A weighted adaptive-velocity self-organizing model and its high-speed performance

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• 作者：Miaomiao Zhao^aAuthor Vitae ; Housheng Su^a ; ^{houshengsu@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Miaomiao Wang^aAuthor Vitae ; Lei Wang^bAuthor Vitae ; Michael Z.Q. Chen^cAuthor Vitae
• 关键词：Self-organization ; Collective behavior ; Adaptive velocity ; Weight ; High speed ; Quick convergence ; Energy efficiency
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 December 2016
• 年：2016
• 卷：216
• 期：Complete
• 页码：402-408
• 全文大小：2410 K

文摘

This paper proposed an improved adaptive-velocity self-organizing model as a prospective candidate in order to enhance high-speed convergence and accelerate convergence. Moreover, weights are assigned to reinforce convergence under super high-speed circumstances. Convergence performance is assessed via group polarization, convergence ratio and convergent time. As verified by numerical experiments, superior high-speed performance and fast convergence are achieved simultaneously in the improved adaptive-velocity model. The weighted adaptive model prominently improved super high-speed performance with short convergent time and low energy consumption. Then, the parameter space of the weighted adaptive flocking model is investigated.