Abstract: Harmony search (HS) algorithm is easily trapped into local optimal. To improve this shortcoming， chaos oppositionbased learning harmony search (COLHS) algorithm was proposed. Based on the thought of aggregation and divergence， preliminary judgments whether this algorithm was trapped into local optimal or backwater status were given， then according to the judge result， disturbance strategy was integrated with oppositionbased learning technology. The ergodicity of logistic chaos sequence and the space extensibility of oppositionbased learning were used. Besides， to balance aggregation and divergence， the history information of harmony memory was used to define the updating factor and the evolution factor， which were applied to dynamically adjust the pitch adjustment rate (PAR) and the bandwidth (BW). Numerical results demonstrated that the proposed algorithm is better than HS and the other eight kinds of improved HS algorithms that reported in recent literatures.

Key words: harmony search algorithm, chaos disturbance strategy, oppositionbased learning, local optimal, history information