基于群智能算法的设备布局离散优化研究

针对单向环形设备布局设计问题,建立了新的数学模型.利用多维实数编码及映射方法,将连续粒子群优化算法应用于求解设备单向环形布局问题,提供了求解离散优化问题的新思路.利用遗传算法中的杂交策略扩展了粒子群优化算法,提高了粒子群优化算法性能.建立了问题的图结构描述,以引入蚁群系统算法搜索优化解.给出了两种方法的求解步骤.通过实例计算和结果比较,说明该算法能有效地解决此类离散优化问题,降低成本,提高效率,所得解质量较高,有很好的实用价值.     

求解第Ⅰ类装配线平衡问题的离散粒子群优化算法

为求解具有NP难性质的第Ⅰ类装配线平衡问题,提出一类离散粒子群优化算法。该算法中所发展的排列数编码方法使得粒子解码后总满足装配作业间先后关系约束。针对排列数编码特点,提出一种基于位置交叉算子的粒子位置更新机制,确保了更新后粒子仍为排列数。为增强该算法的全局寻优能力,将简化变邻域搜索算法嵌入该算法中,对群体最佳粒子的邻域进行局部搜索,从而构建一种混合粒子群优化算法。通过将该算法和混合粒子群优化算法用于一系列测试算例并与遗传算法结果比较,验证了算法的有效性。计算结果对比表明,离散粒子群算法引入简化变邻域搜索可明显增强全局寻优能力,就综合解的质量和计算效率而言,混合粒子群优化算法优于现有遗传算法。     

求解Job shop调度的协同优化算法

针对量子粒子群算法、遗传算法在求解车间调度存在的局部收敛的问题,提出用量子粒子群算法与遗传算法相结合的协同优化方法求解该问题。该算法采用量子粒子群算法与遗传算法的并行搜索结构,通过迁移算子把各个种群联系起来。仿真结果表明,该算法收敛速度快,且具有较高的求解质量。     

置换流水车间调度粒子群优化与局部搜索方法研究

采用粒子群优化算法求解置换流水车间调度问题,提出了一种基于工件次序和粒子位置的二维粒子编码方法.为提高粒子群算法的优化性能,在描述了面向置换流水车间调度问题的粒子邻域结构后,提出了三种基于粒子邻域操作的局部搜索方法,分别是基于互换操作、基于插入操作和基于逆序操作的局部搜索方法.计算结果说明,粒子群算法的优化性能好于遗传算法和NEH启发式算法.三种局部搜索算法均能有效地提高粒子群算法的优化性能,采用基于互换操作局部搜索的粒子群算法的优化性能要好于其它两种局部搜索算法.     

求解作业车间调度问题的广义粒子群优化算法

为克服传统粒子群优化算法在解决组合优化问题上的局限性，分析了其优化机理，并在此基础上提出了广义粒子群优化模型。按照此模型提出了一种求解作业车间调度问题的广义粒子群优化算法。在本算法中，利用遗传算法中的交叉操作作为粒子间的信息交换策略，利用遗传算法中的变异操作作为粒子的随机搜索策略，而粒子的局部搜索策略则采用禁忌搜索来实现。为了控制粒子的局部搜索以及向全局最优解的收敛，迭代过程中交叉概率以及禁忌搜索的最大步长都是动态变化的。实验结果表明，本算法可有效地求解作业车间调度问题，验证了广义粒子群优化模型的合理性。     

基于混合群集智能算法的并行公差优化设计

通过对并行公差优化设计的分析,将其视为一种混合变量组合优化问题.首先给出了并行公差优化设计的数学模型,然后将其映射为一类特殊的旅行商问题--顺序多路旅行商问题,从而降低了问题的求解难度.利用蚁群优化算法和粒子群优化算法,分别在求解离散和连续变量优化时的优势,提出了一种求解并行公差优化设计问题的混合群集智能算法.通过一个计算实例,将混合群集智能算法分别与遗传算法和模拟退火算法进行了比较,结果表明,前者具有更强的搜索能力和较高的效率.同时,混合群集智能算法也为求解一般意义的混合变量优化问题提供了借鉴和参考.     

