An ant algorithm for balanced job scheduling in grids

Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids.In the natural environment, the ants have a tremendous ability to team up to find an optimal path to food resources. An ant algorithm simulates the behavior of ants. In this paper, we propose a Balanced Ant Colony Optimization (BACO) algorithm for job scheduling in the Grid environment. The main contributions of our work are to balance the entire system load while trying to minimize the makespan of a given set of jobs. Compared with the other job scheduling algorithms, BACO can outperform them according to the experimental results.     

Routing table updating by using intelligent agents

In a network, one of the important problems is making an efficient routing decision. Many studies have been carried out on making a decision and several routing algorithms have been developed. In a network environment, every node has a routing table and these routing tables are used for making routing decisions. Nowadays, intelligent agents are used to make routing decisions. Intelligent agents have been inspired by social insects such as ants. One of the intelligent agent types is self a cloning ant. In this study, a self cloning ant colony approach is used. Self cloning ants are a new synthetic ant type. This ant assesses the situation and multiplies through cloning or destroying itself. It is done by making a routing decision and finding the optimal path. This study explains routing table updating by using the self cloning ant colony approach. In a real net, this approach has been used and routing tables have been created and updated for every node.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

基于蚁群优化的DBN转移网络结构学习算法

针对动态贝叶斯转移网络的特点,以I-ACO-B为基础,提出基于蚁群优化的分步构建转移网络的结构学习算法ACO-DBN-2S。算法将转移网络的结构学习分为时间片之间和时间片内2个步骤进行,通过改进隔代优化策略,减少无效优化次数。标准数据集下的大量实验结果证明,该算法能更有效地处理大规模数据,学习精度和速度有较大改进。     

面向TSP求解的混合蚁群算法

针对蚁群算法的早熟和停滞等现象,将免疫算法机制引入蚁群算法,提出用于TSP求解的混合算法。该算法具有蚁群算法的自适应反馈机理、收敛速度快和免疫算法操作算子简单和维持种群多样性、防止种群退化等特性。从算法解的质量与效率方面与基本蚁群算法和免疫算法进行比较,结果表明融合免疫机制的蚁群算法性能显著提高,也为解决其他组合优化问题提供一个新的思路。     

改进的蚁群算法在修磨轨迹优化中的应用

提出一种适用于钢坯修磨轨迹优化问题的改进蚁群算法,给出一种修磨轨迹优化问题的实用数学模型。针对蚁群算法对参数敏感的问题,提出用启发信息归一化来解决的办法。仿真实验与初步试用结果表明,经改进蚁群算法优化的修磨轨迹能大幅度减少修磨过程中的空行程。该算法具有一定的理论参考价值和实际意义。     

新蚁群算法模型在大学课程时间表问题中的应用

分析大学课程时间表问题的特征,结合已有蚁群算法的求解策略,构建了新的问题求解模型,提出了一种基于蚁群算法和改进过程的求解算法,并在不同规模的问题实例上进行实验。结果表明,算法在目标函数解的质量上有明显改进。     

无线自组织网络中基于蚁群算法结合连通支配集的路由协议

针对蚁群优化(ACO)在无线自组织网络应用的缺点,如搜寻和维护路由信息过程中需要消耗大量的开销和能量。在ACO算法的基础上,提出一种结合连通支配集的混合路由协议。该协议将网络中的连通支配集(CDS)作为集群节点的辅助结构,从前进蚂蚁中获取网络的状态信息,这些信息仅可以通过每个集群头进行广播,从而减少传输蚂蚁数据包所需的开销。为了增加网络的效率,采用伪随机比例选择策略对后向蚂蚁从源节点到目的地节点间的最优路径进行评估。NS-2网络仿真器实验结果表明,与自组织按需距离向量(AODV)路由协议和蚁群优化路由协议相比,提出的路由协议在数据包传输率、网络总体吞吐量和平均端到端延迟等方面均有明显改进。此外,提出的路由协议消耗的网络资源较少,适合节点连接程度比较高的网络。     

基于蚁群优化的极限学习机选择性集成学习算法

针对现有极限学习机集成学习算法分类精度低、泛化能力差等缺点,提出了一种基于蚁群优化思想的极限学习机选择性集成学习算法。该算法首先通过随机分配隐层输入权重和偏置的方法生成大量差异的极限学习机分类器,然后利用一个二叉蚁群优化搜索算法迭代地搜寻最优分类器组合,最终使用该组合分类测试样本。通过12个标准数据集对该算法进行了测试,该算法在9个数据集上获得了最优结果,在另3个数据集上获得了次优结果。采用该算法可显著提高分类精度与泛化性能。     

Cooperative group optimization with ants (CGO-AS): Leverage optimization with mixed individual and social learning

We present CGO-AS, a generalized ant system (AS) implemented in the framework of cooperative group optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. Ant colony is a simple yet efficient natural system for understanding the effects of primary intelligence on optimization. However, existing AS algorithms are mostly focusing on their capability of using social heuristic cues while ignoring their individual learning. CGO can integrate the advantages of a cooperative group and a low-level algorithm portfolio design, and the agents of CGO can explore both individual and social search. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The optimization performance is tested with instances of the traveling salesman problem (TSP). The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning. The best performance is achieved under the condition when agents use individual memory as their primary information source, and simultaneously use social memory as their searching guidance. In comparison with existing AS systems, CGO-AS retains a faster learning speed toward those higher-quality solutions, especially in the later learning cycles. The leverage in optimization by CGO-AS is highly possible due to its inherent feature of adaptively maintaining the population diversity in the individual memory of agents, and of accelerating the learning process with accumulated knowledge in the social memory.