MOPSO算法及其在地下水监测网布局优化中的应用

在已有多目标粒子群优化算法（CMOPSO）研究和分析的基础上,为提高算法的聚合性和分布性,设计了一种新的精英档案维护及全局最优值选取策略,同时,使用动态全局最优值设置策略对原有算法的粒子速度更新公式进行扩展,以增强粒子的搜索能力,克服早熟现象。通过对疏勒河项目区地下水监测网空间布局多目标优化计算,表明该算法是求解大规模复杂多目标优化问题的一种有效手段。     

Multi-objective optimal fixture layout design

This paper addresses a major issue in fixture layout design to determine and evaluate the acceptable fixture designs based on multiple quality criteria and to select an optimal fixture with appropriate trade-offs among multiple performance requirements. The performance objectives considered are related to the fundamental requirements of kinematic localization and total fixturing (form-closure). Three performance objectives are defined as the workpiece localization accuracy and the norm and dispersion of the locator contact forces. The paper focuses on multi-criteria optimal design with a hierarchical approach. An efficient interchange algorithm is extended and used for different practical cases, leading to proper trade-off strategies for performing fixture synthesis. Examples are given to illustrate empirical observations with respect to the proposed approach and its effectiveness.     

Multi-objective ordinal optimization for simulation optimization problems

Ordinal optimization (OO) has been successfully applied to accelerate the simulation optimization process with single objective by quickly narrowing down the search space. In this paper, we extend the OO techniques to address multi-objective simulation optimization problems by using the concept of Pareto optimality. We call this technique the multi-objective OO (MOO). To define the good enough set and the selected set, we introduce two performance indices based on the non-dominance relationship among the designs. Then we derive several lower bounds for the alignment probability under various scenarios by using a Bayesian approach. Numerical experiments show that the lower bounds of the alignment probability are valid when they are used to estimate the size of the selected set as well as the expected alignment level. Though the lower bounds are conservative, they have great practical value in terms of narrowing down the search space.     

Multi-objective layout optimization of a satellite module using the Wang-Landau sampling method with local search

The layout design of satellite modules is considered to be NP-hard. It is not only a complex coupled system design problem but also a special multi-objective optimization problem. The greatest challenge in solving this problem is that the function to be optimized is characterized by a multitude of local minima separated by high-energy barriers. The Wang-Landau (WL) sampling method, which is an improved Monte Carlo method, has been successfully applied to solve many optimization problems. In this paper we use the WL sampling method to optimize the layout of a satellite module. To accelerate the search for a global optimal layout, local search (LS) based on the gradient method is executed once the Monte-Carlo sweep produces a new layout. By combining the WL sampling algorithm, the LS method, and heuristic layout update strategies, a hybrid method called WL-LS is proposed to obtain a final layout scheme. Furthermore, to improve significantly the efficiency of the algorithm, we propose an accurate and fast computational method for the overlapping depth between two objects (such as two rectangular objects, two circular objects, or a rectangular object and a circular object) embedding each other. The rectangular objects are placed orthogonally. We test two instances using first 51 and then 53 objects. For both instances, the proposed WL-LS algorithm outperforms methods in the literature. Numerical results show that the WL-LS algorithm is an effective method for layout optimization of satellite modules.     

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.     

一类Job-shop多目标优化调度方法

提出了一类Job-shop多目标优化调度方法。构建了一个以工件平均流程时间最短、全部工件完工时间最短的Job-shop优化调度模型,并针对模型提出了一种分阶段混合变异的禁忌搜索算法。为增强算法收敛性,利用“逆序变异”和“基因段交换变异”找到了较好的初解,在此基础上利用“基因交换变异”继续搜索近优解。以10个算例验证了提出的Job-shop多目标优化调度方法的正确性和有效性。     

基于粒子群优化算法的约束布局优化

布局优化是NP难问题，也是复杂的非线性约束优化问题．针对这个问题，将新的基于粒子群优化的方法应用于布局参数的优化，提出了适合粒子群优化的约束处理，并通过与直接搜索算法的混合，加强了算法在局部区域的搜索能力．通过实例将该算法与乘子法以及基于遗传算法的布局优化方法进行了比较．仿真结果表明，该算法可以提高布局优化问题解的质量，同时降低计算费用．     

