NDLI: Application of an Ant Colony Algorithm based on complex networks in migration of mobile agents

Content Provider	IEEE Xplore Digital Library
Author	Ju Ze-wang Wang Hong
Copyright Year	2009
Description	Author affiliation: Finance and Economics Department, Weifang College of Education, China, 262500 (Ju Ze-wang) \|\| Institute of Information Science and Engineering, ShanDong Normal University, China, 250014 (Wang Hong)
Abstract	One of the main problems in mobile agent migration is planning out an optimal migration path according to the agent tasks and other restrictions when agents migrate to several other hosts. The Ant Colony Algorithm, which has the characteristic of parallelism, positive feedback and heuristic search, is a new evolutionary algorithm and is extremely suitable to the mobile agent migration problem. But it still has some shortcomings such as slowly speed and stagnation behavior. Complex networks theory is a new kind of theory, which finds that some practical networks have new characters. In order to describe these new characters, some new characteristic measures are introduced, one of which is the node's “degree”. Based on the classical Ant Algorithm, the parameter “degree” is added into the state transfer rules of the Ant Algorithm and a self-adaptive pheromone evaporation rate is proposed, which can accelerate the convergence rate and improve the ability of searching an optimum solution. This improved Ant Colony Algorithm is used to plan out an optimal migration path of mobile agents. The results of contrastive experiments show that the algorithm is superior to other related methods both on the quality of solution and on the convergence rate.
Starting Page	821
Ending Page	825
File Size	77013
Page Count	5
File Format	PDF
ISBN	9781424439287
DOI	10.1109/ITIME.2009.5236310
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Finance Evolutionary computation Educational institutions Path planning complex networks Environmental economics Information science Ant Colony Algorithm Mobile agents Feedback mobile agent Complex networks Acceleration
Content Type	Text
Resource Type	Article

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