多目标设备经费分配的混合遗传优化方法

为了探索新的经费分配方法和管理模式,建立了一种新的多目标非线性规划优化模型,提出了一种先进的基于正交试验的新型混合遗传算法来求解该问题。对求解过程中的选择算子、交叉算子和变异算子等进行正交试验,得到的种群个体明显优于基本遗传算法的个体。仿真结果表明,该算法收敛寻优能力强,并能产生很多次优解,是一种高效的方法。     

多目标设备经费分配的混合遗传优化方法

为了探索新的经费分配方法和管理模式，建立了一种新的多目标非线性规划优化模型。提出了一种先进的基于正交试验的新型混合遗传算法来求解该问题。对求解过程中的选择算子、交叉算子和变异算子等进行正交试验，得到的种群个体明显优于基本遗传算法的个体。仿真结果表明，该算法收敛寻优能力强，并能产生很多次优解，是一种高效的方法。     

多目标扰动生物地理学优化算法

提出一种多目标扰动生物地理学优化算法(MDBBO)来求解多目标优化问题(MOPs).该算法基于现有群体中非支配可行解的比率,联合个体非支配等级排序和拥挤距离对个体进行评价;在生物地理迁移策略基础上提出扰动迁移算子并应用于群体进化,增强群体多样性;应用归档种群来保存所获得的非支配可行解,并用循环拥挤距离法对其更新,确保群体的均匀分布性.通过标准函数测试以及与经典算法比较表明了该算法求解MOPs的有效性.     

多目标扰动生物地理学优化算法

提出一种多目标扰动生物地理学优化算法(MDBBO) 来求解多目标优化问题(MOPs). 该算法基于现有群体中非支配可行解的比率, 联合个体非支配等级排序和拥挤距离对个体进行评价; 在生物地理迁移策略基础上提出扰动迁移算子并应用于群体进化, 增强群体多样性; 应用归档种群来保存所获得的非支配可行解, 并用循环拥挤距离法对其更新, 确保群体的均匀分布性. 通过标准函数测试以及与经典算法比较表明了该算法求解MOPs 的有效性.     

一类带约束动态多目标优化问题的进化算法

动态多目标约束优化问题是一类NP-Hard问题,定义了动态环境下进化种群中个体的序值和个体的约束度,结合这两个定义给出了一种选择算子.在一种环境变化判断算子下给出了求解环境变量取值于正整数集Z+的一类带约束动态多目标优化问题的进化算法.通过几个典型的Benchmark函数对算法的性能进行了测试,其结果表明新算法能够较好地求出带约束动态多目标优化问题在不同环境下质量较好、分布较均匀的Pareto最优解集.     

基于双重贡献分配的多目标混合算子进化算法

针对多目标混合算子进化算法中各算子有效选择的自适应问题,提出一种基于双重贡献分配的多目标混合算子进化算法(DCA-MOEA/D).首先,将两种现有的进化算子与两种基于方向引导的差分进化组成算子池,每代个体以轮盘赌的方式从中选择一种进化算子产生子代;然后,根据子代的表现,结合两种方法为各算子分配贡献值,从而确定算子的选择概率;接着,引入外部归档集,根据非支配关系与拥挤度策略对其进行维护;最后,将整个进化过程划分为5个阶段,以达到算子选择中“探索”与“探究”之间的平衡.以IGD与HV为性能评价指标,通过与其他4种多目标进化算法在23个测试函数上的对比,验证所提出算法在收敛性和分布性上的显著优势.     

基于群智能的模糊多目标软件可靠性冗余分配

针对软件可靠性冗余分配问题,建立了一种模糊多目标分配模型,并提出了基于分布估计的细菌觅食优化算法求解该模型。将软件可靠性和成本作为模糊目标函数,通过三角形隶属函数对模糊多目标进行处理,用高斯分布对细菌觅食算法进行优化,并将该优化算法用来求解多目标软件可靠性冗余分配问题,设置不同的隶属函数参数可以得到不同的Pareto最优解,实验数据验证了该群智能算法对解决多目标软件可靠性分配的有效性和正确性,Pareto最优解可为在可靠性和成本之间决策提供依据。     

基于精英选择和个体迁移的多目标遗传算法

提出基于遗传算法求解多目标优化问题的方法,将多目标问题分解成多个单目标优化问题,用遗传算法分别在每个单目标种群中并行搜索.在进化过程中的每一代,采用精英选择和个体迁移策略加快多个目标的并行搜索,提出了控制Pareto最优解数量并保持个体多样性的有限精度法,同时还提出了多目标遗传算法的终止条件.数值实验说明所提出的算法能较快地找到一组分布广泛且均匀的Pareto最优解.     

基于GA的网络最短路径多目标优化算法研究

针对现有基于遗传算法(GA)优化的网络最短路径算法存在优化目标单一、遗传编码质量低、搜索策略间平衡性差、适应度分配效率与灵活性较低等问题,建立一种多目标优化最短路径自适应GA模型,提出了优先级编码和优先级索引交叉算子,引入了遗传算子参数的模糊控制机制和基于自适应加权的适应度分配方法.实验结果表明,该算法的准确性和稳定性高、复杂度合理,实现了对网络设计优化中多目标最短路径问题的高质量求解.     

