- Tytuł:
- Repulsive self - adaptive acceleration particle swarm optimization approach
- Autorzy:
- Ludwig, S. A.
- Powiązania:
- https://bibliotekanauki.pl/articles/91874.pdf
- Data publikacji:
- 2014
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
adaptive Particle Swarm Optimization
adaptive PSO
optimization
Repulsive Self-adaptive Acceleration PSO
RSAPSO
velocity weights
optimal solution of the problem
function evaluations - Opis:
- Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 189-204
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł