In particle swarm optimization (PSO) algorithms there is a delicate balance to maintain between exploitation (local search) and exploration (global search). When facing multimodal functions, the standard PSO algorithm often converges to a local minimum quickly, missing better opportunities. Methods such as non-global best neighborhoods increase exploration, but at the expense of slowing the convergence of the whole PSO algorithm. In this paper, we propose a new method to extend PSO, velocity-based reinitialization (VBR). VBR is both simple to implement and effective at enhancing many different PSO algorithms from the literature. In VBR-PSO, the velocities of the particles are monitored throughout the evolution, and when the median velocity of the swarm particles has dropped below a threshold, the whole swarm is reinitialized. Through VBR, the problem of premature convergence is alleviated; VBR-PSO focuses on one minimum at a time. In our experiments, we apply VBR to the global-best, local best, and von Neumann neighborhood PSO algorithms. Results are presented using the standard benchmark functions from the PSO literature. VBR enhanced PSO yields improved results on the multimodal benchmark functions for all PSO algorithms investigated in this study.
|Number of pages||9|
|Journal||Transactions of the Japanese Society for Artificial Intelligence|
|Publication status||Published - 2008|
- Particle swarm optimization
- Velocity-based reinitialization
ASJC Scopus subject areas
- Artificial Intelligence