技能组合可选择的赛汝生产系统多能工配置问题

作为以人为中心的赛汝生产系统,如何合理地配置多能工是系统构建的主要问题.针对赛汝生产系统构建的基本问题,考虑工人技能组合的可选择性和工人技能水平的差异性,以最小化最大完工时间和最小化工人工作时间差异为目标,建立赛汝生产系统多能工配置的多目标优化模型.针对模型和解空间的特征,开发并采用基于NSGA-II的多目标优化算法进...     

基于灰色综合关联分析的多目标优化方法

针对现有多目标优化方法存在的搜索性能弱、效率低等问题,提出一种基于灰色综合关联分析的多目标优化方法.该多目标优化方法采用单目标优化算法构建高质量的参考序列,计算参考序列与优化解的目标函数值序列之间的灰色综合关联度,定义基于灰色综合关联度的解支配关系准则,将灰色综合关联度作为多目标优化算法的适应度值.以带顺序相关调整时间的多目标流水车间调度问题作为应用对象,建立总生产成本、最大完工时间、平均流程时间及机器平均闲置时间的多目标函数优化模型.提出基于灰色关联分析的多目标烟花算法,对所建立的多目标优化模型进行优化求解.仿真实验表明,所提出多目标烟花算法的性能优于3种基于不同多目标优化方法的烟花算法及两种经典多目标算法,验证了所提出的多目标优化方法及多目标算法的可行性和有效性.     

An efficient metamodel-based multi-objective multidisciplinary design optimization framework

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.     

基于多目标优化策略的在线学习资源推荐方法

目前在线学习资源推荐较多采用单目标转化方法,推荐过程中对学习者偏好考虑相对不足,影响学习资源推荐精度.针对上述问题,文中提出基于多目标优化策略的在线学习资源推荐模型(MOSRAM),在学习者规划时间内,以同时获得学习者对学习资源类型偏好度最大和难度水平适应度最佳为优化目标,设计具有向邻居均值学习能力和探索新区域能力的多目标粒子群优化算法(NEMOPSO),提出以MOSRAM为核心的在线学习资源推荐方法(NEMOPSO-RA).不同问题规模下融合经典多目标优化算法的推荐方法对比实验表明,NEMOPSO-RA可以有效提高在线学习资源的推荐精度和推荐性能.     

一种基于多策略差分进化的分解多目标进化算法

为了提高多目标优化问题非支配解集合的分布性和收敛性,根据不同差分进化策略的特点,基于切比雪夫分解机制,提出一种基于多策略差分进化的分解多目标进化算法(MOEA/D-WMSDE).该算法首先采用切比雪夫分解机制,将多目标优化问题转化为一系列单目标优化子问题;然后引入小波基函数和正态分布实现差分进化算法的参数控制,探究一种...     

基于动态资源权重的多技能项目调度启发式算法

多技能资源受限项目调度问题中,一个资源可同时具备多项技能,相较于传统的单技能项目调度,其资源分配对调度计划的工期影响程度更大,因此在对多技能项目进行排程时更加重视资源的分配.基于此,从资源视角提出一种启发式算法求解工期最短的调度计划.算法以并行调度为主体,并设计一种动态资源权重计算方法,在每一决策点,首先采用二分图最大匹配法确定当前可排活动集,而后将动态资源权重值作为调度过程中资源分配的依据,其核心思想是将资源灵活度高、对后续活动影响大的资源留置,以待下一决策点调用.最后,为验证算法有效性,对不同参数设置下的算例进行实验,结果表明,相较于随机资源分配算法和静态资源权重算法,新算法具有明显优势.     

基于改进多目标布谷鸟搜索的资源调度算法

针对IaaS(Infrastructure as a Service)云计算中资源调度的多目标优化问题,提出一种基于改进多目标布谷鸟搜索的资源调度算法。在多目标布谷鸟搜索算法的基础上,通过改进随机游走策略和丢弃概率策略提高了算法的局部搜索能力和收敛速度。以最大限度地减少完成时间和成本为主要目标,将任务分配特定的VM(Virtual Manufacturing)满足云用户对云提供商的资源利用的需求,从而减少延迟,提高资源利用率和服务质量。实验结果表明,该算法可以有效地解决IaaS云计算环境中资源调度的多目标问题,与其他算法相比,具有一定的优势。     

