Name
Title | ||
|---|---|---|
Improvement of Two-swarm Cooperative Particle Swarm Optimization Using Immune Algorithms and Swarm Clustering |
Abstract | ||
|---|---|---|
Particle Swarm Optimization (PSO) is useful as a method for solving optimization problems with continuous value variables because the convergence speed of solution search is fast. PSO is a evolutionary computation method in which individuals (particles) with position and velocity information are placed in the search space and acts for the purpose of finding an optimal solution with sharing information with other particles. This study constructs a particle swarm optimization method introducing the immune algorithms to improve the search capability of each particle and perform solution search more efficiently. To verify the usefulness of the proposed method, some numerical experiments are performed in this study. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IWCIA47330.2019.8955042 | 2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) |
Keywords | DocType | ISSN |
Particle swarm optimization,evolutionary computation,immune algorithms,two-swarm optimization | Conference | 1883-3977 |
ISBN | Citations | PageRank |
978-1-7281-2430-8 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomohiro Hayashida | 1 | 29 | 11.56 |
| Ichiro Nishizaki | 2 | 443 | 42.37 |
| Shinya Sekizaki | 3 | 0 | 0.34 |
| Yuki Takamori | 4 | 0 | 0.34 |