Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (8): 1409-1426.DOI: 10.3778/j.issn.1673-9418.1908037

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Stochastic Single-Dimensional Mutated Particle Swarm Optimization with Dynamic Subspace

DENG Zhicheng, SUN Hui, ZHAO Jia, WANG Hui   

  1. 1. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    2. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330099, China
    3. National-Local Joint Engineering Laboratory of Water Engineering Safety and Effective Utilization of Resources in Poyang Lake Area, Nanchang 330099, China
  • Online:2020-08-01 Published:2020-08-07



  1. 1. 南昌工程学院 信息工程学院,南昌 330099
    2. 江西省水信息协同感知与智能处理重点实验室,南昌 330099
    3. 鄱阳湖流域水工程安全与资源高效利用国家地方联合工程实验室,南昌 330099


The traditional particle swarm optimization algorithm adopts the overall dimension updating strategy, and the particle??s fitness value deteriorates frequently because the optimal solution is not reached in a certain dimension or a few dimensions. Aiming at this problem, a stochastic single-dimensional mutated particle swarm optimization algorithm with dynamic subspace is proposed. The dynamic subspace is constructed from the high-quality particle's dimension, and one-dimension different from the subspace is randomly selected to mutate. The subspace size changes dynamically. In the early stage, most of the dimensions are used to form the subspace, which increases the diversity of the mutated dimension. Later, a few dimensions are selected to form the subspace, which enhances the ability of the particle to search fine. At the same time, according to Pareto's law, the population explores the new solution space region within the first 20% iterations, and performs an effective balanced search within 80% of the iterations in the later period to accelerate the population convergence speed. Simulation experiments are carried out in 30, 50 and 100 dimensions using multi-type benchmark functions. The results show that the proposed algorithm is not only superior to state of the art particle swarm optimization algorithms, but also the artificial bee colony and firefly algorithm in convergence speed and precision.

Key words: particle swarm optimization (PSO), single-dimensional mutation, dynamic subspace, Pareto's law



关键词: 粒子群优化算法(PSO), 单维变异, 动态子空间, Pareto定律