Ant Agent-Based QoS Multicast Routing in Networks with Imprecise State Information

Author(s):  
Xin Yan ◽  
Layuan Li
2017 ◽  
Vol 5 (10) ◽  
pp. 26-37
Author(s):  
A. D. Devangavi ◽  
◽  
◽  
Rajendra Gupta

2012 ◽  
Vol 24 ◽  
pp. 1951-1958 ◽  
Author(s):  
Gu Shen-jun ◽  
Chen Jie ◽  
Tian Hao-cheng ◽  
Xu Ping ◽  
Yang Yun

Author(s):  
Toshiki Mori ◽  
Mark R. Cutkosky

Abstract In this paper, we propose an architecture in which engineering design agents interact with each other, exchange design information and keep track of state information to assist with collaborative design. We present an example involving CAD agents, for which each state corresponds to a particular design model. If a designer publishes a new design, the operation is recorded as a state transition that triggers action. Focusing on the history of design states and operations, we present a coordination algorithm that corresponds to the tracking of Pareto optimality. A prototype implementation is described, using a commercial 3D CAD system and agent interfaces written in Java.


2014 ◽  
Vol 971-973 ◽  
pp. 1803-1807
Author(s):  
Bin Fan

Because of the existing mobile Agent migration algorithm, is little or no consideration of the cases in NGI, such as imprecise network state information, imprecise user QoS requirements, so it's difficult to accurately plan the best migration path. In view of this, a flexible and Multi-constraint migration algorithm of Mobile Agent based on QoS is proposed. In the proposed algorithm, intervals are used to describe the ser QoS requirements and network link parameters; Probability density function, satisfaction functions and Objective function are adopted to overcome difficulties on accurately measuring network link parameter and exactly expressing on user QoS requirements; finally, the improved CGA is used to plan out the best migration path for mobile agent. Experimental results show that the algorithm can effectively avoid the occurrence of the premature convergence in traditional GA, and has faster convergence and higher stability in order to plan the best migration path for Mobile Agent. The algorithm reduces migration time of Mobile Agent, and greatly improves the efficiency of the system.


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