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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (1): 27-38    DOI: 10.3785/j.issn.1008-973X.2025.01.003
    
Review on computational intelligence based on parallel computing
Fei WU(),Jiacheng CHEN,Wanliang WANG*()
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
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Abstract  

Traditional computational intelligence technology was found to lack real-time capabilities and adaptability, and computational intelligence technology based on parallel computing made computational efficiency improve and addressed the issue of compatible processing of multimodal information. From three branches of computational intelligence: neural networks, evolutionary algorithms, and swarm intelligence algorithms, the current states were reviewed on the integration of computational intelligence and big data-parallel computing. Problems present in parallel computing intelligence were summarized, and some thoughts were given to the development direction of related studies.

Key words： parallel computing      computational intelligence      neural network      evolutionary algorithm      swarm intelligence     
Received: 10 December 2023      Published: 18 January 2025
CLC:  TP 301  
Fund:  国家自然科学基金资助项目（61873240）.
Corresponding Authors: Wanliang WANG     E-mail: wfmook@163.com;zjutwwl@zjut.edu.cn
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Fei WU
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Cite this article:

Fei WU,Jiacheng CHEN,Wanliang WANG. Review on computational intelligence based on parallel computing. Journal of ZheJiang University (Engineering Science), 2025, 59(1): 27-38.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.01.003     OR     https://www.zjujournals.com/eng/Y2025/V59/I1/27


基于并行计算的计算智能综述

传统计算智能技术缺乏实时性和适应性，基于并行计算的计算智能技术能够提高计算效率，解决多模态信息兼容处理的问题. 分别从智能计算的3个分支（神经网络、进化算法和群智能算法）介绍计算智能与大数据并行计算融合的研究现状. 总结并行计算智能面临的问题与挑战，思考相关研究的发展方向.


关键词： 并行计算,  计算智能,  神经网络,  进化算法,  群智能 
框架并行性接口开源随机梯度下降预训练模型
DeepLearnning4J[18]数据Java、Scala是同步CNN、RNN、 LSTM、DBN、SAE
H2O Deep Water[19]数据—是——
CaffeOnSpark[20]数据和模型Scala and Python是同步DNN、LSTM
TensorFlow on Spark数据Python是异步CNN、DNN for MNIST
Spark ONE[21]数据Scala否同步和异步—
DeepSpark[22]数据Scala否异步和弹性平均梯度下降—
SparkNET[23]数据Scala and Java是在第N次迭代中同步TensorFlow and Caffe
DeepDist[24]数据Python是同步Word to Vector
DistDL[25]数据和模型—否——
SparkML[26]数据Scala、Java and Python否同步, L-BFGSCNN
Tab.1 Frameworks for parallel deep learning
Fig.1 Development timeline of swarm intelligence optimization algorithms
Fig.2 Architecture diagram of parallel swarm intelligence
框架数据处理数据流计算模型计算速度支持语言内存缓存硬件要求迭代处理
Hadoop批处理线性数据流面向批次低Java、C++、C、Python、Perl、Groovy、Ruby无低无
Spark批处理+流处理循环数据流微批次处理中Java、Python、R、Scala有高有
Flink批处理+流处理循环数据流连续流模型高Java、Python、R、Scala有高有
Tab.2 Performance comparison of big data parallelization frameworks
[1]   陈永忠, 陈顺怀 计算智能技术综述[J]. 航海科技动态, 2000, (6): 1- 3
CHEN Yongzhong, CHEN Shunhuai Review of computational intelligence technologies[J]. Marine Technical News and Trends, 2000, (6): 1- 3
[2]   王万良, 金雅文, 陈嘉诚, 等 多角色多策略多目标粒子群优化算法[J]. 浙江大学学报: 工学版, 2022, 56 (3): 531- 541
WANG Wanliang, JIN Yawen, CHEN Jiacheng, et al Multi-objective particle swarm optimization algorithm with multi-role and multi-strategy[J]. Journal of Zhejiang University: Engineering Science, 2022, 56 (3): 531- 541
[3]   王万良, 张兆娟, 高楠, 等 基于人工智能技术的大数据分析方法研究进展[J]. 计算机集成制造系统, 2019, 25 (3): 529- 547
WANG Wanliang, ZHANG Zhaojuan, GAO Nan, et al Progress of big data analysis methods based on artificial intelligence technology[J]. Computer Integrated Manufacturing Systems, 2019, 25 (3): 529- 547
[4]   郭平, 王可, 罗阿理, 等. 大数据分析中的计算智能研究现状与展望[J]. 软件学报, 2015, 26(11): 3010–3025.
