基于自适应参数与混沌搜索的PSO算法求解柔性作业车间调度问题

针对传统粒子群优化(PSO)算法在求解柔性作业车间调度问题中的不足,提出了基于自适应参数与混沌搜索的粒子群优化算法。对粒子群算法中的惯性系数等参数采用基于迭代搜索而自适应调整的方式,使粒子在初期以较大惯性进行大范围搜索,后期逐渐减小惯性而转入精细搜索。这种方法改变了传统粒子群算法在求解过程中的盲目随机与求解精度不高的问题;同时,通过在局部搜索过程中引入混沌技术,扩大对最优解的寻找范围,以此避免算法陷入局部最优,有效提高算法的全局寻优能力。实验结果表明,基于自适应参数与混沌搜索的粒子群优化算法在求解柔性作业车间调度问题(FJSP)时能够获得更优粒子适应度平均值及更好的优化目标。所提算法对求解柔性作业车间调度问题可行,有效。     

求解整合资源条件下的运输调度问题

针对传统的物流运输调度问题(Vehicle Routing Problem,VRP)中车辆之间不协作会造成资源浪费的情况,提出整合资源条件下的运输调度问题(Vehicle Routing Problem with Integration of resources,VRPIR),建立了相应的数学模型。由于混沌具有良好的遍历性,而粒子群优化算法(Particle Swarm Optimization,PSO)具有概念简单,参数少,容易实现等优点,将混沌优化方法引入到粒子群优化算法中,应用混沌粒子群优化算法(Chaos Particle Swarm Algorithm,CPSO)求解VRPIR和VRP,并用CPSO和PSO分别求解VRPIR,实验结果证明该算法优于粒子群优化算法,也证明了提出的VRPIR模型优于VRP,能节省资源,且最小化成本。     

改进的变尺度混沌粒子群算法及其应用

针对粒子群算法(PSO)存在局部最优及后期收敛速度慢等问题,提出一种改进的变尺度混沌粒子群算法(IMCPSO).该算法初期,在整个解空间对最优粒子进行变尺度混沌扰动,以防止陷入局部最优;算法后期,则以最优粒子为中心引入变尺度混沌扰动,以提高算法收敛速度.当算法一旦陷入局部最优时,采用混沌粒子替代部分种群粒子以增加粒子多样性,使算法尽快跳出局部最优.基于benchmark测试函数的仿真结果表明,所提算法与基本粒子群算法(SPSO)和变尺度混沌粒子群算法(MCPSO)相比,具有明显好的搜索精度和收敛速度.最后,将该算法应用于电路故障诊断实验中的支持向量机参数优化问题,实验结果说明了其应用价值.     

改进的混沌粒子群算法在TSP中的应用

针对基本粒子群(PSO)算法不能较好地解决旅行商优化问题(TSP),分析了基本粒子群算法的优化机理,在新定义粒子群进化方程中进化算子的基础上利用混沌运动的随机性、遍历性等特点,提出一种结合混沌优化和粒子群算法的改进混沌粒子群算法.该算法对惯性权重进行自适应调整,引入混沌载波调整搜索策略避免陷入局部最优,形成一种同时满足全局和局部寻优搜索的混合离散粒子群算法,使其适合解决TSP此类组合优化问题.利用MATLAB对其进行了仿真.仿真结果说明此算法的搜索精度、收敛速度及优化效率均较优,证明了此算法在TSP中应用的有效性,且为求解TSP提供了一种参考方法.     

粒子群优化算法在桁架结构优化中的应用

介绍了粒子群优化(PSO)算法的一种改进算法:用于约束优化问题的启发式粒子群优化(HPSO)算法.针对HPSO算法在桁架结构优化中速度较慢的问题,将HPSO算法的约束处理策略与另一种适用于粒子群算法的约束处理方法结合,并将改进后的算法应用到1个桁架结构截面优化设计算例中,同时与HPSO算法进行对比分析.对于此算例,改进算法和HPSO算法都运行了多次,从多次运行的统计分析中可以看出,改进算法的优化效果和稳定性好于HPSO算法,且结构分析的次数减少了一半左右,从而整个程序运行的速度比HPSO算法提高了将近一倍.     

