基于免疫修复的快速蚁群优化算法

蚁群优化算法通过信息素记录搜索过程中获取的知识,并基于信息素搜索新的解．影响信息素质量的因素主要是信息素更新策略和蚂蚁已找到的候选解的质量．为了提高已有候选解的质量,提出基于免疫原理识别候选解中的“病变”成分,并对其“病变”成分进行修复．经免疫修复后,候选解的质量大大提高,由它更新的信息素更好地反映了优质解的特点,从而加快了信息的正反馈过程．实验结果验证了该算法的有效性．     

基于免疫修复的快速蚁群优化算法

蚁群优化算法通过信息素记录搜索过程中获取的知识,并基于信息素搜索新的解．影响信息素质量的因素主要是信息素更新策略和蚂蚁已找到的候选解的质量．为了提高已有候选解的质量,提出基于免疫原理识别候选解中的“病变”成分,并对其“病变”成分进行修复．经免疫修复后,候选解的质量大大提高,由它更新的信息素更好地反映了优质解的特点,从而加快了信息的正反馈过程．实验结果验证了该算法的有效性．

     

基于蚁群算法的车间作业调度问题研究

对基于蚁群算法的车间作业调度问题求解进行了研究,在分析了传统蚁群算法求解车间作业调度问题容易出现早熟、收敛于局部最优解以及搜索速度慢的缺陷,提出了一种改进的混合蚁群算法。该方法在信息素更新规则上利用信息素局部更新策略和全局更新策略来进行信息素的更新,并将领域搜索与蚁群算法相结合,从而求得问题的可行解。最后,基于benchmarks问题进行了实验仿真,实验结果证明该改进混合算法的有效性及可行性。     

基于自适应路径选择和信息素更新的蚁群算法

针对蚁群算法加速收敛和早熟、停滞现象的矛盾,提出了一种基于自适应路径选择和信息素更新的蚁群算法,以求在加速收敛和防止早熟、停滞现象之间取得很好的平衡。该算法根据优化过程中解的分布状况,自适应地调整路径选择策略和信息量更新策略。基于旅行商问题的实验验证了算法比一般蚁群算法具有更好的全局搜索能力、收敛速度和解的多样性。     

基于网格划分策略的连续域改进蚁群算法

针对连续空间函数优化问题,提出一种基于网格划分策略的改进蚁群算法。算法使用一种特殊的信息素更新策略,使得更新信息素时不需要使用解的具体目标函数值,从而降低了目标函数值差异化给算法性能带来的不利影响,并且网格点上的信息素可以直接作为构建解过程中的转移概率。对几种典型的连续函数优化问题进行了测试,实验结果表明所提出算法具有很强的搜索能力。     

连续空间多目标最优化问题的蚁群遗传算法

本文提出了一种基于蚁群算法和遗传算法的多目标蚁群遗传算法,用于解决连续空间中带约束条件多目标最优化问题。本算法先将解空间分解成子区域,再用信息素标定这些子区域,信息素对遗传搜索进行指导,在搜索中更新信息素,同时采用了最优决策集的更新策略和搜索收敛退出机制,从而提高求解效率,降低算法复杂度。实验证明,与以往算法相比,此算法能更快、更精确地逼近Pareto前沿。     

基于蚁群优化算法的网格任务映射策略

针对网格环境下实现任务最优映射的问题,提出一种基于蚁群优化算法的网格任务映射策略(ACO-GTM)。该算法通过人工蚂蚁在构建图上行走构建初始解,利用最优改进2-选择局部搜索方法对初始解进行局部优化,并采用全局信息素更新与局部信息素更新相结合的信息素更新策略。最后通过实验与其他算法进行比较,表明所提出的映射算法在最优跨度和负载平衡方面具有明显的优越性。     

引入侦查子群的二进制蚁群算法求解函数优化问题

为求解函数优化问题,将遗传算法中的二进制编码方式引入标准蚁群算法.但由于该算法迭代过程中易出现早熟停滞现象,为此提出一种改进的蚁群算法,该算法在原算法基础上引入一定比例的侦查蚁群.侦查蚁群以一定的概率做侦查搜索以扩大解的搜索空间;在信息素更新策略上,为兼顾当代和历代的搜索成果,采取信息素混合更新策略,同时增强侦查子群的最佳路径信息及其余蚁群的路径信息.最后,通过对几个经典测试函数的求解, 证明该算法解决函数优化问题非常有效,不仅能够克服早熟现象,而且能够加快收敛速度.     

