Name
Abstract | ||
|---|---|---|
Particle swarm optimization is a heuristic random optimization algorithm, in which each particle pursues its own optimal particle and the global optimal position. In the random search process of particle swarm optimization, these particles will eventually converge to the population optimal particle. The algorithm has the shortcoming of premature convergence because it is a stochastic approximate optimization algorithm. In this paper, the concept of random observation points is defined. Based on the standard PSO algorithm, a particle swarm optimization algorithm based on random observation points (OBPSO) which replaces the particles in the original particle swarm optimization by setting random observation points in each algorithm iteration is proposed. By comparing the experimental results of this algorithm with those of standard PSO and other variants on benchmark functions, OBPSO algorithm has the advantages of avoiding premature convergence and improving the global search ability of standard PSO algorithm, which effectively improves the search performance of standard PSO algorithm. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CSCloud-EdgeCom49738.2020.00031 | 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) |
Keywords | DocType | ISBN |
Observation points,particle swarm optimization,global optimum,local optimum,multimodal function,unimodal function | Conference | 978-1-7281-6551-6 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengsheng Xu | 1 | 0 | 0.34 |
| Yu-Lin He | 2 | 90 | 6.31 |
| Joshua Zhexue Huang | 3 | 1365 | 82.64 |