GUO Ping, WANG Ke, LUO Ali, et al. Computational intelligence for big data analysis: current status and future prospect [J] Journal of Software , 2015, 26(11): 3010–3025.
[5]   刘广轩, 黄山, 胡佳丽, 等 面向 Flink 流处理框架的主动备份容错优化[J]. 浙江大学学报: 工学版, 2022, 56 (2): 297- 305
LIU Guangxuan, HUANG Shan, HU Jiali, et al Fault tolerant optimization of active backup for flink stream processing framework[J]. Journal of Zhejiang University: Engineering Science, 2022, 56 (2): 297- 305
[6]   YANG C T, KRISTIANI E, LEONG Y K, et al Big data and machine learning driven bioprocessing: recent trends and critical analysis[J]. Bioresource Technology, 2023, 372: 128625
doi: 10.1016/j.biortech.2023.128625
[7]   RAMESH A V, LI X. Feasibility layer aided machine learning approach for day-ahead operations [J]. IEEE Transactions on Power Systems , 2024, 39(1): 1582–1593.
[8]   YANG Y, ZHAN D C, FAN Y, et al. Deep learning for fixed model reuse [C]// Thirty-First AAAI Conference on Artificial Intelligence . [S.l.]: AAAI, 2017, 31(1): 2831–2837.
[9]   DHAR T, DEY N, BORRA S, et al Challenges of deep learning in medical image analysis: improving explainability and trust[J]. IEEE Transactions on Technology and Society, 2023, 4 (1): 68- 75
doi: 10.1109/TTS.2023.3234203
[10]   BHATTI U A, TANG H, WU G, et al Deep learning with graph convolutional networks: an overview and latest applications in computational intelligence[J]. International Journal of Intelligent Systems, 2023, 2023: 1- 28
[11]   XIAO A, HUANG J, GUAN D, et al Unsupervised point cloud representation learning with deep neural networks: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45 (9): 11321- 11339
doi: 10.1109/TPAMI.2023.3262786
[12]   DONG Y, LIU Q, DU B, et al Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 2022, 31: 1559- 1572
doi: 10.1109/TIP.2022.3144017
[13]   CRUZ L, TOUS R, OTERO B Distributed training of deep neural networks with Spark: the MareNostrum experience[J]. Pattern Recognition Letters, 2019, 125 (7): 174- 178
[14]   KLEIN B, ZENTENO A C, JOSEPH D, et al Forecasting hospital-level COVID-19 admissions using real-time mobility data[J]. Communications Medicine, 2023, 3 (1): 25
doi: 10.1038/s43856-023-00253-5
[15]   DEAN J, CORRADO G S, MONGA R, et al. Large scale distributed deep networks [C]// Proceedings of the 25th International Conference on Neural Information Processing Systems . [S.l.]: Curran Associates Incorporated, 2012: 1223–1231.
[16]   CHALLU C, OLIVARES K G, ORESHKIN B N, et al. NHITS: neural hierarchical interpolation for time series forecasting [C]/ / Proceedings of the 37th AAAI Conference on Artificial Intelligence . [S.l.]: AAAI, 2023: 6989–6997.