求解TSP问题的一种改进蚁群优化算法

针对基本蚁群算法求解TSP问题时容易出现早熟和停滞现象的缺陷,提出了一种改进的蚁群算法。算法的基本思想是,将信息素分为局部和全局二种不同的信息素,在搜索过程中。对局部和全局信息素采用不同的更新策略和动态的路径选择概率,使得在搜索的中后期能更有效地发现全局最优解。以TSPLIB的数据进行实验的结果表明．在中大型问题上有着更好的发现最优解的能力。     

面向多目标测试用例优先排序的蚁群算法信息素更新策略

针对蚁群算法在求解多目标测试用例优先排序（MOTCP）时收敛速度缓慢、易陷入局部最优的问题,提出一种基于上位基因段（ETS）的信息素更新策略。利用测试用例序列中ETS可以决定适应度值的变化,选取ETS作为信息素更新范围,再根据ETS中测试用例间的适应度增量和测试用例的执行时间更新路径上的信息素值。为进一步提升蚁群算法求解效率、节省蚂蚁依次访问测试用例序列的时间,优化的蚁群算法还通过估算ETS长度重新设置蚂蚁遍历测试用例的搜索终点。实验结果表明,与优化前的蚁群算法及NSGA-Ⅱ相比,优化后的蚁群算法能提升求解MOTCP问题时的收敛速度,获得更优的Pareto解集。     

改进蚁群算法在基于服务质量的Web服务组合优化中的应用

为了克服基础蚁群算法存在的前期搜索速度较慢、后期极易陷入局部最优解的缺点,提出初始信息素分布策略和局部优化策略;同时还提出了依赖解的质量的信息素更新依据,以增强算法过程中信息素的有效积累。将该改进蚁群算法应用于基于服务质量(QoS)的Web服务组合优化问题中,通过在数据集QWS2.0上的实验对改进蚁群算法的可用性和有效性进行了验证。结果表明改进的蚁群算法与基础蚁群算法、利用解与理想解距离更新信息素的改进蚁群算法以及用支配程度作为解的个体评价的改进遗传算法相比,能够找到更多的非劣解,寻优能力更优,表现出了较稳定的性能。     

Modeling the dynamics of ant colony optimization

The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model.     

An efficient hybrid algorithm for resource-constrained project scheduling

We propose an efficient hybrid algorithm, known as ACOSS, for solving resource-constrained project scheduling problems (RCPSP) in real-time. The ACOSS algorithm combines a local search strategy, ant colony optimization (ACO), and a scatter search (SS) in an iterative process. In this process, ACO first searches the solution space and generates activity lists to provide the initial population for the SS algorithm. Then, the SS algorithm builds a reference set from the pheromone trails of the ACO, and improves these to obtain better solutions. Thereafter, the ACO uses the improved solutions to update the pheromone set. Finally in this iteration, the ACO searches the solution set using the new pheromone trails after the SS has terminated. In ACOSS, ACO and the SS share the solution space for efficient exchange of the solution set. The ACOSS algorithm is compared with state-of-the-art algorithms using a set of standard problems available in the literature. The experimental results validate the efficiency of the proposed algorithm.     