多目标粒子群优化算法在柔性车间调度中的应用

将粒子群优化(Particle Swarm Optimization,PSO)算法和混沌搜索方法结合在一起,提出一种求解多目标柔性作业车间调度问题(Flexible job shop scheduling problem,FJSP)的新算法,利用混沌对PSO的参数进行自适应优化来有效平衡算法的全局搜索和局部开挖能力,并采用混沌局部优化策略来改善算法的搜索性能.此外,为了搜索到问题的所有非劣解,采用基于模糊逻辑的适应度函数来评价粒子.对于四个典型FJSP实例的实验验证了算法的可行性和有效性.     

基于混合粒子群算法的PID参数寻优

提出一种将单纯形法SM与粒子群算法PSO结合的混合粒子群算法HPSO。通过对3种常用测试函数进行优化和比较．结果表明HPSO比PSO和SM都更容易找到全局最优解。然后用HPSO优化算法对某涡扇发动机PID控制中的参数进行优化并将结果与混合遗传算法HGA的结果进行比较，结果表明HPSO在找寻最优解效率上好于HGA。且算法实现简单，具有很高的可靠性，是一种PID控制参数寻优的有效方法。     

基于HPSO的钢坯加热过程炉温优化设定

针对一种蓄热推钢式加热炉三个加热区的炉温稳态优化问题,本文提出了一种混合粒子群优化(HPSO)方法.首先,基于钢坯导热偏微分方程和边界条件,建立钢坯温度预报模型.然后,采用HPSO算法确定最佳稳态炉温,即炉温控制的参考输入.该方法利用混沌机制产生初始种群,通过免疫和克隆来提高粒子群优化(PSO)算法的全局搜索能力和搜索精度.     

解决单机准时调度问题的混合粒子群算法

针对共同交货期给定的单机准时调度问题,提出了一种混合粒子群优化（Hybrid Particle Swarm Optimization,HPSO）算法。该算法采用了工件排列和开工时间混合的粒子编码方式及新的粒子产生策略,非常适合于求解开工时间不为零的调度问题。为了提高算法性能,将HPSO分别与模拟退火算法、局部搜索算法和迭代的局部搜索算法相结合,得到了三种混合算法：HPSO1、HPSO2和HPSO3。基于典型算例的试验表明：三种算法在求解质量和求解效率两方面均优于Hino等人的研究成果。     

适应性粒子群寻优算法Ⅱ

适应性粒子群寻优算法Ⅰ(APSO-Ⅰ)是在有序的决策中始终引入随机的、不可预测的决定.为解决APSO-Ⅰ算法收敛深度不够的问题,提出适应性粒子群寻优第Ⅱ代算法(APSO-Ⅱ).APSO-ⅡⅢ算法是将有序(标准PSO粒子群寻优)和无序(自适应寻优)进行适当的分离.以发挥各自的优势.在自适应寻优阶段,通过在最优柱子邻域空间探寻更优化的解.一但新的优化解被发掘,便利用标准PSO快速寻优.典型复杂函数优化的仿真结果表明,APSO-Ⅱ在收敛速度和收敛深度上均优于DPSO(耗散型PSO),HPSO(自适应层次PSO),EPSO(自适应逃逸PSO)和APSO-I.     

求解高校教室调度问题的混合粒子群算法

针对高校教室调度问题进行了研究,综合考虑教室集中时间利用率和学生需求,采用三元组方式,用任务表示课程,用设备表示不同类型的教室。据此,教室排课问题被描述为一类以最小化Cmax与滞后时间和为调度目标,具有机器适用限制的并行机调度问题。然后结合问题特性,建立对应的运筹学调度模型,并运用混合粒子群算法求解该类调度问题。最后仿真结果表明实现了所讨论的两个优化调度目标,获得了满意解;同时通过与其他算法解的比较,得出混合粒子群算法非常适合求解这里所讨论的教室排课问题这一结论。     

一种辨识Wiener-Hammerstein模型的新方法

针对非线性Wiener-Hammerstein模型,提出利用粒子群优化算法对非线性模型进行辨识的新方法.该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题;然后采用粒子群优化算法获得该优化问题的解.为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法.最后,仿真结果验证了该方法的有效性和可行性.     

