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  • 标题:Hybrid Latin-Hyper-Cube-Hill-Climbing Method for Optimizing: Experimental Testing
  • 本地全文:下载
  • 作者:Calista Elysia ; Michelle Hartanto ; Ditdit Nugeraha Utama
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2019
  • 卷号:10
  • 期号:9
  • 页码:421-425
  • 出版社:Science and Information Society (SAI)
  • 摘要:A noticeable objective of this work is to experiment and test an optimization problem through comparing hillclimbing method with a hybrid method combining hill-climbing and Latin-hyper-cube. These two methods are going to be tested operating the same data-set in order to get the comparison result for both methods. The result shows that the hybrid model has a better performance than hill-climbing. Based on the number of global optimum value occurrence, the hybrid model outperformed 7.6% better than hill-climbing, and produced more stable average global optimum value. However, the model has a little longer running time due to a genuine characteristic of the model itself.
  • 关键词:Hill-Climbing; Latin-Hyper-Cube; Optimization
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