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| 1 | A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends显示文摘Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments. | Jun Tang Gang Liu Qingtao Pan | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，10： | 18 |
| 2 | 基于连续型深度置信神经网络的软件可靠性预测显示文摘为了提高软件可靠性智能预测的精度,采用连续型深度置信神经网络算法用于软件可靠性预测。首先提取影响软件可靠性的核心要素样本,并获取样本要素的关键特征;然后建立连续型深度置信神经网络(Deep Belief Network,DBN)的软件可靠性预测模型,输入待预测样本,通过多个受限波尔兹曼机(Restricted Boltzmann Machine,RBM)层的预处理训练,以及多次反向微调迭代获取DBN权重等参数,直到达到最大RBM层数和最大反向微调迭代次数;最后获得稳定的软件可靠性预测模型。实验结果证明,通过合理设置DBN隐藏层节点数和学习速率,可以获得良好的软件可靠性预测准确率和标准差。与常用的软件可靠性预测算法相比,所提算法的预测准确度高且标准差小,在软件可靠性预测方面的适用度较高。 | 亓慧 史颖 李灯熬 穆晓芳 侯明星 | 2021 | 计算机科学2021，48，5： | 3 |
| 3 | 神经网络非梯度优化方法研究进展显示文摘神经网络优化是机器学习领域的一个基础性前沿课题。相较于神经网络的纯梯度优化算法,非梯度算法在解决收敛速度慢、易陷入局部最优、无法解决不可微等问题上表现出更大的优势。在剖析基于梯度的神经网络方法优缺点的基础上,重点对部分非梯度优化方法进行了综述,包括前馈神经网络优化和随机搜索优化;从基本理论、训练神经网络的步骤以及收敛性等方面对非梯度优化方法的优缺点和应用情况进行了分析;总结了基于非梯度的训练神经网络的算法在理论和应用方面面临的挑战并且展望了未来的发展方向。 | 盛蕾 陈希亮 康凯 | 2022 | 计算机工程与应用2022，58，17： | 2 |
| 4 | Grey-based approach for estimating software reliability under nonhomogeneous Poisson process显示文摘Due to the randomness and time dependence of the factors affecting software reliability, most software reliability models are treated as stochastic processes, and the non-homogeneous Poisson process(NHPP) is the most used one.However, the failure behavior of software does not follow the NHPP in a statistically rigorous manner, and the pure random method might be not enough to describe the software failure behavior. To solve these problems, this paper proposes a new integrated approach that combines stochastic process and grey system theory to describe the failure behavior of software. A grey NHPP software reliability model is put forward in a discrete form, and a grey-based approach for estimating software reliability under the NHPP is proposed as a nonlinear multi-objective programming problem. Finally, four grey NHPP software reliability models are applied to four real datasets, the dynamic R-square and predictive relative error are calculated. Comparing with the original single NHPP software reliability model, it is found that the modeling using the integrated approach has a higher prediction accuracy of software reliability. Therefore, there is the characteristics of grey uncertain information in the NHPP software reliability models, and exploiting the latent grey uncertain information might lead to more accurate software reliability estimation. | LIU Xiaomei XIE Naiming | 2022 | Journal of Systems Engineering and Electronics2022，33，2： | 1 |
| 5 | 梯度下降优化神经网络的主轴可靠性预测模型显示文摘为了提升机床主轴可靠性预测精度,提出了基于优化级联前向神经网络进行数控机床主轴可靠性预测的改进算法。将小批量梯度下降算法与级联前向神经网络预测方法相结合进行神经网络优化,增加了预测的准确度。对收集的主轴可靠性相关数据进行初步分析,选取前n个可靠度数据以及第n+1个可靠度数据对应的故障时间点t作为神经网络的输入变量,第n+1个可靠度数据作为输出变量,完成可靠性预测模型的训练以及测试数据的误差对比分析。实例仿真分析结果表明:应用该方法得到的可靠度预测值最大相对误差的绝对值为2.41%,小于3%,该预测方法精度较高。与BP神经网络等其他预测方法得到的预测结果最大相对误差大于3%,甚至超过10%相比,可以实现数控机床主轴更加精确的预测,为研究数控机床可靠性提供理论依据。 | 王晓燕 王品 郎贺 白贤明 | 2022 | 沈阳航空航天大学学报2022，39，4： | 1 |
| 6 | Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution显示文摘Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers. | Yang Yu Zhenyu Lei Yirui Wang Tengfei Zhang Chen Peng Shangce Gao | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，1： | 1 |
| 7 | 基于CA-RBF神经网络的导弹健康状态预测显示文摘为了解决导弹健康状态评估指标的选取问题,以及选取的指标与导弹健康状态映射关系问题,提出一种基于CA-RBF神经网络的导弹健康状态预测方法。首先,通过导弹寿命剖面分析导弹健康状态影响因素,并给出量化方法;然后,运用对应分析(Correspondence Analysis,CA)方法进行健康状态影响因子的筛选,以所选因子和导弹健康状态评估结果作为神经网络的输入和输出,建立RBF神经网络训练样本,给出导弹健康状态的预测;最后,通过实例分析说明所提方法的实用性和有效性。该方法可为导弹健康状态指标的选取和导弹健康状态预测提供新思路,为导弹预防性维修决策提供依据。 | 李海君 宋超 赵建忠 | 2022 | 航空兵器2022，29，5： | 0 |
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