A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization

Sensors

Sayantani Bhattacharya¹  Amit Konar¹  Swagatam Das¹ 

[1] Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India; E-Mails:

关键词: particle swarm dynamics;    metaheuristics;    continuous function optimization;    stability;    convergence;    lyapunov stability theorem;   

DOI  :  10.3390/s91209977

来源: mdpi

【 摘 要 】

The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy.

【 授权许可】

CC BY   
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.