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。     

平均计算时间复杂度优化的动态粒子群优化算法

粒子群优化(PSO:Particle Swarm Optimization)算法已经被广泛地应用,其中包括大量实时性要求很高的领域,如宽带数字信号处理。传统PSO算法需要对大量粒子分别进行若干次迭代运算,这将导致该算法的平均计算时间复杂度较高,运算延时大,不能满足这种高实时性要求。因此,需要在不影响性能的前提下降低PSO算法的平均计算时间复杂度。提出了一种粒子数量可变的动态粒子群优化(DPSO:Dynamic PSO)算法,其核心是丢弃粒子判定条件,在迭代过程中,根据该条件动态地抛弃一些粒子,从而降低算法的平均计算时间复杂度。此外,在算法迭代过程中对粒子的个体极值进行变异,从而避免陷入局部最优解。实验和理论分析结果表明,在算法的平均计算时间复杂度方面,对于相同的优化结果,DPSO算法的平均计算时间复杂度比传统PSO算法降低了30%左右;在算法的性能方面,对于单峰值目标函数,DPSO算法与传统PSO算法的优化性能相当,而对于多峰值目标函数,DPSO算法的优化性能要优于传统PSO算法。     

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.     

基于粒子群算法的智能电梯群控系统调度

电梯群控系统(Elevator Group Control System,EGCS)调度是一个多输入、多输出的复杂优化问题,属于NP难问题.为解决EGCS调度优化,提出了一种基于粒子群优化算法(Particle Swarm Optimization,PSO)的电梯调度策略.算法中,每个粒子代表一种派梯方案,系统首先随机初始化若干解,然后以候梯时间最短、乘梯时间最少、电梯能耗最低为主控目标,来实现电梯群控系统的调度优化.仿真对比结果表明了算法的有效性.     

最佳维变异粒子群优化算法在传感器调度中应用

粒子群优化(PSO)算法的主要特点是能快速得到问题的解,缺点是容易陷入局部最优.提出了一种利用最佳维变异技术和量子理论方法改进的PSO算法,并应用于目标跟踪传感器调度问题.目标的动力学模型为线性高斯模型,传感器观测值被高斯噪声污染并与目标状态线性相关.对于多传感器单目标跟踪的数学问题,引入提出的最佳维变异PSO算法,在...     

基于周期性演化策略的粒子群优化算法

针对标准粒子群优化算法(PSO)在寻优过程中容易出现早熟的问题,提出一种基于周期性演化策略的粒子群优化算法.该策略通过在速度更新方程中构建基于粒子群能量的粒子群最优值扰动项,使得粒子群能量在演化过程中可以周期性变化.相比标准PSO算法,当粒子群能量较大时,能够增强局部搜索能力;当粒子群能量较小时,能够增强全局搜索能力.典型优化问题的仿真结果表明,所提出的算法与线性下降惯性权重粒子群优化(LWPSO)和PSO算法相比,优化性能得到了显著提高.     

免疫PSO算法求解多库房带时间窗VRP

为了降低物流系统的总费用,对车辆优化调度模型进行了改进,考虑了库房容量和时间窗两方面的因素,以总的耗费成本为目标,采用免疫粒子群优化（PSO）算法对此多库房车辆调度模型进行了优化求解。仿真结果表明免疫PSO比PSO更有效、更优越。     

云环境下基于DPSO的任务调度算法

针对云计算任务调度问题,结合粒子群优化(PSO)算法的种群个体协作和信息共享特点,提出一种基于离散粒子群优化(DPSO)的任务调度算法。采用随机方法生成初始种群,利用时变方式调整惯性权重,并在位置更新中使用绝对值取整求余映射法进行合法化处理,提高PSO算法的离散化程度。搭建并重新编译了CloudSim云计算仿真平台进行实验,结果显示,当迭代次数为200时,DPSO、PSO、GA算法的所有任务最终调度时间分别为457.69 s、467.90 s、472.41 s,从而证明DPSO算法能够有效解决云计算环境下的任务调度问题,并且算法收敛速度优于PSO和GA算法。     

一种基于和声搜索的动态交叉粒子群算法

针对粒子群优化算法(PSO)在优化多维问题时容易陷入局部最优的问题,提高其全局搜索能力和拓展能力,提出了一种基于和声搜索的动态交叉粒子群算法.引入动态交叉操作,使得粒子在更新速度时实现共享有效信息,保证粒子进化过程中的种群多样性,提高全局搜索能力.结合和声搜索(HS)的随机搜索能力提出了HS-DCPSO,利用和声搜索的自适应调整参数音符调节概率PAR和间隔调整带宽bw来提高粒子群的拓展能力.通过多个基准函数对所提出的HS-DCPSO算法进行仿真测试,并与HS、PSO及多种改进的粒子群算法对比,验证所提出的HS-DCPSO算法具有较强的全局搜索能力和局部拓展能力,并且算法时间复杂度相比传统PSO增加不明显.     

