A model of multi-agent system based on immune evolution

Author

Chen, Guangzhu ; Li, Zhishu ; Yuan, Daohua ; Nimazhashi

Author_Institution

Sch. of Comput., Sichuan Univ., Chengdu, China

Volume

1

fYear

2005

fDate

28-30 March 2005

Firstpage

53

Abstract

Increasingly, multi-agent systems are being designed for a variety of complex, dynamic and uncertain domains. An agent in such domains often needs to learn or evolve to adjust its behaviors, or negotiate with other agents, and so on. Immune cells are the key components of the human immune system, their evolutions in the whole lifecycle directly decide the immune system´s performance. In this paper, a model of immune evolution multi-agent system (IEMAS) is introduced based on immune cells´ evolution principles. In such a system agents can evolve to adapt to the complex, dynamic and uncertain environment. Also, the formal model of IEMAS is described, and then the mature immune cell evolution algorithm and the memory immune cell evolution algorithm of IEMAS are presented. Finally, the run of IEMAS applied to the task allocation in multi-agent systems are presented and displays the efficiency of IEMAS in the complex, dynamic and uncertain environment.

Keywords

biology computing; cellular biophysics; evolutionary computation; multi-agent systems; uncertainty handling; IEMAS; formal model; human immune system; immune evolution multiagent system; mature immune cell evolution algorithm; memory immune cell evolution algorithm; system agent; task allocation; uncertain environment; Bones; Convergence; Displays; Electrical engineering; Evolutionary computation; Humans; Immune system; Multiagent systems; Pattern recognition; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on

ISSN

1550-445X

Print_ISBN

0-7695-2249-1

Type

conf

DOI

10.1109/AINA.2005.36

Filename

1423470