It is observed that NSGA-II could not improve the convergence of non-dominated front at latter generations when solving multi-objective topology and sizing optimization of truss structures. Therefore, an adaptive multi-island search strategy for multi-objective optimization problem (AMISS-MOP) is developed to enhance the convergence. Meanwhile, an elitist strategy based on archive set is introduced to reduce the size of non-dominated sorting to improve computation efficiency. Two numeric examples are presented to demonstrate the performance of AMISS-MOP. Results show that the global Pareto front could be found by AMISS-MOP, the convergence is improved as generation increases, and the time spent on non-dominated sorting is reduced.     

基于NSGA-II 算法的平面并联机构动态性能多目标优化

针对高速高加速度的平面并联机构,采用面向控制系统的机构设计方法,对机械结构进行优化设计． 分别在运动学、动力学层面上提出多个性能指标,以其作为目标函数和约束条件建立了标准的多目标优化模型．基 于NSGA-II 算法求解多目标优化问题,进行尺度综合．最终结果表明,优化后的机构能较大程度上消除系统耦合, 提升动态性能,为高速控制系统设计提供良好的机械硬件平台．