| Title | Application research and improvement of particle swarm optimization algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Cai, L., Hou, Y., Zhao, Y., Wang, J. |
| Conference Name | 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) |
| Keywords | Application research, composability, compositionality, convergence, Heuristic algorithms, Improvement, local optimization, multiobjective optimization, Optimization, particle swarm optimisation, particle swarm optimization, particle swarm optimization algorithm, PSO, pubcrawl, Sociology, Standards, Statistics, swarm intelligence, Swarm intelligence algorithm |
| Abstract | Particle swarm optimization (PSO), as a kind of swarm intelligence algorithm, has the advantages of simple algorithm principle, less programmable parameters and easy programming. Many scholars have applied particle swarm optimization (PSO) to various fields through learning it, and successfully solved linear problems, nonlinear problems, multiobjective optimization and other problems. However, the algorithm also has obvious problems in solving problems, such as slow convergence speed, too early maturity, falling into local optimization in advance, etc., which makes the convergence speed slow, search the optimal value accuracy is not high, and the optimization effect is not ideal. Therefore, many scholars have improved the particle swarm optimization algorithm. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future. |
| DOI | 10.1109/ICPICS50287.2020.9202023 |
| Citation Key | cai_application_2020 |
Application research and improvement of particle swarm optimization algorithm