基于改进蚁群算法的白车身焊接机器人路径优化

针对国内整车制造业中白车身焊接机器人工作路径设计不合理的现象,提出了一种遗传—蚁群混合算法,在白车身侧围工位对多机器人协同工作路径优化展开研究,以实现多机器人焊点分配和单机器人焊接路径最优的焊接要求。通过过程仿真软件Process Simulation建立机器人离线工作站,对优化结果进行仿真实验。结果表明,该算法具有较快的迭代速度,同时能够实现焊点的合理分配,有效提升焊接效率。     

一种基于自适应蚁群算法的产品配置方案

在大规模定制生产模式下,企业面对复杂产品配置问题。在基于蚁群算法实现产品配置求解方案的过程中,针对参数选取对于蚁群算法性能的影响进行了研究,给出了基于自适应蚁群算法的产品配置求解方案,仿真实验验证了该算法策略不仅能保持蚁群算法较为精确的求解性能,且在算法的全局搜索能力和收敛速度上都取得了较好的平衡。     

水文频率计算方法的探讨

在水文频率参数分析中,基于蚁群算法的水文频率优化适线法模型.利用3个水文站年最大洪峰流量数据作为样本数据,对模型进行学习训练.试验结果表明,基于蚁群算法的优化适线法在使得离(残)差平方和(OLS)准则最小这个目标函数下,获得了P-III曲线3个参数的最优解,比常规算法结果精确度更高,同时能够克服常规算法收敛性较差问题,是一种可行的水文频率分析方法.     

基于混沌蚁群算法的SVM模型在水库枯水期径流预测中的应用

本文采用混沌蚁群算法对SVM模型的参数进行优化计算,并采用高斯核函数对模型进行混沌特性识别,简化模型在枯水期径流预测非线性求解过程。结合优化模型对水库枯水期径流进行预测。研究结果表明:基于混沌蚁群算法的SVM模型可较好的辨识复杂的水文序列,模型具有较高的泛化能力,相比于传统算法,水库枯水期径流预测精度得到明显改善,具有较高的适用性。研究成果对于水库枯水期径流预测提供方法参考。     

电网工程项目工期及成本优化问题研究

正为了解决蚁群算法容易陷入局部最优、求解容易出现停滞现象,从转移概率和信息素挥发因子两方面对蚁群算法进行改进,并将其应用于工程项目工期成本优化问题中。众所周知,电网工程项目的工期和成本是相互关联、相互制约的,将二者分开讨论则会失去实际意义。对一个项目而言,如果要使该项目的施     

基于改进蚁群算法的数控机床故障诊断方法

为提高数控机床的保障与维护管理水平,推动我国制造产业的高质量循环发展,设计一种基于改进蚁群算法的数控机床故障诊断方法。采集数量机床设备的运行数据,利用粗糙集理论提取故障特征信号,应用改进蚁群算法构建数控机床的故障诊断模型,通过蚁群信息素的初始化与动态更新,寻找最优路径,实现对数控机床设备故障的智能化辨识与诊断。仿真测试结果显示,对于随机10组不同故障的数据信息,该方法的故障诊断时间均值约0.118 7 s、具有高效性,实际诊断结果与期望输出结果的平均误差约0.000 6、误差极小,具有精确性与适用性,为数控机床的维护管理工作提供了一种可靠的技术支持。     

自动清粪机器人控制系统与设计

从自动清粪机器人的架构出发,进一步探究了自动清粪机器人控制系统的硬件设计、软件设计等,并对自动清粪机器人的实际运动轨迹进行模拟,为机器人运行轨迹规划算法的优化改良提供了一定支撑。     

基于改进的蚁群算法的移动机器人路径规划

针对移动机器人路径规划中的传统蚁群算法收敛精度低、易陷入局部最优等问题,提出一种改进蚁群算法。首先,对算法的转移概率进行改进,加入转向代价,减少不必要的转折,并针对启发函数启发性能不够强,对路径启发信息进行改进;然后,提出一种自适应的参数调整伪随机状态转移策略,动态改变参数值,避免过早陷入搜索停滞,增强搜索的全面性,同时对信息素更新方式进行改进,调整信息素挥发系数,保持蚂蚁发现最优路径的能力;最后,通过Matlab与其他算法进行对比分析。仿真结果表明,改进的蚁群算法收敛速度快,且路径长度和算法迭代次数有明显减少,能得到全局最优路径。改进蚁群算法具有可行性、有效性,在移动机器人路径规划中有一定的应用价值。     

