| Citation: | TANG Y，DAI Q，YANG M Y，et al. Software defect prediction algorithm for intra-membrane sparrow optimizing ELM[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：643-654 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0438
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The original sparrow search algorithm,easy to fall into local extremum in the later stage of iteration, has the problems of low optimization accuracy. Combining the improved sparrow search algorithm with efficient optimization performance and the membrane computing with parallel computing capability, an intra-membrane sparrow optimization algorithm is proposed (IMSSA). The experimental results on ten CEC2017 test functions show that IMSSA has higher optimization accuracy. In addition, to further verify the performance of IMSSA, the extreme learning machine(ELM) parameters are optimized using IMSSA. An intra-membrane sparrow optimal ELM algorithm to be used in software defect predictionis proposed (IMSSA-ELM). The experimental results show that in the 15 public software defect datasets, the prediction performance of the IMSSA-ELM algorithm is significantly better than the other fourcompared algorithms under the two evaluation indicators of G-mean and MCC. The results also show that the IMSSA-ELM algorithm has better prediction accuracy and stability,and have obvious statistical significance in Friedman ranking and Holm’s post-hoc test nonparametric tests.
| [1] |
XU Z, LI L, YAN M, et al. A comprehensive comparative study of clustering-based unsupervised defect prediction models[J]. Journal of Systems and Software, 2021, 172: 110862. doi: 10.1016/j.jss.2020.110862
|
| [2] |
JAYANTHI R, FLORENCE L. Software defect prediction techniques using metrics based on neural network classifier[J]. Cluster Computing, 2019, 22(1): 77-88.
|
| [3] |
董浩, 李明星, 张淑清, 等. 基于核主成分分析和极限学习机的短期电力负荷预测[J]. 电子测量与仪器学报, 2018, 32(1): 188-193.
DONG H, LI M X, ZHANG S Q, et al. Short-term power load forecasting based on kernel principal component analysis and extreme learning machine[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(1): 188-193(in Chinese).
|
| [4] |
陈恒志, 杨建平, 卢新春, 等. 基于极限学习机(ELM) 的连铸坯质量预测[J]. 工程科学学报, 2018, 40(7): 815-821.
CHEN H Z, YANG J P, LU X C, et al. Quality prediction of the continuous casting bloom based on the extreme learning machine (ELM)[J]. Chinese Journal of Engineering Science, 2018, 40(7): 815-821(in Chinese).
|
| [5] |
LIU B Y, CHEN G L, LIN H C, et al. Prediction of IGBT junction temperature using improved cuckoo search-based extreme learning machine[J]. Microelectronics Reliability, 2021, 124: 114267. doi: 10.1016/j.microrel.2021.114267
|
| [6] |
DING L, ZHANG X Y, WU D Y, et al. Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer[J]. Journal of Integrative Medicine, 2021, 19(5): 395-407. doi: 10.1016/j.joim.2021.08.001
|
| [7] |
LI L L, SUN J, TSENG M L, et al. Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation[J]. Expert Systems with Applications, 2019, 127: 58-67. doi: 10.1016/j.eswa.2019.03.002
|
| [8] |
孙远, 杨峰, 郑晶, 等. 基于膜计算与粒子群算法的盲源分离方法[J]. 振动与冲击, 2018, 37(17): 63-71.
SUN Y, YANG F, ZHENG J, et al. Blind source separation method based on membrane computing and particle swarm optimization[J]. Journal of Vibration and Shock, 2018, 37(17): 63-71(in Chinese).
|
| [9] |
谢佩军, 张育斌. 膜计算粒子群算法改进极限学习机的水肥预测模型研究[J]. 中国农机化学报, 2021, 42(4): 142-149.
XIE P J, ZHANG Y B. Research on water and fertilizer prediction model of improved extreme learning machine by membrane computing particle swarm optimization[J]. Chinese Journal of Chinese Agricultural Mechanization, 2021, 42(4): 142-149(in Chinese).
|
| [10] |
ADNAN R M, MOSTAFA R R, KISI O, et al. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization[J]. Knowledge-Based Systems, 2021, 230: 107379. doi: 10.1016/j.knosys.2021.107379
|
| [11] |
XU Z, LIU J, LUO X P, et al. Software defect prediction based on kernel PCA and weighted extreme learning machine[J]. Information and Software Technology, 2019, 106: 182-200. doi: 10.1016/j.infsof.2018.10.004
|
| [12] |
KHADIJAH K, SASONGKO P S. Software defect prediction using synthetic minority over-sampling technique and extreme learning machine[J]. Kinetik Game Technology Information System Computer Network Computing Electronics and Control, 2020, 5(3): 203-210.
|
| [13] |
曾亮, 雷舒敏, 王珊珊, 等. 基于OVMD-SSA-DELM-GM模型的超短期风电功率预测方法[J]. 电网技术, 2021, 45(12): 4701-4712.