[17]   WANG X, LI S, PUN C M, et al A parkinson’s auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024, 21 (4): 912- 923
doi: 10.1109/TCBB.2023.3246961
[18]   GIBSON A, NICHOLSON C, PATTERSON J, et al. DeepLearnning4J [EB/OL]. (2019–06–10)[2023–12–09]. https://github.com/deeplearning4j/deeplearning4j.
[19]   MALOHLAVA M, HAVA J, MEHTA N. Machine learning with sparkling water: H2O+Spark [EB/OL]. [2023–12–09]. https://h2o.ai/resources/booklet/machine-learning-with-sparkling-water-H2O-Spark.
[20]   JIA Y, SHELHAMER E, DONAHUE J, et al. Caffe: convolutional architecture for fast feature embedding [C]// Proceedings of the 22nd ACM International Conference on Multimedia . [S.l.]: ACM, 2014: 675–678.
[21]   GLAUNER P, STATE R. Deep learning on big data sets in the cloud with Apache Spark and Google TensorFlow [EB/OL]. (2016–12–06)[2023–12–09]. https://orbilu.uni.lu/bitstream/10993/28807/1/Deep%20Learning%20on%20Big%20Data%20Sets%20in%20the%20Cloud%20with%20Apache%20Spark%20and%20Google%20TensorFlow.pdf.
[22]   HE H, ZHAI H, LIU Q, et al. Using object-oriented big data analytics to reveal server performance dead zone [C]// 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC) . Atlanta: IEEE, 2016: 619–624.
[23]   KIM H, PARK J, JANG J, et al. DeepSpark: Spark-based deep learning supporting asynchronous updates and Caffe compatibility [EB/OL]. (2016–10–01)[2023–12–09]. https://arxiv.org/pdf/1602.08191v1.
[24]   NEUMANN D. DeepDist [EB/OL]. (2016–10–05)[2023–12–09]. http://deepdist.com//.
[25]   WANG J, CHENG L. DistDL: a distributed deep learning service schema with GPU accelerating [C]// Web Technologies and Applications . [S.l.]: Springer, 2015: 793–804.
[26]   MENG X, BRADLEY J, YAVUZ B, et al MLlib: machine learning in Apache Spark[J]. The Journal of Machine Learning Research, 2016, 17 (1): 1235- 1241
[27]   MIAO Q, LV Y, HUANG M, et al Parallel learning: overview and perspective for computational learning across Syn2Real and Sim2Real[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10 (3): 603- 631
doi: 10.1109/JAS.2023.123375
[28]   XIAO S, HUANG S, LIN Y, et al. Let the chart spark: Embedding semantic context into chart with text-to-image generative model [J]. IEEE Transactions on Visualization and Computer Graphics , 2024, 30(1): 284–294.
[29]   ULLAH F, SRIVASTAVA G, ULLAH S, et al NIDS-VSB: network intrusion detection system for VANET using Spark-based big data optimization and transfer learning[J]. IEEE Transactions on Consumer Electronics, 2024, 70 (1): 1798- 1809
doi: 10.1109/TCE.2023.3328320
[30]   TEERAPITTAYANON S, MCDANEL B, KUNG H T. Distributed deep neural networks over the cloud, the edge and end devices [C] // 2017 IEEE 37th International Conference on Distributed Computing Systems . Atlanta: IEEE, 2017: 328–339
[31]   TAKAM C A, SAMBA O, KOUANOU A T, et al Spark architecture for deep learning-based dose optimization in medical imaging[J]. Informatics in Medicine Unlocked, 2020, 19: 100335
doi: 10.1016/j.imu.2020.100335
[32]   WBA B, MS A, MDA B, et al RS-DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification[J]. Computers and Electronics in Agriculture, 2021, 182: 106014
doi: 10.1016/j.compag.2021.106014
[33]   JANG G, LEE J W, LEE J G, et al Distributed fine-tuning of CNNs for image retrieval on multiple mobile devices[J]. Pervasive and Mobile Computing, 2020, 64: 101134
doi: 10.1016/j.pmcj.2020.101134
[34]   CAO W, ZHANG C An effective parallel integrated neural network system for industrial data prediction[J]. Applied Soft Computing, 2021, 107: 107397
doi: 10.1016/j.asoc.2021.107397
[35]   VENKATESAN N J, DONG R S, NAM C S Nodule detection with convolutional neural network using Apache Spark and GPU frameworks[J]. Applied Sciences, 2021, 11 (6): 2838
doi: 10.3390/app11062838
[36]   YANG F, WANG H, FU J Improvement of recommendation algorithm based on collaborative deep learning and its parallelization on Spark[J]. Journal of Parallel and Distributed Computing, 2021, 148 (2): 58- 68
[37]   MARIR N, WANG H, FENG G, et al Distributed abnormal behavior detection approach based on deep belief network and ensemble SVM using Spark[J]. IEEE Access, 2018, 6: 59657- 59671
[38]   YIN C, PAN C, ZHANG P. Deep neural network combined with MapReduce for abnormal data mining and detection in cloud storage [J]. Journal of Ambient Intelligence and Humanized Computing , 2020: 1–12.