改进粒子群优化算法在工程优化问题中的应用研究

粒子群优化（PSO）算法是一种群集智能方法，它通过粒子之间的合作与竞争以实现对多维复杂空间的高效搜索。在对于粒子群群体构造和粒子多样性对收敛速度和精度影响的研究基础上提出了一种改进型粒子群优化算法。针对工程中的有约束的优化问题，将改进粒子群算法与函数法相结合进行求解。计算实例表明改进型粒子群优化算法大大改善了传统PSO算法的全局收敛性能，解的精度提高了很多。     

基于改进粒子群优化算法的复合材料可靠性优化设计

提出了一种可用于求解非线性约束优化问题的改进粒子群算法,并将其用于求解复合材料可靠性优化设计。在满足层合结构系统可靠度的情况下,以总厚度最小为目标函数,对复合材料的纤维方向角和厚度进行优化设计。结果表明,改进粒子群算法不但具备基本算法的简单易实现、需调整参数少的特性,而且能够在确保全局收敛性的基础上,快速搜索到高质量的优化解,对复合材料层合结构的可靠性优化设计十分有效。     

离散粒子群优化算法求解矩形件排样问题

提出了一种基于离散粒子群优化算法求解矩形件排样问题的方法.文中介绍了基本粒子群优化的搜索策略与基本算法,用置换子和置换序列构造一种离散粒子群优化矩形件排样算法,通过实例和遗传算法相比较,实验结果表明该算法是有效的.     

一种混合智能排样优化算法的应用研究

基于智能优化方法的混合机理,将并行进化机制引入遗传算法和粒子群算法,提出一种混合智能优化排样方法(HGPA)。该算法依据个体适应度值的大小和相似性对整个种群进行合理划分,在每次迭代中,个体适应度值较好的子种群利用遗传算法进化,个体适应度值较差的子种群则利用粒子群算法处理,实现优化方法的优势互补和信息增值。同时通过设置多样性度量标准来控制种群特征信息和搜索空间。在求解不规则件排样问题的算例表明:该算法可平衡控制个体种群进化中的局部寻优和全局搜索,为智能优化的混合机理研究提供了一个新的思路。     

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design(CMSOD) belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search(ACAM) is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm(GA) and particle swarm optimization(PSO) are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.     

Bicriteria parallel flow line scheduling using hybrid population-based heuristics

The objective of this paper is to determine a schedule for parallel flow line with bicriteria objective of minimizing the total tardiness and earliness of jobs. An enhancement to its basic greedy randomized adaptive search procedure (GRASP) is used in conjunction with genetic algorithm (GA) and particle swarm optimization (PSO). The feasible solution of GRASP construction phase is used as initial population for both GA and PSO. A number of problems are solved, by varying the number of jobs, lines, and machines, using the hybrid PSO, hybrid GA, PSO, and GA-based methods and the results are compared.     

An integrated method of particle swarm optimization and differential evolution

Particle swarm optimization (PSO) and differential evolution (DE) have their similarities and compatibility in the design update process, such that a new design vector is determined by using neighborhood designs under algorithm control parameters. The paper deals with an integrated method of a hybrid PSO (HPSO) algorithm combined with DE in order to refine the optimization performance. PSO and DE also possess common characteristics compared with genetic algorithm (GA). The crossover- and mutation-like operators are suggested in the HPSO. A bounce back method is also implemented to prevent the design from locating out of design spaces during the optimization process. For the purpose of further enhancing the search capabilities, such HPSO is combined with the Q-learning that is one of efficient reinforcement learning methods. Using a number of nonlinear multimodal functions and engineering optimization problems, the proposed algorithms of HPSO and HPSO with Q-learning are compared with PSO DE and GA. This paper was recommended for publication in revised form by Associate Editor Tae Hee Lee Jongsoo Lee received a B.S. degree in Mechanical Engineering from Yonsei University in 1988. He then went on to receive his M.S. degree from University of Minnesota in 1992 and Ph.D. degree from Rensselaer Polytechnic Institute in 1996. Dr. Lee is currently a Professor at the School of Mechanical Engineering at Yonsei University in Seoul, Korea. He is currently serving as a committee member of the division of CAE and Applied Mechanics in the Korean Society of Mechanical Engineers. Dr. Lee’s research interests are in the area of engineering design optimization, fluidstructure interactions, and reliability based robust product design.     