ZENG L, LEI S M, WANG S S, et al. Ultra-short-term wind power prediction method based on OVMD-SSA-DELM-GM model[J]. Power System Technology, 2021, 45(12): 4701-4712 (in Chinese).
|
| [14] |
刘栋, 魏霞, 王维庆, 等. 基于SSA-ELM的短期风电功率预测[J]. 智慧电力, 2021, 49(6): 53-59.
LIU D, WEI X, WANG W Q, et al. Short-term wind power prediction based on SSA-ELM[J]. Smart Power, 2021, 49(6): 53-59(in Chinese).
|
| [15] |
兰世豪, 韩涛, 黄友锐, 等. 基于膜计算和粒子群的煤矿移动机器人动态窗口算法研究[J]. 工矿自动化, 2020, 46(11): 46-53.
LAN S H, HAN T, HUANG Y R, et al. Research on dynamic window algorithm of coal mine mobile robot based on membrane computing and particle swarm[J]. Industry and Mine Automation, 2020, 46(11): 46-53(in Chinese).
|
| [16] |
XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.
|
| [17] |
ZHANG C, DING S. A stochastic configuration network based on chaotic sparrow search algorithm[J]. Knowledge-Based Systems, 2021, 220(10): 106924.
|
| [18] |
付华, 刘昊. 多策略融合的改进麻雀搜索算法及其应用[J]. 控制与决策, 2022, 37(1): 87-96.
FU H, LIU H. Improved sparrow search algorithm based on multi-strategy fusion and its application[J]. Control and Decision, 2022, 37(1): 87-96(in Chinese).
|
| [19] |
WANG W C, XU L, CHAU K W, et al. Yin-Yang firefly algorithm based on dimensionally Cauchy mutation[J]. Expert Systems with Applications, 2020, 150: 113216. doi: 10.1016/j.eswa.2020.113216
|
| [20] |
王正通, 程凤芹, 尤文, 等. 基于翻筋斗觅食策略的灰狼优化算法[J]. 计算机应用研究, 2021, 38(5): 1434-1437.
WANG Z T, CHENG F Q, YOU W, et al. Optimization algorithm of gray wolf based on somersault foraging strategy[J]. Application Research of Computers, 2021, 38(5): 1434-1437(in Chinese).
|
| [21] |
PĂUN G. Computing with membranes[J]. Journal of Computer and System Sciences, 2000, 61(1): 108-143.
|
| [22] |
SALLAM K M, ELSAYED S M, CHAKRABORTTY R K, et al. Improved multi-operator differential evolution algorithm for solving unconstrained problems[C]//Proceedings of the IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2020: 1-8.
|
| [23] |
MOHAMED A W, HADI A A, MOHAMED A K, et al. Evaluating the performance of adaptive gainingsharing knowledge based algorithm on CEC 2020 benchmark problems[C]//Proceedings of the IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2020: 1-8.
|
| [24] |
江妍, 马瑜, 梁远哲, 等. 基于分数阶麻雀搜索优化OTSU肺组织分割算法[J]. 计算机科学, 2021, 48(S1): 28-32.
JIANG Y, MA Y, LIANG Y Z, et al. Optimization of OTSU lung tissue segmentation algorithm based on fractional sparrow search[J]. Computer Science, 2021, 48(S1): 28-32(in Chinese).
|
| [25] |
GARCI S, TRIGUERO I, DERRAC J, et al. Evolutionary-based selection of generalized instances for imbalanced classification[J]. Knowledge-Based Systems, 2012, 25(1): 3-12.
|
| [26] |
吕鑫, 慕晓冬, 张钧, 等. 混沌麻雀搜索优化算法[J]. 北京航空航天大学学报, 2021, 47(8): 1712-1720.
LYU X, MU X D, ZHANG J, et al. Chaossparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1712-1720(in Chinese).
|
| [27] |
DAI Q, LIU J W, LIU Y. Multi-granularity relabeled under-sampling algorithm for imbalanced data[J]. Applied Soft Computing, 2022, 124: 109083. doi: 10.1016/j.asoc.2022.109083
|
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