A Novel Hybrid Evolution Algorithm Based on Agent Behavior and Paradigm Learning

Authors:
Yuhui Xu;Weijin Jiang
Affiliations:
School of Art Layout, Hunan University of Commerce, Changsha, China 410205;School of Computer and Electronic Engineering, Hunan University of Commerce, Changsha, China 410205
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

Distribution system optimal planning has vital significance, but there isn't efficient and practical algorithm at Traditional genetic algorithm has a poor expressive power for complicated problem because of the restriction of its norm mode, which limits the application fields of genetic algorithm. This paper adapts the idea of "Ethogenetics" reference, and presents a new type of genetic algorithm based on Agent behavior and paradigm learning. Unlike the based creating mode of feasible solution in traditional genetic algorithm, a feasible solution is created by ~ series o! ye behaviors of Agent based on knowledge in the new genetic algorithm. To adapt the new creating mode of feasible the traditional mechanism of evolution optimization based on Darwinism is abandoned and the mechanism of learning' is adopted to realize the evolution optimization. At last, an example distribution network is optimized by Ilene tic algorithm and traditional genetic algorithm respectively. The comparative result proves the new genetic I has higher expressive power, computing efficiency, convergent stability and extendable capability.