کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1706968 | 1012487 | 2009 | 14 صفحه PDF | دانلود رایگان |
A new stochastic method named hybrid generalized extremal optimization (HGEO) is proposed in this paper. It combines genetic algorithms (GAs) and generalized extremal optimization (GEO). In order to extend GEO’s mutation operator to accelerate convergence speed and be easily incorporated into HGEO, the real coded GEO is first developed to population-base GEO (PGEO), and then incorporated into the HGEO in the paper. Constraints consideration for using the HGEO and the effects of related operators are also investigated. Finally, the performance of the HGEO is fully investigated compared with other related algorithms to find the optimal power consumption for the semi-track air-cushion vehicle (STACV). The results show that the HGEO has better performance than GAs or other related simpler algorithms.
Journal: Applied Mathematical Modelling - Volume 33, Issue 6, June 2009, Pages 2831–2844