多周期第四方物流集成反馈网络设计的模型与算法

本文研究了多周期第四方物流集成反馈网络设计问题,并考虑3PL运输供应商的信誉等级变化对整体网络配送完成率的影响。通过设计反馈调节机制和信誉评价体系,建立多目标规划模型来描述该问题。针对基本蚁群算法的不足和本问题的特点,设计奖惩蚁群算法,该算法通过增加奖惩因子,提高算法的收敛速度和全局搜索能力,有效避免了算法陷入局部最优。仿真实验结果表明了模型的合理性和奖惩蚁群算法的有效性。     

基于混沌蚁群算法的微网多目标低碳调度

为实现我国到2020年单位国内生产总值二氧化碳排放比2005年下降40%~45%的目标,必须充分利用各地丰富的清洁和可再生能源,大力发展分布式发电技术,实现低碳调度。针对太阳能光伏发电、风力发电的特性,综合考虑接入分布式电源前后系统的总线路损耗、节点电压偏差、发电成本、CO2排放量,提出了微网多目标低碳调度数学模型,并利用混沌蚁群优化算法,通过算例验证了该数学模型的正确性与有效性。     

Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations

This paper studies the simultaneous dock assignment and sequencing of inbound trucks for a multi-door cross docking operation with the objective to minimize total weighted tardiness, under a fixed outbound truck departure schedule. The problem is newly formulated and solved by six different metaheuristic algorithms, which include simulated annealing, tabu search, ant colony optimization, differential evolution, and two hybrid differential-evolution algorithms. To evaluate the total weighted tardiness associated with any given inbound-truck sequence and dock assignment, an operational policy is developed. This policy is employed by every metaheuristic algorithm in searching for the optimal dock assignment and sequence. Each metaheuristic algorithm is tested with 40 problems. The major conclusions are: (1) metaheuristic is generally an effective optimization method for the subject problem; (2) population based metaheuristic algorithms are generally more effective than projection based metaheuristic algorithms; (3) proper selection of algorithmic parameters is important and more critical for projection based metaheuristic algorithms than population based algorithms; (4) the two best algorithms are ant colony optimization and hybrid differential evolution 2; among them, ACO takes less time than hybrid 2 and thus can be declared the best among all the six metaheuristic algorithms tested.     

A multiple ant colony optimization algorithm for the capacitated location routing problem

The success of a logistics system may depend on the decisions of the depot locations and vehicle routings. The location routing problem (LRP) simultaneously tackles both location and routing decisions to minimize the total system cost. In this paper a multiple ant colony optimization algorithm (MACO) is developed to solve the LRP with capacity constraints (CLRP) on depots and routes. We decompose the CLRP into facility location problem (FLP) and multiple depot vehicle routing problem (MDVRP), where the latter one is treated as a sub problem within the first problem. The MACO algorithm applies a hierarchical ant colony structure that is designed to optimize different subproblems: location selection, customer assignment, and vehicle routing problem, in which the last two are the decisions for the MDVRP. Cooperation between colonies is performed by exchanging information through pheromone updating between the location selection and customer assignment. The proposed algorithm is evaluated on four different sets of benchmark instances and compared with other algorithms from the literature. The computational results indicate that MACO is competitive with other well-known algorithms, being able to obtain numerous new best solutions.     

蚁群算法在城市交通系统中的应用

根据城市交通的实际情况,介绍了用蚁群算法求解城市交通行驶中车辆最优路径的方法,帮助车辆找到最优路径,从而选择车流量较少的路径行驶。     

Flexible job shop scheduling with due window—a two-pheromone ant colony approach

Recently, the companies reduce the manufacturing costs and increase capacity efficiency in the competitive environment. Therefore, to balance workstation loading, the hybrid production system is necessary, so that, the flexible job shop system is the most common production system, and there are parallel machines in each workstation. In this study, the due window and the sequential dependent setup time of jobs are considered. To satisfy the customers’ requirement, and reduce the cost of the storage costs at the same time, the sum of the earliness and tardiness costs is the objective. In this study, to improve the traditional ant colony system, we developed the two pheromone ant colony optimization (2PH-ACO) to approach the flexible job shop scheduling problem. Computational results indicate that 2PH-ACO performs better than ACO in terms of sum of earliness and tardiness time.     

