Please wait a minute...
Frontiers of Electrical and Electronic Engineering

ISSN 2095-2732

ISSN 2095-2740(Online)

CN 10-1028/TM

Front. Electr. Electron. Eng.    2008, Vol. 3 Issue (4) : 394-398    https://doi.org/10.1007/s11460-008-0078-3
Research on food-chain algorithm and its parameters
YU Haifei1, WANG Dingwei2
1.School of Business Administration, Northeastern University; 2.Information Science and Engineering School, Northeastern University;
 Download: PDF(86 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract Based on the characteristics of colony emergence of artificial organisms, their dynamic interaction with the environment, and the food-chain crucial to the life system, the rules of local activities of artificial organisms at different levels are defined. The article proposes an artificial life-based algorithm, which is referred to as the food-chain algorithm. This algorithm optimizes computation by simulating the evolution of natural ecosystems and the information processing mechanism of natural organisms. The definition, idea and flow of the algorithm are introduced, and relevant rules on metabolic energy and change in the surroundings where artificial-life individuals live are depicted. Furthermore, key parameters of the algorithm are systematically analyzed. Test results show that the algorithm has quasi-life traits that include being autonomous, evolutionary, and self-adaptive. These traits are highly fit for optimization problems of life-like systems such as the location-allocation problem of a distribution network system.
Issue Date: 05 December 2008
 Cite this article:   
YU Haifei,WANG Dingwei. Research on food-chain algorithm and its parameters[J]. Front. Electr. Electron. Eng., 2008, 3(4): 394-398.
 URL:  
https://academic.hep.com.cn/fee/EN/10.1007/s11460-008-0078-3
https://academic.hep.com.cn/fee/EN/Y2008/V3/I4/394
1 Langton C G . Artificial Life. Redwood: Addison-Wesley, 1989, 1–47
2 Song J D, Yang B S, Choi B G, et al.. Optimum design of short journal bearings byenhanced artificial life optimization algorithm. Tribology International, 2005, 38(4): 403–412.
doi:10.1016/j.triboint.2003.10.008
3 Menczer F, Belew R K . Adaptive retrieval agents:Internalizing local context and scaling up to the web. Machine Learning, 2000, 39(2–3): 203–242.
doi:10.1023/A:1007653114902
4 Howard K R . Unjamming traffic with computers. ScientificAmerican, 1997, 277(4): 158–161
5 Rosin C D, Halliday R S, Hart W E, et al.. A comparison of global and local search methodsin drug docking. In: Proceedings of the7th International Conference on Genetic Algorithms. Orlando: Morgan Kaufmann, 1997, 221–228
6 Yu H F, Wang D W . Food-chain algorithm andits application to supply-chain operation management problems. Journal of Northeastern University (Natural Science), 2005, 26(1): 25–28 (in Chinese)
7 Zhang E D, Kang A L . Pursue and Escape: BehavioralEcology. Shanghai: Shanghai scientificand Technical Publishers, 2002 (in Chinese)
8 Dawkins R . TheSelfish Gene. Oxford: Oxford University Press, 1996
9 Assad A M, Packard N H . Emergent colonization inan artificial ecology. In: Toward a Practiceof Autonomous Systems: Proceedings of the First European Conferenceon Artificial Life. MIT Press, 1992, 143–152
10 Michalewicz Z . GeneticAlgorithms + Data Structures = Evolution Programs. Berlin: Springer-Verlag. 1992
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed