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浙江大学学报(工学版)
浙江大学学报(工学版)  2025, Vol. 59 Issue (1): 27-38    DOI: 10.3785/j.issn.1008-973X.2025.01.003
计算机与控制工程     
基于并行计算的计算智能综述
吴菲(),陈嘉诚,王万良*()
浙江工业大学 计算机科学与技术学院，浙江 杭州 310023
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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摘要：

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    
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
收稿日期: 2023-12-10 出版日期: 2025-01-18
CLC:  TP 301  
基金资助: 国家自然科学基金资助项目（61873240）.
通讯作者: 王万良     E-mail: wfmook@163.com;zjutwwl@zjut.edu.cn
作者简介: 吴菲（1995—），女，博士生，从事大数据、计算智能研究. orcid.org/0000-0002-8483-5392. E-mail：wfmook@163.com
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吴菲,陈嘉诚,王万良. 基于并行计算的计算智能综述[J]. 浙江大学学报(工学版), 2025, 59(1): 27-38.

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.

链接本文:

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

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
表 1  并行深度学习框架
图 1  群智能优化算法的发展时间线
图 2  并行群智能的架构图
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
表 2  大数据并行框架的性能对比
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