Harmony Search (HS), one of the most popular metaheuristic optimization algorithms, is inspired by musical improvisation process. HS operators mimic music player’s different behaviors to make the best harmony. For example, harmony memory considering realizes the player’s utilization of a combination of sounds among the good harmony found in the past whereas pitch adjustment is derived from fine pitch tuning. However, at the authors’ best knowledge, there is no harmony search which takes into account the fact that poor music player improves as he/she follows from the good performer. This study proposes a new improved version of HS called Copycat Harmony Search (CcHS) which employs a novel pitch adjustment approach for dynamic bandwidth change and poor solution’s followship toward a good solution. The performance of CcHS is compared to that of the original HS and HS variants with modified pitch adjustment in a set of well-known mathematical benchmark problems. Results obtained show that CcHS outperforms other algorithms in most problems finding the known global optimum.