[39]   XU J, WANG J, QI Q, et al Effective scheduler for distributed DNN training based on MapReduce and GPU cluster[J]. Journal of Grid Computing, 2021, 19: 8
doi: 10.1007/s10723-021-09550-6
[40]   ZHU M, GUAN X, LI Z, et al sEMG-based lower limb motion prediction using CNN-LSTM with improved PCA optimization algorithm[J]. Journal of Bionic Engineering, 2023, 20: 612- 627
doi: 10.1007/s42235-022-00280-3
[41]   HU X, LI S, HUANG T, et al How simulation helps autonomous driving: a survey of sim2real, digital twins, and parallel intelligence[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9 (1): 593- 612
doi: 10.1109/TIV.2023.3312777
[42]   LV Z, CHEN D, CAO B, et al Secure deep learning in defense in deep-learning-as-a-service computing systems in digital twins[J]. IEEE Transactions on Computers, 2024, 73 (3): 656- 668
doi: 10.1109/TC.2021.3077687
[43]   WANG Z, LV C, WANG F Y A new era of intelligent vehicles and intelligent transportation systems: digital twins and parallel intelligence[J]. IEEE Transactions on Intelligent Vehicles, 2023, 4 (8): 2619- 2627
[44]   ZHU Z, LIN K, JAIN A K, et al Transfer learning in deep reinforcement learning: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 11 (45): 13344- 13362
[45]   CHEN X, YANG R, XUE Y, et al Deep transfer learning for bearing fault diagnosis: a systematic review since 2016[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3508221
[46]   CHEN H, LUO H, HUANG B, et al Transfer learning-motivated intelligent fault diagnosis designs: a survey, insights, and perspectives[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 35 (3): 2969- 2983
[47]   PANDYA S, SRIVASTAVA G, JHAVERI R, et al Federated learning for smart cities: a comprehensive survey[J]. Sustainable Energy Technologies and Assessments, 2023, 55: 102987
doi: 10.1016/j.seta.2022.102987
[48]   MYRZASHOVA R, ALSAMHI S H, SHVETSOV A V, et al Blockchain meets federated learning in healthcare: a systematic review with challenges and opportunities[J]. IEEE Internet of Things Journal, 2023, 16 (10): 14418- 14437
[49]   WEI K, LI J, MA C, et al Personalized federated learning with differential privacy and convergence guarantee[J]. IEEE Transactions on Information Forensics and Security, 2023, 6 (18): 4488- 4503
[50]   FRIEDRICH T, NEUMANN F. What’s hot in evolutionary computation [C]// Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence . [S.l.]: AAAI, 2017: 5046–5066.
