An intelligent global harmony search approach to continuous optimization problems

Harmony search algorithm is a meta-heuristic optimization method imitating the music improvisation process, where musicians improvise their instruments' pitches searching for a perfect state of harmony. To solve continuous optimization problems more efficiently, this paper presents an improved harmony search algorithm using the swarm intelligence technique. Applying the proposed algorithm to several well-known benchmark problems, it is shown that it can find better solutions in comparison with both basic harmony search algorithms, and improved harmony search algorithms such as the self-adaptive global-best harmony search as well as novel global harmony search. Furthermore, a study on the effect of changing the parameters of the proposed algorithm on its performance is carried out. Finally, the proper values of the algorithm parameters are suggested.