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.
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Xu, Y., Jiang, W. (2009). A Novel Hybrid Evolution Algorithm Based on Agent Behavior and Paradigm Learning. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_53
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DOI: https://doi.org/10.1007/978-3-642-01510-6_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
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