Improvement of Two-swarm Cooperative Particle Swarm Optimization Using Immune Algorithms and Swarm Clustering
| Autor: | Yuki Takamori, Shinya Sekizaki, Ichiro Nishizaki, Tomohiro Hayashida |
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| Rok vydání: | 2019 |
| Předmět: |
Optimization problem
Computer science ComputingMethodologies_MISCELLANEOUS Computer Science::Neural and Evolutionary Computation 05 social sciences 050301 education Swarm behaviour Particle swarm optimization 02 engineering and technology Evolutionary computation Position (vector) Convergence (routing) 0202 electrical engineering electronic engineering information engineering Particle 020201 artificial intelligence & image processing Cluster analysis 0503 education Algorithm |
| Zdroj: | IWCIA |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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