Harmony search algorithm with self-adaptive dynamic parameters

This paper presents a new modification of Harmony Search (HS) algorithm for solving global continuous optimization problems. The proposed algorithm is named self-adaptive dynamic Harmony Search (SDHS). In this modification, firstly, harmony memory consideration rate (HMCR), pitch adjusting rate (PAR) and bandwidth distance (BW), are dynamically adjusted simultaneously to keep a good balance between exploration and exploitation in the search process and different search spaces. Secondly, we introduced X_best (the best value so far) to produce candidate harmony and adjust of the parameter BW. Experimental results tested on sixteen benchmark functions show that SDHS performs more effectively or at least similar to other approaches on most of test functions.