Parallel Particle Swarm Optimization Using Apache Beam

被引:3
|
作者
Liu, Jie [1 ]
Zhu, Tao [1 ]
Zhang, Yang [2 ]
Liu, Zhenyu [1 ]
机构
[1] Univ South China, Comp Sch, Hengyang 421001, Peoples R China
[2] Natl Univ Def Technol, Sci & Technol Parallel & Distributed Proc Lab PDL, Changsha 410073, Peoples R China
基金
中国国家自然科学基金;
关键词
parallel particle swarm optimization; Apache Beam; MapReduce; swarm intelligence; big data; PSO;
D O I
10.3390/info13030119
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The majority of complex research problems can be formulated as optimization problems. Particle Swarm Optimization (PSO) algorithm is very effective in solving optimization problems because of its robustness, simplicity, and global search capabilities. Since the computational cost of these problems is usually high, it has been necessary to develop optimization algorithms with parallelization. With the advent of big-data technology, such problems can be solved by distributed parallel computing. In previous related work, MapReduce (a programming model that implements a distributed parallel approach to processing and producing large datasets on a cluster) has been used to parallelize the PSO algorithm, but frequent file reads and writes make the execution time of MRPSO very long. We propose Apache Beam particle swarm optimization (BPSO), which uses Apache Beam parallel programming model. In the experiment, we compared BPSO and PSO based on MapReduce (MRPSO) on four benchmark functions by changing the number of particles and optimizing the dimensions of the problem. The experimental results show that, as the number of particles increases, MRPSO remains largely constant when the number of particles is small (<1000), while the time required for algorithm execution increases rapidly when the number of particles exceeds a certain amount (>1000), while BPSO grows slowly and tends to yield better results than MRPSO. As the dimensionality of the optimization problem increases, BPSO can take half the time of MRPSO and obtain better results than it does. MRPSO requires more execution time than BPSO, as the problem complexity varies, but both MRPSO and BPSO are not very sensitive to problem complexity. All program code and input data are uploaded to GitHub.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Parallel Test Scheduling based on Particle Swarm Optimization
    Li, Zhongwen
    Huang, Xiangmiao
    PROCEEDINGS OF THE 2013 THE INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY AND INFORMATION SYSTEM (ICETIS 2013), 2013, 65 : 736 - 739
  • [42] Parallel quantum-behaved particle swarm optimization
    Na Tian
    Choi-Hong Lai
    International Journal of Machine Learning and Cybernetics, 2014, 5 : 309 - 318
  • [43] Resemblance of Biological Particle Swarm Optimization and Particle Swarm Optimization for CBFR by using NN
    Dubey, Deepika
    Tomar, Geetam Singh
    MATERIALS TODAY-PROCEEDINGS, 2020, 29 : 408 - 419
  • [44] Beam Steering of Time Modulated Antenna Arrays Using Particle Swarm Optimization
    Abusitta, M. M.
    Abd-Alhameed, R. A.
    Elfergani, I. T. E.
    Adebola, A. D.
    Excell, P. S.
    PIERS 2011 MARRAKESH: PROGRESS IN ELECTROMAGNETICS RESEARCH SYMPOSIUM, 2011, : 448 - 452
  • [45] Reactive Power Optimization Based on Parallel Immune Particle Swarm Optimization
    Yuan, Guili
    Zhu, Lei
    Yu, Tong
    JOURNAL OF COMPUTERS, 2014, 9 (09) : 2198 - 2205
  • [46] DESIGN OF A LINEAR ARRAY OF HALF WAVE PARALLEL DIPOLES USING PARTICLE SWARM OPTIMIZATION
    Rattan, M.
    Patterh, M. S.
    Sohi, B. S.
    PROGRESS IN ELECTROMAGNETICS RESEARCH M, 2008, 2 (131-139): : 131 - 139
  • [47] Optimal Design of a 6-DOF Parallel Manipulator Using Particle Swarm Optimization
    Shirazi, A. R.
    Fakhrabadi, M. M. S.
    Ghanbari, A.
    ADVANCED ROBOTICS, 2012, 26 (13) : 1419 - 1441
  • [48] Minimization of Lennard-Jones Potential Using Parallel Particle Swarm Optimization Algorithm
    Deep, Kusum
    Arya, Madhuri
    CONTEMPORARY COMPUTING, PT 1, 2010, 94 : 131 - 140
  • [49] Massively parallel inverse rendering using Multi-objective Particle Swarm Optimization
    Nagano, Koki
    Collins, Thomas
    Chen, Chi-An
    Nakano, Aiichiro
    JOURNAL OF VISUALIZATION, 2017, 20 (02) : 195 - 204
  • [50] Controller Design for Nonlinear Descriptor Systems using Parallel Asynchronous Particle Swarm Optimization
    Tsuge, Yuta
    Narikiyo, Tatsuo
    Kawanishi, Michihiro
    2014 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII), 2014, : 263 - 268