全局自适应蚁群优化算法

为解决蚁群算法存在的收敛速度慢和容易陷入局部最优等缺点,分析了其产生的主要原因,介绍了AS和MMAS算法的工作原理,并基于参数自适应思想,提出了全局自适应蚁群优化算法(GAO).对状态转移和信息素更新等规则做出改进,详尽给出了GAO的编程步骤.针对TSP问题,通过与AS和MMAS算法的数值实验结果比较分析,表明GAO算法具有优良的全局优化能力和适当的收敛时间.     

蚁群算法参数优化

针对蚁群算法运行参数选取问题,提出一种利用粒子群优化算法对蚁群算法的运行参数进行优化选择的方法。将蚁群算法的运行参数作为粒子群的位置信息,在算法迭代过程中使用粒子的当前位置作为算法参数,运行蚁群算法求解标准优化问题,设计适应值评价函数对求解性能做出评价,引导粒子向着适应值高的方向趋近。仿真结果表明,该算法能够方便有效地实现对蚁群算法运行参数的优化选取。     

蚁群混沌混合优化算法

为了克服混沌搜索的盲目性,提出了一种蚁群算法和混沌优化算法相结合的混合优化算法,该算法利用蚁群算法中信息素正反馈的思想指导当前混沌搜索的区域。工作蚁群按照信息素的浓度高低,分别按照不同的概率搜索不同的搜索区域,从而可减少混沌盲目搜索的次数。仿真结果表明,该方法能够明显提高混沌优化算法的寻优效率,同时算法的通用性将有所提高。另外,对于含有多个全局最优解的函数,在一次寻优过程中,该算法可以找到全部最优解,这是通常混沌搜索算法所不具备的。     

增强型的蚁群优化算法

旅行商问题是一个NP-Hard组合优化问题。根据蚁群优化算法和旅行商问题的特点,论文提出了对蚁群中具有优质解的蚂蚁个体所走路径上的信息素强度进行增强的方法,并同其他的优化算法进行了比较,仿真结果表明,对具有全局和局部最优解的个体所走路径上的信息素强度进行增强的蚁群优化算法比标准的蚁群优化算法和其他优化算法在执行效率和稳定性上要高。     

一种带有摄动的导向性蚁群算法

针对加入导向性局部搜索(Guided Local Search,GLS)的蚁群算法(Ant Colony Optimization,ACO)容易过早收敛的问题,提出一种带有摄动的导向性蚁群算法(Perturbation Guided Ant Colony Optimization,PGACO),该算法在当前解表现出过早收敛的趋势时,采用摄动(Perturbation)方式干扰解构建过程,使当前解移动到其邻域空间,从而产生一个新的可行解来避免算法过早收敛,提高算法求解的精度。实验结果表明,PGACO能有效地改善过早收敛问题,获得更优的可行解和执行速度,同时具有更强的全局搜索能力,能进一步提高算法的性能。     

蚁群算法综述

群集智能作为一种新兴的演化计算技术已成为越来越多研究者的关注焦点,其理论和应用得到了很大的发展。作为群集智能的代表方法之一,蚁群算法ACO(Ant Colony Optimization,简称ACO)以其实现简单、正反馈、分布式的优点得到广泛的应用。本文对蚁群算法的最新进展进行了综述和展望。     

基于交互式的并行蚁群优化算法

传统蚁群优化算法研究已经取得了很多重要的成果,但是在解决大规模组合优化问题时仍存在早熟收敛,搜索时间长等缺点.为此,将邻域搜索技术与蚁群优化算法进行融合,提出一种新的并行蚁群优化算法,实验结果表明,在解决大规模TSP问题时,该算法求解质量和稳定性更好,在短时间内即可得到较高质量的解.     

Classification With Ant Colony Optimization

Ant colony optimization (ACO) can be applied to the data mining field to extract rule-based classifiers. The aim of this paper is twofold. On the one hand, we provide an overview of previous ant-based approaches to the classification task and compare them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study. On the other hand, a new ant-based classification technique is proposed, named AntMiner+. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Furthermore, the commonly encountered problem in ACO of setting system parameters is dealt with in an automated, dynamic manner. Our benchmarking experiments show an AntMiner+ accuracy that is superior to that obtained by the other AntMiner versions, and competitive or better than the results achieved by the compared classification techniques.     

变尺度混沌蚁群优化算法

将变尺度混沌搜索算法融合到蚁群算法中,并用于求解连续空间优化问题。蚁群算法每一次迭代结束时,就使用混沌搜索算子在当前全局最优解附近搜索更好的解。而随着蚁群算法的进行,混沌算子搜索范围逐渐缩小,这样,混沌算子在蚁群搜索的初期起到防止陷入局部最优的作用,在蚁群搜索后期起到提高搜索精度的作用。将变尺度混沌蚁群优化算法用于求解函数优化问题的实验结果表明,该算法在求解包括欺骗性函数和高维函数在内的多种测试函数优化问题方面具有很好的效果。     

Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling

This paper presents an algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem. This approach is developed to deal with the model previously proposed in [3] for the parallel machine case. This model is formulated according to a bi-objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability models are used to take into account the maintenance aspect. To improve the quality of solutions found in our previous study, an algorithm based on Multi-Objective Ant Colony Optimization (MOACO) approach is developed. The goal is to simultaneously determine the best assignment of production tasks to machines as well as preventive maintenance (PM) periods of the production system, satisfying at best both objectives of production and maintenance. The experimental results show that the proposed method outperforms two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2 and NSGA II.     

