Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Particle swarm optimization (PSO) is a robust swarm intelligent technique inspired from birds flocking and fish schooling. Though many effective improvements have been proposed, however, the premature convergence is still its main problem. Because each particle's movement is a continuous process and can be modelled with differential equation groups, a new variant, particle swarm optimization with dynamic step length (PSO-DSL), with additional control coefficient- step length, is introduced. Then the absolute stability theory is introduced to analyze the stability character of the standard PSO, the theoretical result indicates the PSO with constant step length can not always be stable, this may be one of the reason for premature convergence. Simulation results show the PSO-DSL is effective.