基于改进蚁群算法的汽车混流装配调度模型求解

针对某汽车总装车间混流装配过程涉及大量人工以及人机协同操作而导致工位过载、整车装配质量无法得到保证的问题,建立了瓶颈选装工位负载平衡化、考虑换装与提前作业时间的加工滞后次数最小化的分层序列双目标优化模型,同时设计了一种改进蚁群算法。该算法在信息素全局更新以及概率转移规则过程中,使用一种特定启发式函数,并更改迭代过程中最优解的评价方法。仿真对比实验结果表明,该算法在优化目标函数过程中的收敛速度、收敛精度、最优解质量等方面均优于传统蚁群算法和对比遗传算法,验证了模型和算法的有效性。此外,该算法还可反向求解加工滞后次数为零时的计划生产节拍,具有一定的生产指导意义。     

Adaptive subcarrier and bit allocation based on ant colony optimization

The problem of resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) system is a combinatorial optimization problem, difficult to obtain optimal solutions in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, an algorithm based on ant colony optimization (ACO), which is a typical algorithm of metaheuristic methods, is proposed for the problem, utilizing excellent search performance of ACO to obtain good solutions. In addition, a parameter is applied to balance the efficiency and fairness of resource allocation. Performance analysis between algorithms based on ACO and genetic algorithm (GA) is carried out, indicating that the proposed algorithm based on ACO outperforms traditional linear algorithms as well as GA in the system throughput with assurance of fairness simultaneously, being as a promising technology for OFDM resource allocation.     

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.     

基于蚁群最优的配电网网架规划方法

配电网规划问题是一个复杂的组合优化问题。蚁群算法是用于解决组合优化问题的一种高效的随机化内启发式全局搜索技术,能有效地求解大规模组合优化问题。文中结合单阶段配电网规划问题的特点,应用蚁群算法来解决配电网规划问题,建立了相应的数学模型,并给出求解算法。算例的计算结果表明了这种方法是可行、有效的。     

基于蚁群优化算法的云数据库动态路径规划

云计算是下一代计算网络模型的发展趋势。云中的存储资源如何快速地路由,更是研究的难点。蚁群算法是基于群体的仿生优化算法,具有智能搜索、全局优化、鲁棒性、分布式计算和容易与其他算法相结合等优点。根据这两种事物的特点,提出了合理的结合算法,该算法能够在云中快速、合理地找到所需访问的数据库,减少云数据库数路由的动态负荷,从而很大程度上提高云计算的效率。     

一种改进ACO的WSNs能量高效路由算法

针对能量和存储能力在无线传感器网络（Wireless Sensor Networks,WSNs）路由上的特殊要求,为了使节点能量消耗相对均衡,同时避免出现拥塞,提出了一种改进的蚂蚁算法。仿真结果表明该算法能够更有效地降低通信负载,减少能量消耗。     

一种基于独立性测试和蚁群优化的贝叶斯网学习算法

针对ACO-B蚁群算法学习贝叶斯网结构的不足, 提出了一种基于独立性测试和蚁群优化的改进算法. 新算法首先利用0阶独立性测试来限制侯选结构的搜索空间, 避免了蚁群的一些不必要的搜索, 然后融合解的全局评分增益和节点间局部的互信息, 给出了启发能力更强的启发函数来引导随机搜索. 实验结果表明, 新算法能够更有效地处理大规模数据, 且学习速度有较大改进.     

基于蚁群优化算法的精密伺服转台故障诊断方法

提出了一种基于蚁群优化算法的精密伺服转台故障诊断方法. 根据现场观测建立了转台系统故障特征模式库. 利用蚁群优化算法求解故障特征模式的最优分类问题, 并定义敏感度和明确度来评价蚁群搜索到的诊断规则的分类性能, 以减少故障特征信息中的冗余信息, 使诊断规则得到约简. 对某精密伺服转台的若干类故障诊断结果表明, 该方法具有收敛速度快、鲁棒性强、诊断精度高和结果可靠等优点.     

Ant colony optimization algorithm to solve for the transportation problem of cross-docking network

This paper addresses the transportation problem of cross-docking network where the loads are transferred from origins (suppliers) to destinations (retailers) through cross-docking facilities, without storing them in a distribution center (DC). We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and then route them either directly to the customers or indirectly to cross-docking facilities so the loads can be consolidated. For generating a truck operating plan in this type of distribution network, the problem was formulated using an integer programming (IP) model and solved using a novel ant colony optimization (ACO) algorithm. We solved several numerical examples for verification and demonstrative purposes and found that our proposed approach finds solutions that significantly reduce the shipping cost in the network of cross-docks and considerably outperform Branch-and-Bound algorithm especially for large problems.     

改进蚁群算法在迷宫路径寻优中的应用研究

针对机器人在障碍环境下寻找最优路径的问题,提出了一种动态环境下的机器人路径规划的仿生算法.该算法采用栅格法对场地建模,并模拟蚂蚁的觅食行为,由多只蚂蚁协作完成最优路径的搜索.搜索过程采用了概率搜索策略和自适应调整信息素的方法,使得搜索策略更有效.仿真实验结果表明,在场地复杂的情况下,该算法可以有效地规划出最优路径.