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Information processing letters, 2008-03, Vol.105 (6), p.231-235
Ort / Verlag
Amsterdam: Elsevier B.V
Erscheinungsjahr
2008
Link zum Volltext
Quelle
ScienceDirect Journals (5 years ago - present)
Beschreibungen/Notizen
In particle swarm optimization (PSO) literatures, the published social coefficient settings are all centralized control manner aiming to increase the search density around the swarm memory. However, few concerns the useful information inside the particles' memories. Thus, to improve the convergence speed, we propose a new setting about social coefficient by introducing an explicit selection pressure, in which each particle decides its search direction toward the personal memory or swarm memory. Due to different adaptation, this setting adopts a dispersed manner associated with its adaptive ability. Furthermore, a mutation strategy is designed to avoid premature convergence. Simulation results show the proposed strategy is effective and efficient.