Hybrid Latin-Hyper-Cube-Hill-Climbing Method for Optimizing: Experimental Testing

Abstract

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.