不确定可靠性优化问题的多目标粒子群优化算法

针对元件可靠性为区间值的系统可靠性优化问题, 提出一种区间多目标粒子群优化方法. 首先, 建立问题的区间多目标优化模型; 然后, 利用粒子群算法优化该模型, 定义一种不精确Pareto 支配关系, 并给出编码、约束处理、外部存储器更新、领导粒子选择等关键问题的解决方法; 最后, 将该方法应用于可靠性优化问题实例, 验证了方法的有效性.

     

Multi-objective optimization using teaching-learning-based optimization algorithm

Two major goals in multi-objective optimization are to obtain a set of nondominated solutions as closely as possible to the true Pareto front (PF) and maintain a well-distributed solution set along the Pareto front. In this paper, we propose a teaching-learning-based optimization (TLBO) algorithm for multi-objective optimization problems (MOPs). In our algorithm, we adopt the nondominated sorting concept and the mechanism of crowding distance computation. The teacher of the learners is selected from among current nondominated solutions with the highest crowding distance values and the centroid of the nondominated solutions from current archive is selected as the Mean of the learners. The performance of proposed algorithm is investigated on a set of some benchmark problems and real life application problems and the results show that the proposed algorithm is a challenging method for multi-objective algorithms.     

Simulation-based layout optimization for multi-station assembly lines

Journal of Intelligent Manufacturing - This article presents a novel approach for the automated 3D-layout planning of multi-station assembly lines. The planning method is based on a comprehensive...     

Swarm-based intelligent optimization approach for layout problem

Layout problem is a kind of NP-Complete problem. It is concerned more and more in recent years and arises in a variety of application fields such as the layout design of spacecraft modules, plant equipment, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are considered powerful tools for solving this kind of problems. While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In proposed approach, chaos initialization and multi-subpopulation evolution strategy based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure is adaptive with evolution process. That is to say, it can avoid early premature as well as benefit speeding up convergence of later period effectively. And more importantly, proposed PSO update operators based on different versions PSO are introduced into presented algorithm. It can take full advantage of the outstanding convergence characteristic of particle swarm optimization (PSO) and improve the global performance of the proposed algorithm. An example originated from layout of printed circuit boards (PCB) and plant equipment shows the feasibility and effectiveness of presented algorithm.     

软件开发团队的多目标优化构建研究

分析了客户需求与候选成员能力的关系,使用模糊排序聚类算法得到专业领域分工的集群;同时依据迈尔斯—布里格斯性格类型指标得到候选成员协作关系的量化评估。建立了以成员综合能力和性格匹配度最大化为目标的团队构建模型。最后,结合一个具体案例,采用带有判断与修复算子的微粒群算法对模型进行求解,得到表示团队构建候选方案集合的帕累托解,从而验证了该优化模型及算法的有效性和实用性。     

质子交换膜燃料电池系统多目标优化

质子交换膜燃料电池（PEMFC）具有操作温度低、能量密度高、稳定性好等优点,是一种具有广泛应用前景的发电装置。本文以PEMFC系统发电效率最大和总成本最小为同步优化目标,以电流密度、系统操作压力、空气及氢气化学计量数为操作变量,建立该系统的多目标优化模型。提出基于拥挤度排序的多目标列队竞争算法（MOLCA-CDS）求解该模型,得到优化目标的Pareto最优前沿。实例计算结果表明,所提出的方法可得到分布均匀的Pareto最优解集,且优化结果能够完全支配文献中采用其它方法的优化解。本文所提出的方法可为PEMFC系统的设计与操作优化提供参考。     

多目标微粒群优化算法

通过设计一种Pareto解集过滤器,并在此基础上给出多目标优化条件下的微粒群算法群体停滞判断准则,基于该准则提出了一种多目标微粒群优化算法。算法利用Pareto解集过滤器提高了候选解的多样性,并使用图形法将所提算法与经典的多目标优化进化算法在一组标准测试函数上进行了比较,结果表明算法具有更好的搜索效率。     

