Daqing Yi
Michael A. Goodrich
ABSTRACT
This paper examines the dynamics of particle swarm optimization (PSO) by modeling PSO as a feedback cascade system and then applying input-to-state stability analysis. Using a feedback cascade system model we can include the effects of the global-best and personal-best values more directly in the model of the dynamics. Thus in contrast to previous study of PSO dynamics, the input-to-state stability property used here allows for the analysis of PSO both before and at stagnation. In addition, the use of input-to-state stability allows this analysis to preserve random terms which were heretofore simplified to constants. This analysis is important because it can inform the setting of PSO parameters and better characterize the nature of PSO as a dynamic system. This work also illuminates the way in which the personal-best and the global-best updates influence the bound on the particle's position and hence, how the algorithm exploits and explores the fitness landscape as a function of the personal best and global best.
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Index Terms
- Input-to-State Stability Analysis on Particle Swarm Optimization
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