基于PSO优化的LSSVM的电力系统电压优化的研究

由于传统的方法在处理电压优化与治理问题时存在较大的局限性。针对地区电力系统电压优化和治理问题进行了研究,建立了以有功网损为目标函数的不等式约束的优化问题,并考虑到电压优化问题的控制变量能够进行种群划分,而粒子群算法(PSO)又能够降低搜索空间的运算复杂度,因此提出了基于粒子群优化最小二乘支持向量机(PSO-LSSVM)来对电力系统电压进行优化。通过实例的结果分析,该方法对电压的合格率有所提高,对电压性能有明显的改善,损耗也下降了,且收敛速度加快,有助于解决地区电力系统电压优化和治理问题。     

Novel scheduling strategy for downlink multiuser MIMO system: Particle swarm optimization

In this paper the scheduling problem in downlink multiuser MIMO system is described as an optimization problem and particle swarm optimization (PSO) algorithm is introduced to address such problem. Two PSO scheduling methods with different objective functions applicable to different requirements on capacity and complexity are investigated. One is the capacity based PSO(C-PSO) scheduling method aiming at achieving the near optimal capacity; and the other is the lower bound of eigenvalue based PSO (LBE-PSO) scheduling method with the purpose of reducing computational complexity and at the same time achieving as large as possible capacity gain. Furthermore, convergence analysis of PSO from both the particle and the velocity aspects is also presented to derive the convergent condition, which is validated by several examples of different parameter values. Simulation results reveal that the C-PSO can obtain nearly the same capacity as the exhaustive search method with lower complexity, while the LBE-PSO provides a viable approach by striking a better tradeoff between capacity and computational complexity.     

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 hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences

The timetabling problem at universities is an NP-hard problem concerned with instructor assignments and class scheduling under multiple constraints and limited resources. A novel meta-heuristic algorithm that is based on the principles of particle swarm optimization (PSO) is proposed for course scheduling problem. The algorithm includes some features: designing an ‘absolute position value’ representation for the particle; allowing instructors that they are willing to lecture based on flexible preferences, such as their preferred days and time periods, the maximum number of teaching-free time periods and the lecturing format (consecutive time periods or separated into different time periods); and employing a repair process for all infeasible timetables. Furthermore, in the original PSO algorithm, particles search solutions in a continuous solution space. Since the solution space of the course scheduling problem is discrete, a local search mechanism is incorporated into the proposed PSO in order to explore a better solution improvement. The algorithms were tested using the timetabling data from a typical university in Taiwan. The experimental results demonstrate that the proposed hybrid algorithm yields an efficient solution with an optimal satisfaction of course scheduling for instructors and class scheduling arrangements. This hybrid algorithm also outperforms the genetic algorithm proposed in the literature.     

Solving a periodic single-track train timetabling problem by an efficient hybrid algorithm

Train timetabling with minimum delays is the most important operating problem in any railway industry. This problem is considered to be one of the most interesting research topics in railway optimization problems. This paper deals with scheduling different types of trains in a single railway track. The primary focus of this paper is on the periodic aspects of produced timetables and the proposed modeling is based on the periodic event scheduling problem (PESP). To solve large-scale problems, a hybrid meta-heuristic algorithm based on simulated annealing (SA) and particle swarm optimization (PSO) is proposed and validated using some numerical examples and an Iranian case study that covers the railway line between two cities of Isfahan and Tehran.     

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.     

基于粒子群算法的流程工业生产调度研究

以优化流程工业生产为目标,研究了将基于惯性权重的粒子群算法应用到流程工业的生产调度问题。在对流程工业生产调度问题进行分析的基础上,建立了以总加工完成时间最短为优化目标的生产调度模型。调度算法采用动态惯性权重,使惯性权值在粒子群算法搜索过程中线性变化,以提高粒子群算法的优化性能。给出了粒子编码与解码实现方法,以及具体的算法实现过程。以某流程工业企业生产调度实例为例,利用建立的优化调度模型和设计的粒子群算法进行了实验仿真,结果表明,建立的调度模型和设计的算法是可行的,与蚁群系统方法相比较,有较好的调度性能,适用于解决流程工业实际生产调度问题。     

具有模糊时间窗的有容积约束车辆调度优化问题研究*

针对现实配送过程中存在的时间参数模糊化与车辆容积限制问题,利用梯形模糊代数、有符号距离和区间数距离公式,构造出一种较高精度的提前/滞后惩罚函数,在此基础上给出了一种以最小化服务点提前/滞后惩罚、最小化配送总里程以及最小化配送车辆数量为目标的、具有模糊时间窗的有容积约束配送车辆调度问题模型。在问题求解方面,结合粒子群算法和遗传算法,引入遗传算法思想对粒子进行交叉、变异操作,给出了一种基于遗传操作的混合粒子群算法,以解决基本粒子群算法容易陷入局部最优的问题。仿真实验表明,该算法具有可行性和有效性。     

带约束的清洁排班问题模型及其求解

保洁服务公司的清洁任务往往具有不同级别、不同时长和不同周期等特点,缺乏通用清洁排班问题模型,现阶段主要依赖人工排班方案,存在耗时费力且排班质量不稳定等问题。因此提出了属于NP难问题的带约束的清洁排班问题的数学模型,并使用模拟退火算法（SA）、蜂群算法（BCO）、蚁群算法（ACO）和粒子群优化算法（PSO）对该模型进行求解,最后以某清洁服务公司实际排班情况进行了实证分析。实验结果表明,与人工排班方案进行对比,启发式智能优化算法求解带约束的清洁排班问题具有明显优势,获得的清洁排班表的人力需求明显减少。具体来说,在一年排班周期内这些算法比人工排班方案可节省清洁人力218.62~513.30 h。可见基于启发式智能优化算法的数学模型对带约束的清洁排班问题的求解可行且有效,能为保洁服务公司提供科学管理的决策支持。     

An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.