In this paper, an orthogonal learning (OL) design whale optimization algorithm (WOA) with clustering mechanism, named OLWOA, is proposed to solve the complex continuous problems. In the proposed algorithm, the OL, as an effective strategy to utilize prior search information (experience), is utilized to overcome the disadvantages of the basic WOA, which converges slowly and falls into local optimum easily. The clustering-based mechanism guides the humpback whales to search toward an interesting area by propagating the information of good solutions from one cluster to another cluster. The experimental results reveal the effectiveness and significance of the OL and the clustering-based learning mechanism in the proposed algorithm.