A Pareto archive particle swarm optimization for multi-objective job shop scheduling

In this paper, we present a particle swarm optimization for multi-objective job shop scheduling problem. The objective is to simultaneously minimize makespan and total tardiness of jobs. By constructing the corresponding relation between real vector and the chromosome obtained by using priority rule-based representation method, job shop scheduling is converted into a continuous optimization problem. We then design a Pareto archive particle swarm optimization, in which the global best position selection is combined with the crowding measure-based archive maintenance. The proposed algorithm is evaluated on a set of benchmark problems and the computational results show that the proposed particle swarm optimization is capable of producing a number of high-quality Pareto optimal scheduling plans.     

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.     

基于种群分类粒子群算法的物流车辆调度优化

为了获得更加理想的配送车辆调度方案,提出一种基于种群分类粒子群算法的配送车辆调度优化方法。首先建立多约束配送车辆调度的数学模型,并以配送路径最短作为目标函数,然后采用粒子群算法对模型进行求解,并对每次迭代产生的粒子群进行分类,根据分类结果对粒子群进行不同的操作,加快了算法的搜索速度,以避免陷入局部最优,最后进行仿真对比实验。结果表明,种群分类粒子群算法获得比较理想的配送车辆调度方案,具有一定的实用价值。     

A discrete differential evolution algorithm for the single machine total weighted tardiness problem with sequence dependent setup times

This paper is concerned with solving the single machine total weighted tardiness problem with sequence dependent setup times by a discrete differential evolution algorithm developed by the authors recently. Its performance is enhanced by employing different population initialization schemes based on some constructive heuristics such as the well-known NEH and the greedy randomized adaptive search procedure (GRASP) as well as some priority rules such as the earliest weighted due date (EWDD) and the apparent tardiness cost with setups (ATCS). Additional performance enhancement is further achieved by the inclusion of a referenced local search (RLS) in the algorithm together with the use of destruction and construction (DC) procedure when obtaining the mutant population. Furthermore, to facilitate the greedy job insertion into a partial solution which will be employed in the NEH, GRASP, DC heuristics as well as in the RLS local search, some newly designed speed-up methods are presented for the insertion move for the first time in the literature. They are novel contributions of this paper to the single machine tardiness related scheduling problems with sequence dependent setup times. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, its highly effective performance with substantial margins both in solution quality and CPU time is shown against the best performing algorithms from the literature, in particular, against the very recent newly designed particle swarm and ant colony optimization algorithms of Anghinolfi and Paolucci [A new discrete particle swarm optimization approach for the single machine total weighted tardiness scheduling problem with sequence dependent setup times. European Journal of Operational Research 2007; doi:10.1016/j.ejor.2007.10.044] and Anghinolfi and Paolucci [A new ant colony optimization approach for the single machine total weighted tardiness scheduling problem. http://www.discovery.dist.unige.it/papers/Anghinolfi_Paolucci_ACO.pdf, respectively. Ultimately, 51 out of 120 overall aggregated best known solutions so far in the literature are further improved while other 50 instances are solved equally.     

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。     

离散猫群优化算法求解带交货期的FJSP问题

针对带交货期的柔性作业车间调度问题(flexible job shop scheduling problem,FJSP),提出一种离散猫群优化算法(discrete cat swarm optimization,DCSO),以优化工件最大完工时间和平均提前/拖期时间.首先,设计一种两段式离散编码方式,用于表示调度解,并采用启发式算法实现种群初始化;其次,为了使算法能够直接在离散调度空间内运行,在搜寻模式下设计基于3种不同邻域结构的搜寻方法,并在跟踪模式下提出一种新型离散个体更新公式;再次,采用线性自适应猫群行为模式选择策略,协调算法全局搜索和局部搜索的能力;最后,为了进一步改善计算结果,在算法中嵌入一种局部搜索策略.通过基准算例测试DCSO算法的性能,仿真结果表明所提DCSO算法在求解FJSP问题方面的有效性.     

