This work introduces a novel technique for dynamic particle swarm optimization (DPSO) using adaptive critic designs. The adaptation between global and local search in an optimization algorithm is critical for optimization problems especially in a dynamically changing environment or process over time. The inertia weight in particle swarm optimization (PSO) is dynamically adjusted in this paper in order to provide a nonlinear search capability for the PSO algorithm. Results on benchmark functions in the literature are provided.
G. K. Venayagamoorthy, "Adaptive Critics for Dynamic Particle Swarm Optimization," Proceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004, Institute of Electrical and Electronics Engineers (IEEE), Jan 2004.
The definitive version is available at https://doi.org/10.1109/ISIC.2004.1387713
2004 IEEE International Symposium on Intelligent Control, 2004
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Critics Design; Dynamic Particle Swarm Optimization; Global Search; Local Search; Optimisation; Search Problems
Article - Conference proceedings
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2004