Harmony Search with Dynamic Dimensional-Reduction Adjustment Strategy for Large-Scale Absolute Value Equation

Harmony Search (HS) algorithm is a meta-heuristic optimization method. Although several variants of the HS algorithm have been proposed, their effectiveness in dealing with diverse problems is still unsatisfactory, especially for larger-scale problems due to these variants mainly depend on the selection of different parameters. In this paper, an improved harmony search algorithm with a dynamic dimensional-reduction adjustment strategy (DIHS) is proposed to solve large-scale absolute value equation. Numerical experiments show that the DIHS algorithm can avoid precocious phenomena and has a higher rate of convergence than the other HS-variants algorithms.