基于粒子群优化的异构多处理器任务调度算法

为提高异构多处理器任务调度的执行效率,充分发挥多处理器并行性能,提出一种基于粒子群优化的异构多处理器任务调度算法——FPSOTTS算法。该算法以求得任务最短完成时间为目标,首先通过建立新的编码方式和粒子更新公式实现粒子搜索空间到离散空间的映射,使连续的粒子群优化算法适用于离散的异构多处理器任务调度问题;同时通过引入禁忌算法进行局部搜索,克服粒子群算法的早熟收敛现象,避免陷入局部最优。实验结果表明,FPSOTTS算法的执行效率优于Min-min算法和遗传算法,有效地降低任务的执行时间。FP-SOTTS算法很好地解决了异构多处理器任务调度问题,并且适合于大规模并行任务调度。     

基于混合粒子群算法的云计算任务调度研究

任务调度是云计算系统可靠运行的关键,云计算环境中要处理的任务量巨大,考虑到云计算任务调度和QoS的优化问题,提出一种混合粒子群优化算法用于云任务调度。算法中引入遗传算法的交叉和变异思想,并结合随迭代次数变化的变异指数,保证种群进化初期具有较高的全局搜索能力,避免出现"早熟",同时将爬山算法引入粒子群算法,改善局部搜索能力。实验结果显示该算法具有很好的寻优能力,是一种有效的云计算任务调度算法。     

基于DEPSO 的模糊时间ZW多产品厂间歇调度

研究以最小化完工时间为目标的模糊加工时间零等待多产品厂间歇调度问题, 提出一种基于差分进化粒子群优化(DEPSO) 的间歇调度算法. 以基本粒子群算法为整体进化框架, 采用基于反向学习的方法初始化种群, 引入群体极值保持代数作为阈值, 利用基于排序的差分进化算法优化粒子个体极值位置, 改变粒子的搜索范围, 防止粒子陷入局部极值. 仿真实验验证了所提算法在解决模糊加工时间零等待多产品厂间歇调度问题上的有效性和优越性.

     

混沌粒子群算法及其在发电资源调度中的应用

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于混沌粒子群算法的调度方案。设计了水火电力系统资源调度问题的数学模型,给出了混沌粒子群调度算法的框架,通过引入最优粒子的混沌搜索机制、优势粒子和劣势粒子的权重自适应调节机制,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,本算法方案能有效解决水火发电资源调度问题,具有较好的应用价值。     

改进的离散型萤火虫优化算法求解柔性作业车间调度问题

针对传统的群智能优化算法在求解柔性作业车间调度问题(FJSP)时,存在寻优能力不足且易陷入局部最优等缺点,本文以最小化最大完工时间为目标,将萤火虫算法(FA)用于求解柔性作业车间调度问题,提出一种改进的离散型萤火虫算法(DFA)。首先,通过两段式编码建立FA连续优化问题与FJSP离散优化问题之间的联系;其次,设计一种群初始化方法,以确保初始解的质量以及多样性;然后,提出改进离散型萤火虫优化算法并引入局部搜索算法,加强算法的全局搜索能力和局部搜索能力;最后,对标准算例进行仿真,验证DFA算法求解FJSP的有效性。通过与遗传算法和粒子群优化算法进行仿真对比,表明了DFA求解FJSP的优越性。     

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.     

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...     

Multi-dimensional visual tracking using scatter search particle filter

Multi-dimensional visual tracking (MVT) problems include visual tracking tasks where the system state is defined by a high number of variables corresponding to multiple model components and/or multiple targets. A MVT problem can be modeled as a dynamic optimization problem. In this context, we propose an algorithm which hybridizes particle filters (PF) and the scatter search (SS) metaheuristic, called scatter search particle filter (SSPF), where the optimization strategies from SS are embedded into the PF framework. Scatter search is a population-based metaheuristic successfully applied to several complex combinatorial optimization problems. The most representative optimization strategies from SS are both solution combination and solution improvement. Combination stage enables the solutions to share information about the problem to produce better solutions. Improvement stage makes also possible to obtain better solutions by exploring the neighborhood of a given solution. In this paper, we have described and evaluated the performance of the scatter search particle filter (SSPF) in MVT problems. Specifically, we have compared the performance of several state-of-the-art PF-based algorithms with SSPF algorithm in different instances of 2D articulated object tracking problem and 2D multiple object tracking. Some of these instances are from the CVBase’06 standard database. Experimental results show an important performance gain and better tracking accuracy in favour of our approach.     

