Multi-Task Coalition Generation of Multi-Agent System with Limited Resource

Ning Qiang; Feng Ju Kang

doi:10.4028/www.scientific.net/AMR.971-973.1655

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973Multi-Task Coalition Generation of Multi-Agent...

Multi-Task Coalition Generation of Multi-Agent System with Limited Resource

Abstract:

A new fitness function is introduced in order to maximize the number of task served by the multi-agent system (MAS) with limited resource, while the tasks information remains unknown until the system found them one by one. The new fitness function not only considers to maximize the profit of the system which can be seen as to maximize the remaining resource of the system in the case of the MAS with limited resource, but also takes the balance of remaining resource in to account and it can makes a compromise between them. This paper uses an improved discrete particle swarm optimization to optimize the coalition of MAS. In order to improve the performance of the algorithm we redefine the particle velocity and position update formula. The simulation results show the effectiveness and superiority of the proposed fitness function and optimization algorithm.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1655-1658

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1655

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Online since:

June 2014

Authors:

Ning Qiang*, Feng Ju Kang

Keywords:

Coalition Generation, Limited Resource, Multi-Agent System (MAS), Particle Swarm Optimization (PSO)

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* - Corresponding Author

References

[1] Shehory O, Kraus S. Task allocation via coalition formation among autonomous agents[C]. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence-95. Los Angeles, CA, USA: MorganKaufmann Publishers, 1995. 655- 661.

Google Scholar

[2] Kennedy J, Eberhart R C. Particle swarm optimization [A]. Proc IEEE Conference on Neural Networks [C]. Piscataway, NJ, 1995(4), 1942- (1948).

Google Scholar

[3] Liu jianhua, Fan Xiaoping, Qu zhihua. An Improved Particle Swarm Optimization with Mutation Base on Similarity[C]. Third International Conference on Natural Computation, ICNC200. Haikou, China, 2007. 8. 24-27, 824-828.

DOI: 10.1109/icnc.2007.223

Google Scholar

[4] Liu Huiyi, Chen Jinfeng. Multi-robot Cooperation Coalition Formation Based on Genetic Algorithm[C]. Proceedings of the 5th International Conference on Machine Learning and Cybernetics. [S. l. ]: IEEE Press, 2006, 85-88.

DOI: 10.1109/icmlc.2006.258820

Google Scholar