Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13591
Title: Particle swarm optimization in multi-agents cooperation applications
Authors: XU LIANG
Keywords: Multi-Agents Cooperation, Particle Swarm Optimization, Neural Networks, Genetic Algorithm, Fully Autonomous Multi-Agents Cooperation, M2PSO
Issue Date: 10-Dec-2003
Source: XU LIANG (2003-12-10). Particle swarm optimization in multi-agents cooperation applications. ScholarBank@NUS Repository.
Abstract: Rapid progress has been achieved in the development of group intelligence. However, most of the present group intelligence is external-driven. This thesis presents a Fully Automatic Multi-Agents Cooperation (FAMAC) strategy that is inner-motivated and enables multi-agents to perform autonomic cooperation independently of external instruction. To further improve the performance of FAMAC, a Multi-level--Multi-step PSO-Network ( PSO-Network) is put forward to replace Neural Networks in the Intelligent Learning and Reasoning Unit (ILRU). Through simulation, it is shown that the inner-motivated group intelligence is achievable and is efficient in prompting the capacity of multi-agents as a united team.
URI: http://scholarbank.nus.edu.sg/handle/10635/13591
Appears in Collections:Master's Theses (Open)

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