认知无线网络中多信道频谱感知周期优化算法

为了使认知无线电次用户发现并使用更多的空闲频谱资源,提出了一种多信道频谱感知周期优化算法。针对实际网络中各授权信道使用规律的不同,本文基于交替更新理论建立了多信道状态转移模型,将各信道感知周期的选取建模为一个带约束条件的多目标优化问题,并采用遗传算法对其求解,获得了相对较优的多信道感知周期向量。仿真结果表明,本文提出的目标函数能够有效衡量多信道空闲频谱资源检测率。当目标网络中包含8个授权信道时,所提算法可发现的空闲频谱资源占实际空闲频谱资源的68.23%,相对于以相同周期对各信道进行频谱感知的OFDM机制提高17.68%。     

Multi-objective genetic algorithm and its applications to flowshop scheduling

In this paper, we propose a multi-objective genetic algorithm and apply it to flowshop scheduling. The characteristic features of our algorithm are its selection procedure and elite preserve strategy. The selection procedure in our multi-objective genetic algorithm selects individuals for a crossover operation based on a weighted sum of multiple objective functions with variable weights. The elite preserve strategy in our algorithm uses multiple elite solutions instead of a single elite solution. That is, a certain number of individuals are selected from a tentative set of Pareto optimal solutions and inherited to the next generation as elite individuals. In order to show that our approach can handle multi-objective optimization problems with concave Pareto fronts, we apply the proposed genetic algorithm to a two-objective function optimization problem with a concave Pareto front. Last, the performance of our multi-objective genetic algorithm is examined by applying it to the flowshop scheduling problem with two objectives: to minimize the makespan and to minimize the total tardiness. We also apply our algorithm to the flowshop scheduling problem with three objectives: to minimize the makespan, to minimize the total tardiness, and to minimize the total flowtime.     

Community detection in complex networks: Multi-objective discrete backtracking search optimization algorithm with decomposition

Community detection is believed to be a very important tool for understanding both the structure and function of complex networks, and has been intensively investigated in recent years. Community detection can be considered as a multi-objective optimization problem and the nature-inspired optimization techniques have shown promising results in dealing with this problem. In this study, we present a novel multi-objective discrete backtracking search optimization algorithm with decomposition for community detection in complex networks. First, we present a discrete variant of the backtracking search optimization algorithm (DBSA) where the updating rules of individuals are redesigned based on the network topology. Then, a novel multi-objective discrete method (MODBSA/D) based on the proposed discrete variant DBSA is first proposed to minimize two objective functions in terms of Negative Ratio Association (NRA) and Ratio Cut (RC) of community detection problems. Finally, the proposed algorithm is tested on some real-world networks to evaluate its performance. The results clearly show that MODBSA/D has effective and promising performance for dealing with community detection in complex networks.     

基于分解的预测型动态多目标粒子群优化算法

针对动态多目标问题求解,提出一种基于分解的预测型动态多目标粒子群优化算法.首先借助分解思想,将目标问题划分为多个不同的子问题,当问题动态变化时,选择对应于不同子问题的优化个体检测环境变化程度,以提高算法对不同动态问题的适应与响应能力;然后,设计一种群体预测策略,通过将目标空间中相同收敛方向上不同时刻的个体位置转换为时间序列,引入时间序列预测方法预测下一刻位置,从而提高预测种群的多样性和有效性,进而有效减少算法在问题变化后的收敛时间;最后,为避免问题发生变化后个体与子问题不匹配,设计一种再匹配策略,以提高预测策略的准确性.实验结果表明,在6个标准动态多目标测试问题上,与2个动态多目标优化算法进行比较,所提出算法在收敛性、分布性与稳定性上均具有显著优势.     

一种求解多目标优化问题的进化算法混合框架

为了提高多目标优化算法的求解性能,提出一种启发式的基于种群的全局搜索与局部搜索相结合的多目标进化算法混合框架.该框架采用模块化、系统化的设计思想,不同模块可以采用不同策略构成不同的算法.采用经典的改进非支配排序遗传算法(NSGA-II)和基于分解的多目标进化算法(MOEA/D)作为进化算法的模块算法来验证所提混合框架的有效性.数值实验表明,所提混合框架具有良好性能,可以兼顾算法求解的多样性和收敛性,有效提升现有多目标进化算法的求解性能.     