[51]   AZIZ R M, MAHTO R, GOEL K, et al Modified genetic algorithm with deep learning for fraud transactions of Ethereum smart contract[J]. Applied Sciences, 2023, 13 (2): 697
doi: 10.3390/app13020697
[52]   ZOU H, JIN X, WEN M, et al Genetic algorithm aided compressed sensing detector for differential space-time media-based modulation[J]. IEEE Transactions on Vehicular Technology, 2023, 5 (72): 6874- 6878
[53]   BICHARA R M, ASADALLAH F A B, AWAD M, et al Quantum genetic algorithm for the design of miniaturized and reconfigurable IoT antennas[J]. IEEE Transactions on Antennas and Propagation, 2023, 71 (5): 3894- 3904
doi: 10.1109/TAP.2023.3245199
[54]   SALTO C, MINETTI G, ALBA E, et al Big optimization with genetic algorithms: Hadoop, Spark, and MPI[J]. Soft Computing, 2023, 27: 11469- 11484
doi: 10.1007/s00500-023-08301-x
[55]   HUANG J, HUANG X, CUI Y, et al. A Hadoop configuration optimization method based on middle platform business operation requirements [C]// 2023 IEEE International Conference on Sensors , Electronics and Computer Engineering . Jinzhou: IEEE, 2023: 1211–1216.
[56]   SEETHALAKSHMI V, GOVINDASAMY, AKILA V Real-coded multi-objective genetic algorithm with effective queuing model for efficient job scheduling in heterogeneous Hadoop environment[J]. Journal of King Saud University: Computer and Information Sciences, 2022, 6 (34): 3178- 3190
[57]   KALIA K, GUPTA N Analysis of hadoop MapReduce scheduling in heterogeneous environment[J]. Ain Shams Engineering Journal, 2021, 12 (1): 1101- 1110
doi: 10.1016/j.asej.2020.06.009
[58]   CHEN M, GUO Y, JIN Y, et al An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems[J]. Complex and Intelligent Systems, 2023, 9: 659- 675
doi: 10.1007/s40747-022-00824-4
[59]   JIANG S, ZOU J, YANG S, et al Evolutionary dynamic multi-objective optimisation: a survey[J]. ACM Computing Surveys, 2022, 55 (4): 76
[60]   SAHMOUD S, TOPCUOGLU H R. Sensor-based change detection schemes for dynamic multi-objective optimization problems [C]// 2016 IEEE Symposium Series on Computational Intelligence (SSCI) . Athens: IEEE, 2016: 1–8.
[61]   RICHTER H. Detecting change in dynamic fitness landscapes [C]// 2009 IEEE Congress on Evolutionary Computation . Trondheim: IEEE, 2009: 1613–1620.
[62]   JIANG S, YANG S A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization[J]. IEEE Transactions on evolutionary Computation, 2017, 21 (1): 65- 82
doi: 10.1109/TEVC.2016.2574621
[63]   YAN L, QI W, LIANG J, et al Inter-individual correlation and dimension based dual learning for dynamic multi-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2023, 6 (27): 1780- 1793
[64]   HU Y, ZHENG J, JIANG S, et al A Mahalanobis distance-based approach for dynamic multi-objective optimization with stochastic changes[J]. IEEE Transactions on Evolutionary Computation, 2024, 28 (1): 238- 251
doi: 10.1109/TEVC.2023.3253850
[65]   MORALES-HERNÁNDEZ A, VAN NIEUWENHUYSE I, ROJAS GONZALEZ S A survey on multi-objective hyperparameter optimization algorithms for machine learning[J]. Artificial Intelligence Review, 2023, 56: 8043- 8093
doi: 10.1007/s10462-022-10359-2
[66]   HASSAN E, SHAMS M Y, HIKAL N A, et al The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study[J]. Multimedia Tools and Applications, 2023, 82: 16591- 16633
doi: 10.1007/s11042-022-13820-0
[67]   ZHOU G, ZHAO L, ZHENG G, et al Joint multi-objective optimization for radio access network slicing using multi-agent deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2023, 72 (9): 11828- 11843
doi: 10.1109/TVT.2023.3268671
[68]   ZHANG X, YU G, JIN Y, et al An adaptive Gaussian process based manifold transfer learning to expensive dynamic multi-objective optimization[J]. Neurocomputing, 2023, 538: 126212
doi: 10.1016/j.neucom.2023.03.073
[69]   BARBA-GONZÁLEZ C, GARCÍA-NIETO J, NEBRO A J, et al jMetalSP: a framework for dynamic multi-objective big data optimization[J]. Applied Soft Computing, 2017, 69: 737- 748
[70]   BARBA-GONZÁLEZ C, NEBRO A J , BENÍTEZ-HIDALGO A, et al. On the design of a framework integrating an optimization engine with streaming technologies [J]. Future Generation Computer Systems , 2020, 107: 538–550.