Estimation of digraph costs for keyboard layout optimization

The main aim of this study was to estimate the digraph costs (interkey-stroke times) based on the digraph (two consecutive keys) tapping rates for the optimization of keyboard layouts considering the touch typing principles. The study also investigated the effects of column, row, hand and period on digraph-tapping rate. For the purpose, a laboratory experiment was performed with seven subjects using a conventional keyboard. Digraph-tapping rates of a total of 241 same hand digraphs were recorded for a duration of 2-min. The interkey-stroke times were calculated as the digraph costs for the same hand digraphs using the estimated mean digraph-tapping rates. The different hand digraph costs were calculated based on the same hand digraph costs and the results of a previous study. The results indicated significant column, row, hand and period effect on the digraph-tapping rate. Using the digraph costs and the digraph frequencies of the considered language in a quadratic assignment problem, an optimal touch typing keyboard layout can be developed to satisfy all but one of Dvorak's touch typing criteria. As an application, an optimal keyboard layout, called Turkish I-layout, is developed for Turkish language. The comparison results between I and existing Turkish F and Q layouts showed that the I-layout is superior both according to the results of the optimization and Dvorak's criteria.Relevance to industryOptimal and ergonomic keyboard layouts improve typing performance and reduce the likelihood of upper extremity disorders. The digraph-tapping rates estimated through this study are essential for the development of such layouts.     

Data layout optimization for multi-valued containers in OpenCL

Scientific data is mostly multi-valued, e.g., coordinates, velocities, moments or feature components, and it comes in large quantities. The data layout of such containers has an enormous impact on the achieved performance, however, layout optimization is very time-consuming and error-prone because container access syntax in standard programming languages is not sufficiently abstract. This means that changing the data layout of a container necessitates syntax changes in all parts of the code where the container is used. Object oriented languages allow to solve this problem by hiding the data layout behind a class interface. However, the additional coding effort is enormous in comparison to a simple structure. A clever coding pattern, previously presented by the author, significantly reduces the code overhead, however, it relies heavily on advanced C++ features, a language that is not supported on most accelerators. This paper develops a concise macro based solution that requires only support for structures and unions and can therefore be utilized in OpenCL, a widely supported programming language for parallel processors. This enables the development of high performance code without an a-priori commitment to a certain layout and includes the possibility to optimize it subsequently. This feature is used to identify the best data layouts for different processing patterns of multi-valued containers on a multi-GPU system.     

Combinatorial optimization in facility layout

This research provides the means of a realistic or a close to realistic layout development using practical constraints and objectives. An experimental system, FLUKES, is constructed based on the method for testing purpose. FLUKES uses these factors not only for the evaluation of layout, but also for the search to solution. This system develops optimized layouts within the boundary of the practical factors.     

Multi-objective biogeography based optimization for optimal PMU placement

The paper proposes a multi-objective biogeography based optimization (MO-BBO) algorithm to design optimal placement of phasor measurement units (PMU) which makes the power system network completely observable. The simultaneous optimization of the two conflicting objectives such as minimization of the number of PMUs and maximization of measurement redundancy are performed. The Pareto optimal solution is obtained using the non-dominated sorting and crowding distance. The compromised solution is chosen using a fuzzy based mechanism from the Pareto optimal solution. Simulation results are compared with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Non-dominated Sorting Differential Evolution (NSDE). Developed PMU placement method is illustrated using IEEE standard systems to demonstrate the effectiveness of the proposed algorithm.     

Checkerboard patterns in layout optimization

Effective properties of arrangements of strong and weak materials in a checkerboard fashion are computed. Kinematic constraints are imposed so that the displacements are consistent with typical finite element approximations. It is shown that when four-node quatrilateral elements are involved, these constraints result in a numerically induced, artificially high stiffness. This can account for the formation of checkerboard patterns in continuous layout optimization problems of compliance minimization.