Abstract: Harmony search algorithm(HS) exists a shortcoming is that it is easy to fall into local optimal, For the purpose of improving this shortcoming, chaos opposition-based learning harmony search algorithm(COLHS) is proposed in this paper. Based on the consideration between aggregation and divergence, we preliminary judges this algorithm whether is trapped into local optimal or backwater status, then according to the judge result to integrate chaos disturbance strategy and local opposition-based learning. The ergodic of Logistic Chaos sequence and the space extensibility of opposition-based learning used to make the algorithm escaping local optimal. Beside, COLHS uses the history information of harmony memory to define updating factor and evolution factor. The two factors are applied to dynamically adjust pitch adjustment rate (PAR) and bandwidth (bw), which is aimed at making COLHS able to balance aggregation and divergence by effectively adjust parameter in different search stage. The numerical calculation results demonstrated that the proposed algorithm has better global optimal ability than HS and the other 8 improved HS algorithms were reported in recent literature.