基于多目标遗传算法的集装箱码头泊位—岸桥分配问题研究

为获得合理的集装箱码头泊位—岸桥分配方案,建立了以最小化船舶在港时间和码头生产成本为目标的优化模型。提出一种多目标遗传算法用于求解该模型,算法中采用染色体组的方式表示可行解,给出了多个约束条件下的交叉算子运算规则,个体的各目标值结合岸桥分配启发式算法求得,并应用Pareto分级方法进行适应度值评价;同时给出了最终实施方案的选择策略。试验算例表明,与单目标优化相比,提出的优化方法能获得使码头综合效益较大的满意解。     

一种基于多目标优化的遗传规划模型

遗传规划常因进化过程中层次树的复杂度无节制的增大，导致运行时间过长而难以直接在工程上应用．本文在传统遗传规划中引入多目标优化原理，这种基于多目标优化的遗传规划模型不仅产生精度更高的最优结果，而且提供了一种在随机搜索过程中有效控制树结构长度的方法．通过对符号回归问题的实验验证，得到了较好的结果．     

A problem space genetic algorithm in multiobjective optimization

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in flexible manufacturing systems. The PSGA is used to generate approximately efficient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the first implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new fitness assignment method, which is used in PSGA, is proposed to find a well-diversified, uniformly distributed set of solutions that are close to the global Pareto set. The proposed fitness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems.     

Multiple- and single-objective approaches to laminate optimization with genetic algorithms

In this paper an application of a genetic algorithm to a material- and sizing-optimization problem of a plate is described. This approach has obvious advantages: it does not require any derivative information and it does not impose any restriction, in terms of convexity, on the solution space. The plate optimization problem is firstly formulated as a constrained mixed-integer programming problem with a single objective function. An alternative multiobjective formulation of the problem in which some constraints are included as additional objectives is also presented. Some numerical results are included that show the appropriateness of the algorithm and of the mathematical model for the solution of this optimization problem, as well as the superiority of the multiobjective approach.     

A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling.

This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for the multiobjective flow shop scheduling problem (FSSP), which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. On the one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in the discrete 0-1 hyperspace by using the updating operator of quantum gate and genetic operators of Q-bit. Moreover, random-key representation is used to convert the Q-bit representation to job permutation for evaluating the objective values of the schedule solution. On the other hand, permutation-based GA (PGA) is applied for both performing exploration in permutation-based scheduling space and stressing exploitation for good schedule solutions. To evaluate solutions in multiobjective sense, a randomly weighted linear-sum function is used in QGA, and a nondominated sorting technique including classification of Pareto fronts and fitness assignment is applied in PGA with regard to both proximity and diversity of solutions. To maintain the diversity of the population, two trimming techniques for population are proposed. The proposed HQGA is tested based on some multiobjective FSSPs. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed HQGA.     

Multiobjective optimization and multiple constraint handling withevolutionary algorithms. I. A unified formulation

In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and differentiability of the cost surface add yet another conflicting element to the decision process. While “better” solutions should be rated higher than “worse” ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or fitness, to such candidate solutions. After reviewing current revolutionary approaches to multiobjective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape     

一类动态非线性约束优化问题的新解法

动态非线性约束优化是一类复杂的动态优化问题,其求解的困难主要在于如何处理问题的约束及时间（环境）变量。给出了一类定义在离散时间（环境）空间上的动态非线性约束优化问题的新解法,从问题的约束条件出发构造了一个新的动态熵函数,利用此函数将原优化问题转化成了两个目标的动态优化问题。进一步设计了新的杂交算子和带局部搜索的变异算子,提出了一种新的多目标优化求解进化算法。通过对两个动态非线性约束优化问题的计算仿真,表明该算法是有效的。     

分层多目标最优化进化算法

文章用进化算法给出了求解二层字典分层多目标最优化的方法,该算法把求解问题转化为多目标最优化,并研究了这两个问题的解集之间的联系。对多目标最优化定义了一个新的选择算子和适应值函数,这样定义的选择算子和适应值函数结合均匀设计能有效地引导搜索,直接求出问题的解而不用逐层求解。数值模拟表明该方法十分有效。     

考虑分配次序的无人机协同目标分配建模与遗传算法求解

本文研究了动态战场环境中的多无人机协同目标分配(MUCTA)问题.首先通过分析无人机(UAV)分配次序对打击任务总收益的影响,设计了动态战场环境的更新规则.将航程代价和任务代价作为惩罚项修正目标函数,建立了考虑分配次序的UAVs协同目标分配优化模型.然后针对模型的物理意义改进了遗传算法基因编码方式,设计了MUCTA遗传算法.该算法利用状态转移思想,引进SDR算子获得多种分配次序种群,同时以单行变异算子修正UAV与目标对应关系,并采用最优个体法和轮盘赌法筛选子代个体.最后仿真结果验证了所设计算法的有效性.     

动态系统的常微分方程组建模—基于不同搜索技术的实验研究

以人口模型和化学反应模型为例,通过大量实验研究比较了分别采用基于两种传统的搜索方法即局部搜索算法和模拟退火算法、遗传算法（简称GA）四者相结合的14种不同算法建立动态系统的常微分方程组模型的实验结果,得到了有关各算法性能比较的一些新的结论。两个实例的实验结果表明：在14种算法中,GP＋GA＋LS－MU算法（即在采用GP的模型结构的优化过程中嵌入采用GA的模型参数的优化过程,并且在每一演化代对种群中的部分个体进行基于GP的标准变异算子产生邻域解的局域搜索过程）是目前解决常微分方程组建模问题的最好算法。