基于粒子群算法的并联机构结构参数优化设计

介绍了粒子群优化算法的原理和实现方法，分析了该算法的主要参数对搜索性能的影响，井把粒子群算法用于六自由度的并联机构的参数优化设计中，取得了较好的效果，试验证明，粒子群算法是一种有效的优化方法，适用于大型复杂结构的优化设计。     

A hybrid particle swarm optimization for parallel machine total tardiness scheduling

The parallel machine scheduling problem has received increasing attention in recent years. This research considers the problem of scheduling jobs on parallel machines with a total tardiness objective. In the view of its non-deterministic polynomial-time hard nature, the particle swarm optimization (PSO), which is inspired by the swarming or collaborative behavior of biological populations, is employed to solve the parallel machine total tardiness problem (PMTP). Since it is very hard to directly apply standard PSO to this problem, a new solution representation is designed based on real number encoding, which can conveniently convert the job sequences of PMTP to continuous position values. Moreover, in order to enhance the performance of PSO, we introduce clonal selection algorithm (CSA) into PSO and therefore propose a new CSPSO method. The incorporation of CSA can greatly improve the swarm diversity and avoid premature convergence. We further investigate three parameters of PSO and CSPSO, finding that the parameters have marginal impact on CSPSO, which indicates that CSPSO is a very stable and robust method. The performance of CSPSO is evaluated in comparison with traditional genetic algorithm (GA) and standard PSO on 250 benchmark instances. Experimental results show that CSPSO significantly outperforms GA and PSO, with obtaining the optimal solutions of 237 instances. Additionally, PSO appears more effective than GA.     

A swarm optimization approach for flexible flow shop scheduling with multiprocessor tasks

In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow shop problem (FFSP). Flexible flow shops are thus generalization of simple flow shops. Flexible flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. FFSP can be stated as finding a schedule for a general task graph to execute on a multiprocessor system so that the schedule length can be minimized. FFSP is known to be NP-hard. In this study, we present a particle swarm optimization (PSO) algorithm to solve FFSP. PSO is an effective algorithm which gives quality solutions in a reasonable computational time and consists of less numbers parameters as compared to the other evolutionary metaheuristics. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast towards near-optimal solution and hence reduce computational efforts further. The performance of schedules is evaluated in terms of total completion time or makespan (C_max). The results are presented in terms of percentage deviation (PD) of the solution from the lower bound. The results are compared with different versions of genetic algorithm (GA) used for the purpose from open literature. The results indicate that the proposed PSO algorithm is quite effective in reducing makespan because average PD is observed as 2.961, whereas GA results in average percentage deviation of 3.559. Finally, influence of various PSO parameters on solution quality has been investigated.     

设备布局离散优化问题的粒子群算法研究

设备布局设计是制造系统设计的重要组成部分，设备布局是否合理对整个制造系统的总体功效起着非常重要的影响。粒子群优化算法（PSO）是一种新的群智能优化算法，常用于求解连续空间极值问题，近来正逐渐进入组合优化领域。利用多雏实数编码及映射方法将连续PSO算法应用于求解设备环形布局问题，为此类离散优化问题的求解提供了一种新的思路。利用GA中的杂交策略扩展PSO算法，提高了PSO算法性能。通过实例计算和结果比较，说明了该算法能有效地求得设备环形布局问题的优化解，是一种行之有效地算法，有很好的实用价值。     

Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Nowadays tolerance optimization is increasingly becoming an important tool for manufacturing and mechanical design. This seemingly, arbitrary task of assigning dimension tolerance can have a large effect on the cost and performance of manufactured products. With the increase in competition in today’s market place, small savings in cost or small increase in performance may determine the success of a product. In practical applications, tolerances are most often assigned as informal compromises between functional quality and manufacturing cost. Frequently the compromise is obtained interactively by trial and error. A more scientific approach is often desirable for better performance. In this paper particle swarm optimization (PSO) is used for the optimal machining tolerance allocation of over running clutch assembly to obtain the global optimal solution. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. The result obtained by PSO is compared with the geometric programming (GP) and genetic algorithm (GA) and the performance of the result are analyzed .     

基于混合群智能优化的机器人立体视觉标定

准确的立体视觉模型是机器人高精密视觉定位的基础,而传统的单一非线性优化算法难以实现稳定和高精度的机器人立体视觉标定。结合遗传算法全局搜索能力强和粒子群算法局部搜索能力强的特点,提出了一种基于混合群智能优化的机器人立体视觉三步标定方法。针对非线性视觉模型,标定第一步和第二步分别对两个摄像机模型单独作线性初值求解和初次非线性优化,第三步对双目立体视觉模型作联合非线性优化,直接线性变换、遗传算法、粒子群算法分别作用于标定的三个步骤,每一步计算的结果被用作下一步的初始化。仿真试验分析与实际试验结果表明,相对于传统的优化标定方法和使用单一群智能优化算法的标定方法,该方法在噪声环境下具有更高的准确性和鲁棒性,能够更好满足机器人精密视觉操作的需求。