一种改进的蚁群算法在工艺规划与车间调度集成优化中的应用

改进标准蚁群算法的执行策略,可提高工艺规划和调度集成问题的求解 质量和效率。通过节点集、有向弧/无向弧集、AND/OR 关系,建立了基于AND/OR 图的工 艺规划和调度集成优化模型。提出一种求解工艺规划与车间调度集成问题的改进蚁群优化算 法,采用了信息素动态更新策略避免收敛过慢和局部收敛,利用多目标优化策略提高求解质 量。仿真结果证明了该算法的有效性。     

Two-stage updating pheromone for invariant ant colony optimization algorithm

Ant colony optimization (ACO) is a metaheuristic approach for combinatorial optimization problems. With the introduction of hypercube framework, invariance property of ACO algorithms draws more attention. In this paper, we propose a novel two-stage updating pheromone for invariant ant colony optimization (TSIACO) algorithm. Compared with standard ACO algorithms, TSIACO algorithm uses solution order other than solution itself as independent variable for quality function. In addition, the pheromone trail is updated with two stages: in one stage, the first r iterative optimal solutions are employed to enhance search capability, and in another stage, only optimal solution is used to accelerate the speed of convergence. And besides, the pheromone value is limited to an interval. We prove that TSIACO not only has the property of linear transformational invariance but also has translational invariance. We also prove that the pheromone trail can limit to the interval (0, 1]. Computational results on the traveling salesman problem show the effectiveness of TSIACO algorithm.     

基于多Agent结构的自适应蚁群优化聚类算法

该文在分析蚁群优化算法多Agent结构的基础上,提出了一种新的自适应蚁群优化聚类算法。算法的多Agent分层结构为L0层agent构造解,L1层agent改进可行解,L2层agent更新信息素,更新后的信息素矩阵为下一轮解的构造提供反馈信息。算法选取变异概率p及信息素残留度ρ作为自适应参数,在演化过程中进行自动调节,较好地解决了加速收敛和停滞早熟的矛盾。实验结果验证了算法的有效性,该算法的聚类效果和运行效率优于GA和SA两种演化聚类算法。     

An ACO-RFD hybrid method to solve NP-complete problems

In this paper we hybridize ant colony optimization (ACO) and river formation dynamics (RFD), two related swarm intelligence methods. In ACO, ants form paths (problem solutions) by following each other’s pheromone trails and reinforcing trails at best paths until eventually a single path is followed. On the other hand, RFD is based on copying how drops form rivers by eroding the ground and depositing sediments. In a rough sense, RFD can be seen as a gradient-oriented version of ACO. Several previous experiments have shown that the gradient orientation of RFD makes this method solve problems in a different way as ACO. In particular, RFD typically performs deeper searches, which in turn makes it find worse solutions than ACO in the first execution steps in general, though RFD solutions surpass ACO solutions after some more time passes. In this paper we try to get the best features of both worlds by hybridizing RFD and ACO. We use a kind of ant-drop hybrid and consider both pheromone trails and altitudes in the environment. We apply the hybrid method, as well as ACO and RFD, to solve two NP-hard problems where ACO and RFD fit in a different manner: the traveling salesman problem (TSP) and the problem of the minimum distances tree in a variable-cost graph (MDV). We compare the results of each method and we analyze the advantages of using the hybrid approach in each case.     

基于记忆表的连续蚁群优化算法

蚁群算法的离散本质限制了其在连续问题求解中的应用,针对该问题提出求解连续函数优化问题的连续蚁群优化算法。对概率密度呈高斯分布的分布函数进行随机采样,为每只蚂蚁产生下一步迭代的 个候选位置,引入记忆表取代基本蚁群算法中的禁忌表,通过对记忆表中的优良解进行动态替换实现信息素更新。与其他连续优化算法的比较结果证明,该算法在复杂度、稳定性等方面具有优势。     

多维背包问题的一个蚁群优化算法

蚁群优化(ACO)是一种通用的启发式方法,已被用来求解很多离散优化问题.近年来,已提出几个ACO算法求解多维背包问题(MKP).这些算法虽然能获得较好的解但也耗用太多的CPU时间.为了降低用ACO求解MKP的复杂性,文章基于一种已提出但未实现过的MKP的信息素表示定义了新的选择概率的规则和相应的基于背包项的一种序的启发式信息,从而提出了一种计算复杂性较低、求解性能较好的改进型蚁群算法.实验结果表明,无论串行执行还是虚拟并行执行,在计算相同任务时,新算法耗用时间少且解的价值更高.不仅如此,在实验中,文中的新算法获得了ORLIB中测试算例5.250-22的两个"新"解.