Improved harmony search with general iteration models for engineering design optimization problems

Harmony search (HS) algorithm has a strong exploration and exploitation capability based on its unique improvisation. However, little research has been done on its improvisation mechanism. This paper offers a detailed discussion on the HS improvisation mechanism, which aims to state that the improvisation is a generic search framework. To improve the performance of HS, global learning strategy is designed to enhance the global search capability, and modified random selection is used to reduce the possibility of falling into local optimum. Moreover, a new improvement perspective such as the adjustment of iteration model is presented in this paper. Different iteration models such as dimension-to-dimension mode, stochastic multi-dimensional mode, vector mode and matrix mode to explore the optimization potential of HS algorithm are employed. Combining the improved operations, parameter adjustments and the four iteration models, four improved HS variants are proposed to analyze the effectiveness of iteration model on HS algorithm. Experimental results demonstrate the proposed HS algorithms can yield significant improved performance. Overall, the paper shows that the HS improvisation framework has a good extensibility and the iteration model has significant impact on the performance of HS.