Harmony Search (HS), as a novel optimization algorithm, is interesting for researchers because of its simplicity and appropriate efficiency. This algorithm has been applied to different continuous optimization problems leading to successful results. The parameter setting significantly impacts the performance of Harmony Search, similar to other meta-heuristics. In order to enhance this algorithm and facilitate the task of parameter setting, a hybrid approach is introduced in this paper. The harmony algorithm is improved by combining with the Differential Evolution (DE), as a successful evolutionary optimization algorithm which have been yielded admissible results in solving difficult problems. The new algorithm is tested on some well-known benchmark functions. The results show that the new hybrid algorithm has superior performance in comparison to the original Harmony Search and a modified version which is called Improved Harmony Search.
