کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
480893 | 1446148 | 2009 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms](/preview/png/480893.png)
In this paper a new heuristic hybrid technique for bound-constrained global optimization is proposed. We developed iterative algorithm called GLPτS that uses genetic algorithms, LPτ low-discrepancy sequences of points and heuristic rules to find regions of attraction when searching a global minimum of an objective function. Subsequently Nelder–Mead Simplex local search technique is used to refine the solution. The combination of the three techniques (Genetic algorithms, LPτO Low-discrepancy search and Simplex search) provides a powerful hybrid heuristic optimization method which is tested on a number of benchmark multimodal functions with 10–150 dimensions, and the method properties – applicability, convergence, consistency and stability are discussed in detail.
Journal: European Journal of Operational Research - Volume 196, Issue 2, 16 July 2009, Pages 413–422