An Optimized Solution for Multi-agent Coordination Using Integrated GA-Fuzzy Approach in Rescue Simulation Environment
Agents’ coordination, communication and information sharing have been always open problems in multi-agent research fields. In complex rescue simulation environment, each agent observes a large amount of data which exponentially increases through the time while the capacity of messages in which agents’ information is shared with others and also the time needed to process the data is limited. Apparently, having a suitable coordination strategy and data sharing system will lead to better overall performance. Therefore, agents should select and spread out the most useful data among their observed information in order to achieve better coordination. In this paper, we propose an unique method based on integration of Genetic Algorithms and Fuzzy Logic theory to decide which part of data is more important to share in different situations. We also advise a new iterative method in order to obtain admissible experimental results in rescue simulation environment which is a good measurement for our research.
KeywordsMembership Function MultiAgent System Fuzzy Logic System Message Type Training Situation
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