A real multi-parent tri-hybrid evolutionary optimization method and its application in wind velocity estimation from wind profiler data

A real-coded multi-parent tri-hybrid evolutionary algorithm (EA) for problem optimization is presented. The hybrid EA algorithm combines the features of Simplex, stochastic relaxation (SR) and multi-parent EA reproduction in a model that encourages competition among the best individual solutions fro...

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Published in:Applied soft computing 2001-12, Vol.1 (3), p.225-235
Main Authors: Mendoza, Noel E, Chen, Yen-Wei, Nakao, Zensho, Adachi, Tatsuhiro, Masuda, Yoshihisa
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Language:English
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cited_by cdi_FETCH-LOGICAL-c197t-678bc9d4457664bc931c54c357277348c63cf2b9b3e051ee720aea687a9200353
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container_title Applied soft computing
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creator Mendoza, Noel E
Chen, Yen-Wei
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description A real-coded multi-parent tri-hybrid evolutionary algorithm (EA) for problem optimization is presented. The hybrid EA algorithm combines the features of Simplex, stochastic relaxation (SR) and multi-parent EA reproduction in a model that encourages competition among the best individual solutions from various operations. Its strength has been evaluated using standard test functions and shown to do better than other methods. The algorithm's ability to handle noise is evident when applied to experiments involving resolution of overlapping wind profiler (WP) data. Results obtained using raw data closely matched those obtained with data preprocessed by a low-pass FFT filter. Resolution of low- speed wind and clutter signals in various degrees of overlap is made possible, thereby allowing the determination of wind velocity and variance to be executed with ease.
doi_str_mv 10.1016/S1568-4946(01)00021-7
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title A real multi-parent tri-hybrid evolutionary optimization method and its application in wind velocity estimation from wind profiler data
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