Ant Colony Optimization based approach for efficient packet filtering in firewall

A firewall is a security guard placed at the point of entry between a private network and the outside network. The function of a firewall is to accept or discard the incoming packets passing through it based on the rules in a ruleset. Approaches employing Neural networks for packet filtering in firewall and packet classification using 2D filters have been proposed in the literature. These approaches suffer from the drawbacks of acceptance of packets from the IP address or ports not specified in the firewall rule set and a restricted search in the face of multiple occurrences of the same IP address or ports respectively. In this paper we propose an Ant Colony Optimization (ACO) based approach for packet filtering in the firewall rule set. Termed Ant Colony Optimization Packet Filtering algorithm (ACO-PF), the scheme unlike its predecessors, considers all multiple occurrences of the same IP address or ports in the firewall rule set during its search process. The other parameters of the rule matching with the compared IP address or ports in the firewall ruleset are retrieved and the firewall decides whether the packet has to be accepted or rejected. Also this scheme has a search space lesser than that of binary search in a worst case scenario. It also strictly filters the packets according to the filter rules in the firewall rule set. It is shown that ACO-PF performs well when compared to other existing packet filtering methods. Experimental results comparing the performance of the ACO-PF scheme with the binary search scheme, sequential search scheme and neural network based approaches are presented.     

带有狮王竞比参数的蚁群优化算法

由于蚁群算法采用随机选择策略,使得进化速度较慢,容易出现停滞现象,从而不能对解空间进一步进行搜索,不利于发现更好的解.针对以上问题,提出了一个带有狮王竞比参数的蚁群优化算法.该算法借鉴狮子种群生存竞争中狮王法则的作用,减少大量不必要的搜索,从而大大缩短了求解时间,同时又引用了最大—最小蚂蚁系统(MMAS)算法对信息素的限制,有效地控制了搜索停滞的问题.通过结合MMAS算法的仿真,结果表明:带有狮王竞比参数的改良算法,在求解同样TSP问题时,大大地缩短了优化时间,并且得到了更优的解.     

High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

     

蚁群优化算法及其应用研究进展

综述了近年来蚁群算法及其在组合优化中的应用研究成果。首先简述了蚁群的觅食行为及蚂蚁的信息系统，其次介绍了人工蚁群算法的基本原理及其主要特点。然后概述了这种算法在组合优化问题中的多种应用，诸如旅行商问题(TSP)、二次分配问题(QAP)、任务调度问题(JSP)、车辆路线问题(VRP)、图着色问题(GCP)、有序排列问题(SOP)及网络由问题等。最后对蚁群算法仍需要解决的问题和未来的发展方向进行了探讨。     

有限级信息素蚁群算法

提出一种新的蚁群算法,将信息素分成有限个级别,通过级别的更新实现对信息素的更新,并且信息素的更新量独立于目标函数值. 文中采用有限马氏链的理论证明算法可以线性地收敛到全局最优解. 针对TSP问题,通过与MMAS和ACS等蚁群算法的数值实验结果进行比较,表明所提出的算法是有效的、鲁棒的.     

On the Invariance of Ant Colony Optimization

Ant colony optimization (ACO) is a promising metaheuristic and a great amount of research has been devoted to its empirical and theoretical analysis. Recently, with the introduction of the hypercube framework, Blum and Dorigo have explicitly raised the issue of the invariance of ACO algorithms to transformation of units. They state (Blum and Dorigo, 2004) that the performance of ACO depends on the scale of the problem instance under analysis. In this paper, we show that the ACO internal state - commonly referred to as the pheromone - indeed depends on the scale of the problem at hand. Nonetheless, we formally prove that this does not affect the sequence of solutions produced by the three most widely adopted algorithms belonging to the ACO family: ant system, MAX-MIN ant system, and ant colony system. For these algorithms, the sequence of solutions does not depend on the scale of the problem instance under analysis. Moreover, we introduce three new ACO algorithms, the internal state of which is independent of the scale of the problem instance considered. These algorithms are obtained as minor variations of ant system, MAX-MIN ant system, and ant colony system. We formally show that these algorithms are functionally equivalent to their original counterparts. That is, for any given instance, these algorithms produce the same sequence of solutions as the original ones.     

基于生命周期的二元蚁群优化算法

将自然生态系统中生物生命周期的思想引入二元蚁群优化算法中,通过对蚂蚁设置相应的营养阈值而执行繁殖、迁徙、死亡操作,从而保持种群的动态多样性,进而克服二元蚁群优化算法易陷入局部最优的缺陷,然后结合分形维数将该算法应用于属性约简问题中,通过UCI中的6个数据集进行测试,结果表明该算法具有较好的可行性和有效性.     

结合信息熵的多种群博弈蚁群算法

针对蚁群算法在旅行商问题（Traveling Salesman Problem，TSP）求解中难以找到最优解、容易早熟的问题，提出一种基于信息熵的多种群博弈蚁群算法。首先，算法采用主从合作博弈机制，引入夏普里公式和信息熵，自适应调整各算子的使用权重，同时构造奖惩算子，提高算法收敛性；然后，对从种群引入针锋相对策略，进行协同学习，提高从种群多样性；进一步，根据帕累托最优原则，对从种群引入协调博弈机制进行自适应合作，提高算法性能。最后，以TSPLIB标准库中的多组TSP问题作为实验算例，进行算法性能分析。实验结果表明，对比传统算法，该算法具有良好的求解精度和求解稳定性。