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。     

改进混合粒子群算法的立体车库存取调度

为了进一步提高立体车库存取效率，提出一种改进混合粒子群算法，应用于立体车库存取策略时间模型，寻找存取车最优时间和最优排序。该算法主要在粒子群算法前期引入遗传算法，改善全局搜索能力，后期引入模拟退火算法弥补其局部搜索能力弱的特点。与目前应用于立体车库存取车调度的遗传算法相比，改进混合粒子群算法存取效率提高了24.5%~36.07%，并优于其他车库调度算法，提高了车库运营效率。     

A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem

In this paper, a local-best harmony search (HS) algorithm with dynamic sub-harmony memories (HM), namely DLHS algorithm, is proposed to minimize the total weighted earliness and tardiness penalties for a lot-streaming flow shop scheduling problem with equal-size sub-lots. First of all, to make the HS algorithm suitable for solving the problem considered, a rank-of-value (ROV) rule is applied to convert the continuous harmony vectors to discrete job sequences, and a net benefit of movement (NBM) heuristic is utilized to yield the optimal sub-lot allocations for the obtained job sequences. Secondly, an efficient initialization scheme based on the NEH variants is presented to construct an initial HM with certain quality and diversity. Thirdly, during the evolution process, the HM is dynamically divided into many small-sized sub-HMs which evolve independently so as to balance the fast convergence and large diversity. Fourthly, a new improvisation scheme is developed to well inherit good structures from the local-best harmony vector in the sub-HM. Meanwhile, a chaotic sequence to produce decision variables for harmony vectors and a mutation scheme are utilized to enhance the diversity of the HM. In addition, a simple but effective local search approach is presented and embedded in the DLHS algorithm to enhance the local searching ability. Computational experiments and comparisons show that the proposed DLHS algorithm generates better or competitive results than the existing hybrid genetic algorithm (HGA) and hybrid discrete particle swarm optimization (HDPSO) for the lot-streaming flow shop scheduling problem with total weighted earliness and tardiness criterion.     

A discrete version of particle swarm optimization for flowshop scheduling problems

Particle swarm optimization (PSO) is a novel metaheuristic inspired by the flocking behavior of birds. The applications of PSO to scheduling problems are extremely few. In this paper, we present a PSO algorithm, extended from discrete PSO, for flowshop scheduling. In the proposed algorithm, the particle and the velocity are redefined, and an efficient approach is developed to move a particle to the new sequence. To verify the proposed PSO algorithm, comparisons with a continuous PSO algorithm and two genetic algorithms are made. Computational results show that the proposed PSO algorithm is very competitive. Furthermore, we incorporate a local search scheme into the proposed algorithm, called PSO-LS. Computational results show that the local search can be really guided by PSO in our approach. Also, PSO-LS performs well in flowshop scheduling with total flow time criterion, but it requires more computation times.     

批量无等待调度问题的微粒群蛙跳混合优化算法*

将离散微粒群与蛙跳算法相结合解决以最大完工时间为指标的批量无等待流水线调度问题.结合微粒群算法较强的全局收敛能力和蛙跳算法较强的深度搜索能力,设计了三种混合算法,平衡了算法的全局开发能力和局部探索能力.对随机生成不同规模的实例进行了广泛的实验,仿真实验结果的比较表明了所得混合算法的有效性和高效性.     

柔性制造系统的改进粒子群无死锁调度算法

基于柔性制造系统的Petri网模型,以制造期最小为优化目标,将死锁避免策略嵌入粒子群算法中,提出一种无死锁改进粒子群调度算法.该算法将粒子与工件的工序序列相对应,以位置数值的大小表示对应工件工序在执行顺序中的优先级.采用一步向前看的死锁避免策略方法对序列的可行性进行验证,提出一种跳出局部极值的策略.实例仿真结果表明了粒子群调度算法的可行性和有效性,以及改进粒子群调度算法的优越性.