[71]   NEBRO A J, RUIZ A B, BARBA-GONZÁLEZ C, et al InDM2: interactive dynamic multi-objective decision making using evolutionary algorithms[J]. Swarm and Evolutionary Computation, 2018, 40: 184- 195
doi: 10.1016/j.swevo.2018.02.004
[72]   ISMAYILOV G, TOPCUOGLU H R Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing[J]. Future Generation Computer Systems, 2020, 102: 307- 322
doi: 10.1016/j.future.2019.08.012
[73]   IBARRONDO R, GATTI G, SANZ M Quantum genetic algorithm with individuals in multiple registers[J]. IEEE Transactions on Evolutionary Computation, 2024, 28 (3): 788- 797
doi: 10.1109/TEVC.2023.3296780
[74]   DING W, LIN C T, CHEN S, et al Multiagent-consensus-MapReduce-based attribute reduction using co-evolutionary quantum PSO for big data applications[J]. Neurocomputing, 2018, 272: 136- 153
doi: 10.1016/j.neucom.2017.06.059
[75]   CAO B, ZHAO J, LV Z, et al A distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm for large-scale optimization[J]. IEEE Transactions on Industrial Informatics, 2017, 13 (4): 2030- 2038
doi: 10.1109/TII.2017.2676000
[76]   CAO B, ZHAO J, YANG P, et al 3-D multiobjective deployment of an industrial wireless sensor network for maritime applications utilizing a distributed parallel algorithm[J]. IEEE Transactions on Industrial Informatics, 2018, 14 (12): 5487- 5495
doi: 10.1109/TII.2018.2803758
[77]   CAO B, FAN S, ZHAO J, et al Quantum-enhanced multiobjective large-scale optimization via parallelism[J]. Swarm and Evolutionary Computation, 2020, 57: 100697
doi: 10.1016/j.swevo.2020.100697
[78]   QI S, WANG R, ZHANG T, et al Cooperative coevolutionary competition swarm optimizer with perturbation for high-dimensional multi-objective optimization[J]. Information Sciences, 2023, 644: 119253
doi: 10.1016/j.ins.2023.119253
[79]   刘建昌, 李飞, 王洪海, 等 进化高维多目标优化算法研究综述[J]. 控制与决策, 2018, 33 (5): 879- 887
LIU Jianchang, LI Fei, WANG Honghai, et al Survey on evolutionary many-objective optimization algorithms[J]. Control and Decision, 2018, 33 (5): 879- 887
[80]   HUSSAIN M M, FUJIMOTO N GPU-based parallel multi-objective particle swarm optimization for large swarms and high dimensional problems[J]. Parallel Computing, 2020, 92: 102589
doi: 10.1016/j.parco.2019.102589
[81]   ELMEILIGY M A, DESOUKY A I E, ELGHAMRAWY S M An efficient parallel indexing structure for multi-dimensional big data using Spark[J]. The Journal of Supercomputing, 2021, 77: 11187- 11214
doi: 10.1007/s11227-021-03718-3
[82]   SHE Q, ZHANG J, ZHOU Y, et al. Distributed high-dimension matrix operation optimization on Spark [C]// 2019 Eleventh International Conference on Advanced Computational Intelligence . Guilin: IEEE, 2019: 268–273.