面向任务T-Petri网的舰载机出动流程仿真

为明确舰载机的出动过程,以更好地指导舰载机操作,对舰载机出动流程仿真问题进行研究。在分析舰载机出动流程共性以及Petri网优点的基础上设计了一种面向任务的综合T-Petri（Task-Petri）网模型。该模型通过对基本网进行简单扩充便可以动态仿真不同的出动任务,不仅简化了出动流程,而且可以满足舰载机出动任务的多样性建模要求,避免了重复建模的复杂性。利用T-Petri网模型对库兹涅佐夫号航母舰载机出动流程进行仿真试验,并给出了甘特图,比较直观地给出了舰载机出动流程。分析给出了舰载机出动时间计算公式,将其所得的出动时间与通过T-Petri网仿真得出的出动时间进行比较,结果表明所提出的T-Petri网模型用于仿真舰载机出动流程是可行的,且结果比较精确。     

An improved particle swarm optimization algorithm for flowshop scheduling problem

The flowshop scheduling problem has been widely studied and many techniques have been applied to it, but few algorithms based on particle swarm optimization (PSO) have been proposed to solve it. In this paper, an improved PSO algorithm (IPSO) based on the “alldifferent” constraint is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that the proposed IPSO algorithm is more effective and better than the other compared algorithms. It can be used to solve large scale flow shop scheduling problem effectively.     

A hierarchical particle swarm optimizer with latin sampling based memetic algorithm for numerical optimization

Memetic algorithms, one type of algorithms inspired by nature, have been successfully applied to solve numerous optimization problems in diverse fields. In this paper, we propose a new memetic computing model, using a hierarchical particle swarm optimizer (HPSO) and latin hypercube sampling (LHS) method. In the bottom layer of hierarchical PSO, several swarms evolve in parallel to avoid being trapped in local optima. The learning strategy for each swarm is the well-known comprehensive learning method with a newly designed mutation operator. After the evolution process accomplished in bottom layer, one particle for each swarm is selected as candidate to construct the swarm in the top layer, which evolves by the same strategy employed in the bottom layer. The local search strategy based on LHS is imposed on particles in the top layer every specified number of generations. The new memetic computing model is extensively evaluated on a suite of 16 numerical optimization functions as well as the cylindricity error evaluation problem. Experimental results show that the proposed algorithm compares favorably with conventional PSO and several variants.     

A heuristic particle swarm optimization method for truss structures with discrete variables

A heuristic particle swarm optimizer (HPSO) algorithm for truss structures with discrete variables is presented based on the standard particle swarm optimizer (PSO) and the harmony search (HS) scheme. The HPSO is tested on several truss structures with discrete variables and is compared with the PSO and the particle swarm optimizer with passive congregation (PSOPC), respectively. The results show that the HPSO is able to accelerate the convergence rate effectively and has the fastest convergence rate among these three algorithms. The research shows the proposed HPSO can be effectively used to solve optimization problems for steel structures with discrete variables.     

Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment

Random search-based scheduling algorithms, such as particle swarm optimization (PSO), are often used to solve independent multi-task scheduling problems in cloud, but the quality of optimal solution of the algorithm often has greater deviation and poor stability when the tasks are associate. In this paper, we propose an algorithm called SADCPSO to solve this challenging problem, which improves the PSO algorithm by uniquely integrating the self-adaptive inertia weight, disruption operator and chaos operator. In particular, the self-adaptive inertia weight is adopted to adjust the convergence rate, the disruption operator is applied to prevent the loss of population diversity, and the chaos operator is introduced to prevent the solution from tending to jump into the local optimal. Furthermore, we also provide a scheme to apply the SADCPSO algorithm to solve the associate multi-task scheduling problem. In the simulation experiments, we initialize two associate multi-task scheduling examples and take the minimum execution time as our optimization objective. The simulation results demonstrate that the optimal solution of our proposed algorithm has better quality and stability than the baseline PSO algorithm.     

Common model analysis and improvement of particle swarm optimizer

Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to （-1, 1）. Based on the deduced principle of acceleration factors, a new adaptive PSO algorithm- harmonious PSO （HPSO） is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.     

Common model analysis and improvement of particle swarm optimizer

Particle swarm optimizer (PSO), a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to (?1, 1). Based on the deduced principle of acceleration factors, a new adaptive PSO algorithmharmonious PSO (HPSO) is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.