基于改进元胞多目标遗传算法的机床主轴优化

为了进一步提高元胞遗传算法在求解多目标优化问题时的收敛性和分布性。在多目标元胞遗传算法的基础上,引入了三维空间元胞,提出了三维元胞多目标遗传算法。采用多目标基准测试函数对该算法进行了测试,并将其与目前比较流行的几种多目标遗传算法进行对比。结果表明,此种算法在收敛性和分布性上取得了更好的效果。采用以上这几种算法分别对机床主轴多目标优化问题进行了求解,相比其他几种算法,改进的多目标元胞遗传算法得到了更优的结果,说明了改进的算法在求解此问题时行之有效。     

网络社区发现的多目标分解粒子群优化算法

通过分析社会网络中社区发现问题的优化目标,构造了社区发现的多目标优化模型,提出一种网络社区发现的多目标分解粒子群优化算法。该算法采用切比雪夫法将多目标优化问题分解为多个单目标优化子问题,使用粒子群优化(PSO)算法对社区结构进行挖掘,并引入了一种新颖的基于局部搜索的变异策略以提高算法的搜索效率和收敛速度,该算法克服了单目标优化算法存在的解单一以及难以发现社区层次结构的缺陷。人工网络及真实网络上的实验结果表明,该算法能够快速准确地挖掘网络社区并揭示社区的层次结构。     

基于蚁群粒子群算法求解多目标柔性调度问题

通过分析多目标柔性作业车间调度问题中各目标的相互关系，提出一种主、从递阶结构的蚁群粒子群求解算法。算法中，主级为蚁群算法，在选择工件加工路径过程中实现设备总负荷和关键设备负荷最小化的目标；从级为粒子群算法，在主级工艺路径约束下的设备排产中实现工件流通时间最小化的目标。然后，以设备负荷和工序加工时间为启发式信息设计蚂蚁在工序可用设备间转移概率；基于粒子向量优先权值的大小关系设计解码方法实现设备上的工序排产。最后，通过仿真和比较实验，验证了该算法的有效性。     

Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm.     

A hybrid multi-objective PSO–EDA algorithm for reservoir flood control operation

Reservoir flood control operation (RFCO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RFCO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RFCO (MO-RFCO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO–EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RFCO problem. MO-PSO–EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RFCO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO–EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO–EDA is suitable for solving MO-RFCO problems.     

A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches

The problem investigated in this study involves an unrelated parallel machine scheduling problem with sequence-dependent setup times, different release dates, machine eligibility and precedence constraints. This problem has been inspired from a realistic scheduling problem in the shipyard. The optimization criteria are to simultaneously minimize mean weighted flow time and mean weighted tardiness. To formulate this complicated problem, a new mixed-integer programming model is presented. Considering the NP-complete characteristic of this problem, two famous meta-heuristics including a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective ant colony optimization (MOACO) which is a modified and adaptive version of BicriterionAnt algorithm are developed. Obviously, the precedence constraints increase the complexity of the scheduling problem in strong sense in order to generate feasible solutions, especially in parallel machine environment. Therefore a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. Due to the fact that appropriate design of parameter has a significant effect on the performance of algorithms, we calibrate the parameters of these algorithms by using new approach of Taguchi method. The performances of the proposed meta-heuristics are evaluated by a number of numerical examples. The results indicated that the suggested MOACO statistically outperformed the proposed NSGA-II in solving the test problems. In addition, the application of the proposed algorithms is justified by a real block erection scheduling problem in the shipyard.     

Modelling and scheduling multi-objective flow shop problems with interfering jobs

This paper studies multi-objective flow shop scheduling problems with interfering jobs. That is, there are two sets of jobs and each of which has its own objective. Some jobs are scheduled so as to minimize makespan while the others are to minimize total tardiness. In this case, the problem was mathematically modeled by a mixed integer linear program. Then, a novel biogeography-based optimization was developed to solve the problem. To evaluate the algorithm, its performance was compared with three well-known algorithms in the literature. The results of the present study show that the proposed algorithm outperforms the other tested algorithms.     

基于决策变量关系的动态多目标优化算法

动态多目标优化问题(DMOPs)需要进化算法跟踪不断变化的Pareto最优前沿,从而在检测到环境变化时能够及时有效地做出响应.为了解决上述问题,提出一种基于决策变量关系的动态多目标优化算法.首先,通过决策变量对收敛性和多样性贡献大小的检测机制将决策变量分为收敛性相关决策变量(CV)和多样性相关决策变量(DV),对不同类型决策变量采用不同的优化策略;其次,提出一种局部搜索多样性维护机制,使个体在Pareto前沿分布更加均匀;最后,对两部分产生的组合个体进行非支配排序构成新环境下的种群.为了验证DVR的性能,将DVR与3种动态多目标优化算法在15个基准测试问题上进行比较,实验结果表明, DVR算法相较于其他3种算法表现出更优的收敛性和多样性.