[83]   ESCOBAR J J, ORTEGA J, GONZÁLEZ J, et al. Issues on GPU parallel implementation of evolutionary high-dimensional multi-objective feature selection [C]// Applications of Evolutionary Computation . [S.l.]: Springer, 2017: 773–788.
[84]   ZAPOTECAS-MARTÍNEZ S, COELLO C A C, AGUIRRE H E, et al A review of features and limitations of existing scalable multiobjective test suites[J]. IEEE Transactions on Evolutionary Computation, 2018, 23 (1): 130- 142
[85]   DEB K, THIELE L, LAUMANNS M, et al. Scalable test problems for evolutionary multiobjective optimization [M]// ABRAHAM A, JAIN L, GOLDBERG R. Evolutionary multiobjective optimization: theoretical advances and applications . [S.l.]: Springer, 2005: 105–145.
[86]   HUBAND S, HINGSTON P, BARONE L, et al A review of multiobjective test problems and a scalable test problem toolkit[J]. IEEE Transactions on Evolutionary Computation, 2006, 10 (5): 477- 506
doi: 10.1109/TEVC.2005.861417
[87]   EMMERICH M T M, DEUTZ A H. Test problems based on Lamé superspheres [C]// Evolutionary Multi-Criterion Optimization . [S.l.]: Springer, 2007: 922–936.
[88]   CHENG R, LI M, TIAN Y, et al A benchmark test suite for evolutionary many-objective optimization[J]. Complex and Intelligent Systems, 2017, 3: 67- 81
doi: 10.1007/s40747-017-0039-7
[89]   LIANG J J, QU B Y, GONG D W, et al. Problem definitions and evaluation criteria for the CEC special session on multimodal multiobjective optimization [R]. Zhengzhou: Zhengzhou University, 2019: 1–25.
[90]   YUE C, QU B, LIANG J A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems[J]. IEEE Transactions on Evolutionary Computation, 2018, 22 (5): 805- 817
doi: 10.1109/TEVC.2017.2754271
[91]   LIU Y, YEN G G, GONG D A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies[J]. IEEE Transactions on Evolutionary Computation, 2019, 23 (4): 660- 674
doi: 10.1109/TEVC.2018.2879406
[92]   TIAN Y, LIU R, ZHANG X, et al A multipopulation evolutionary algorithm for solving large-scale multimodal multiobjective optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2021, 25 (3): 405- 418
doi: 10.1109/TEVC.2020.3044711
[93]   LI H, ZHANG Q, DENG J Biased multiobjective optimization and decomposition algorithm[J]. IEEE Transactions on Cybernetics, 2017, 47 (1): 52- 66
doi: 10.1109/TCYB.2015.2507366
[94]   JAIN H, DEB K An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: handling constraints and extending to an adaptive approach[J]. IEEE Transactions on Evolutionary Computation, 2014, 18 (4): 602- 622
doi: 10.1109/TEVC.2013.2281534
[95]   DEB K, JAIN H An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18 (4): 577- 601
doi: 10.1109/TEVC.2013.2281535
[96]   CHENG R, JIN Y, OLHOFER M, et al Test problems for large-scale multiobjective and many-objective optimization[J]. IEEE Transactions on Cybernetics, 2017, 47 (12): 4108- 4121
doi: 10.1109/TCYB.2016.2600577
[97]   FARINA M, DEB K, AMATO P Dynamic multiobjective optimization problems: test cases, approximations, and applications[J]. IEEE Transactions on Evolutionary Computation, 2004, 8 (5): 425- 442
doi: 10.1109/TEVC.2004.831456
[98]   JIANG S, YANG S Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons[J]. IEEE Transactions on Cybernetics, 2017, 47 (1): 198- 211
doi: 10.1109/TCYB.2015.2510698
[99]   JIANG S, YANG S, YAO X, et al. Benchmark functions for the CEC’2018 competition on dynamic multiobjective optimization [R]. [S.l.]: Newcastle University, 2018: 1–18.