A modified artificial bee colony algorithm for order acceptance in two-machine flow shops

We consider a two-stage make-to-order production system characterized by limited production capacity and tight order due dates. We want to make joint decisions on order acceptance and scheduling to maximize the total net revenue. The problem is computationally intractable. In view of the fact that artificial bee colony algorithm has been shown to be an effective evolutionary algorithm to handle combinatorial optimization problems, we first conduct a pilot study of applying the basic artificial bee colony algorithm to treat our problem. Based on the results of the pilot study and the problem characteristics, we develop a modified artificial bee colony algorithm. The experimental results show that the modified artificial bee colony algorithm is able to generate good solutions for large-scale problem instances.     

基于图像和人工鱼群算法的建筑火灾动态疏散路径规划研究

为了解决大型建筑发生火灾时传统静态疏散系统无法根据火灾点和人员拥挤程度进行路径调整这一问题,提出了基于图像和人工鱼群算法的动态疏散路径规划方法。在栅格图上进行路径规划,通过将鱼群的最优解替换为可行解,使鱼群避免陷入局部最优和全局最优相互干扰的情况,并结合摄像头采集图像,通过人脸识别人数,判断当前路径是否拥挤,及时调整路径,从而确保规划出的路径可以避免堵塞,动态疏散人群。仿真实验结果表明,所提算法能够在相同时间内,规划出较蚁群算法路径更短,可避免陷入局部最优和死锁状态,根据拥挤程度及时改变路径,并能够在时间和空间双重约束的情况下实现人群动态疏散。因此,新算法在相同运行时间内可以规划出更短的路径,可以帮助火灾现场人群以更少时间、更短路径、更高效率的方式进行动态疏散。     

Metaheuristics for drilling operation scheduling in Taiwan PCB industries

Among all types of production environment, identical parallel machines are frequently used to increase the manufacturing capacity of the drilling operation in Taiwan printed circuit board (PCB) industries. Additionally, multiple but conflicting objectives are usually considered when a manager plans the production scheduling. Compared to the single objective problem, the multiple-objective version no longer looks for an individual optimal solution, but a Pareto front consisting of a set of non-dominated solutions will be needed and established. The manager then can select one of the alternatives from the set. This research aims at employing a variable neighborhood search (VNS) algorithm and a multiple ant colony optimization (MACO) algorithm to solve the identical parallel-machine scheduling problem with two conflicting objectives: makespan and total tardiness. In VNS, two neighborhoods are defined—insert a job to a different position or swap two jobs in the sequence. To save the computational expense, one of the neighborhoods is randomly selected for the target solution which is also arbitrarily chosen from the current Pareto front. In MACO, a two-phase construction procedure where three colonies are employed in each phase is proposed. These two algorithms are tested on a set of real data collected from a leading PCB factory in Taiwan and their performances are compared. The computational results show that VNS outperforms all competing algorithms—SPGA, MOGA, NSGA-II, SPEA-II, and MACO in terms of solution quality and computational time.     

Ant colony optimization for the single model U-type assembly line balancing problem

An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them.     

Constraint-based ACO for a shared resource constrained scheduling problem

We consider a scheduling problem arising in the mining industry. Ore from several mining sites must be transferred to ports to be loaded on ships in a timely manner. In doing so, several constraints must be met which involve transporting the ore and deadlines. These deadlines are two-fold: there is a preferred deadline by which the ships should be loaded and there is a final deadline by which time the ships must be loaded. Corresponding to the two types of deadlines, each task is associated with a soft and hard due time. The objective is to minimize the cumulative tardiness, measured using the soft due times, across all tasks. This problem can be formulated as a resource constrained job scheduling problem where several tasks must be scheduled on multiple machines satisfying precedence and resource constraints and an objective to minimize total weighted tardiness. For this problem we present hybrids of ant colony optimization, Beam search and constraint programming. These algorithms have previously shown to be effective on similar tightly-constrained combinatorial optimization problems. We show that the hybrid involving all three algorithms provides the best solutions, particularly with respect to feasibility. We also investigate alternative estimates for guiding the Beam search component of our algorithms and show that stochastic sampling is the most effective.