[100]   GONZALES-ZURITA O, ANDINO O L, CLAIRAND J M, et al PSO tuning of a second-order sliding mode controller for adjusting active standard power levels for smart inverter applications[J]. IEEE Transactions on Smart Grid, 2023, 6 (14): 4182- 4193
[101]   SUN X, JIN Z, XUE M, et al Adaptive ECMS with gear shift control by grey wolf optimization algorithm and neural network for plug-in hybrid electric buses[J]. IEEE Transactions on Industrial Electronics, 2024, 71 (1): 667- 677
doi: 10.1109/TIE.2023.3243304
[102]   LIU R, LIANG Z, WANG Z, et al Indoor visible light positioning based on improved whale optimization method with Min-Max algorithm[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 2504910
[103]   LIU Q, ZENG L, BILAL M, et al A multi-swarm PSO approach to large-scale task scheduling in a sustainable supply chain datacenter[J]. IEEE Transactions on Green Communications and Networking, 2023, 7 (4): 1667- 1677
doi: 10.1109/TGCN.2023.3283509
[104]   KENNEDY J, EBERHART R. Particle swarm optimization [C]// Proceedings of ICNN'95: International Conference on Neural Networks . Perth: IEEE, 1995, 4: 1942–1948.
[105]   PRADHAN A, BISOY S K, DAS A A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment[J]. Journal of King Saud University: Computer and Information Sciences, 2022, 34 (8): 4888- 4901
doi: 10.1016/j.jksuci.2021.01.003
[106]   AKMAN A, YENIAD M. Optimizing load balancing and task scheduling problems in cloud computing [C]// 2023 8th International Conference on Computer Science and Engineering (UBMK) . Burdur: IEEE, 2023: 119–124.
[107]   ALSAIDY S A, ABBOOD A D, SAHIB M A Heuristic initialization of PSO task scheduling algorithm in cloud computing[J]. Journal of King Saud University: Computer and Information Sciences, 2020, 34 (6): 2370- 2382
[108]   GAD A G, HOUSSEIN E H, ZHOU M C, et al Damping-assisted evolutionary swarm intelligence for industrial IoT task scheduling in cloud computing[J]. IEEE Internet of Things Journal, 2024, 11 (1): 1698- 1710
doi: 10.1109/JIOT.2023.3291367
[109]   LIAO C L, LEE S J, CHIOU Y S, et al Power consumption minimization by distributive particle swarm optimization for luminance control and its parallel implementations[J]. Expert Systems with Applications, 2018, 96: 479- 491
doi: 10.1016/j.eswa.2017.11.002
[110]   ZHUO Y, ZHANG T, DU F, et al A parallel particle swarm optimization algorithm based on GPU/CUDA[J]. Applied Soft Computing, 2023, 144: 110499
doi: 10.1016/j.asoc.2023.110499
[111]   WEN T, LIU H, LIN L, et al Multiswarm artificial bee colony algorithm based on Spark cloud computing platform for medical image registration[J]. Computer Methods and Programs in Biomedicine, 2020, 192: 105432
doi: 10.1016/j.cmpb.2020.105432
[112]   LAKSHMANAPRABU S K, SHANKAR K, RANI S S, et al An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: towards smart cities[J]. Journal of Cleaner Production, 2019, 217: 584- 593
doi: 10.1016/j.jclepro.2019.01.115
[113]   SKINDEROWICZ R Implementing a GPU-based parallel MAX-MIN ant system[J]. Future Generation Computer Systems, 2020, 106: 277- 295
doi: 10.1016/j.future.2020.01.011
[114]   BANCHHOR C, SRINIVASU N Integrating cuckoo search-grey wolf optimization and correlative naive Bayes classifier with map reduce model for big data classification[J]. Data and Knowledge Engineering, 2020, 127: 101788
doi: 10.1016/j.datak.2019.101788
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