The multimodal optimization problems (MMOPs) require finding multiple optima simultaneously. Evolutionary computation integrated with niching techniques is commonly used to solve MMOPs. However, different niching techniques are suitable for different situations. In order to make the niche techniques perform better in different problems, in this paper, we propose an ensemble niching technique (ENT). In ENT, three different niching techniques, crowding clustering, speciation clustering, and nearest-better clustering (NBC), are used to identify the potential niche centers. Based on the potential niche center, the probability of an individual being a true niche center is calculated and the true niche center is identified. The identified true niche center can form niches more properly which combines the superiority of different niche techniques. Based on ENT, we combine it with DE and propose a novel multimodal optimization algorithm ENTDE. Meanwhile, we propose a dual-strategy update mechanism (DUM) to select the suitable mutation strategy in different evolutionary states for better evolution. To testify to the effectiveness of the proposed algorithm, ENTDE and eleven state-of-the-art algorithms are experimented on the widely used CEC2013 multimodal benchmark test suite. The experimental results indicate that ENTDE is significantly better than the compared algorithms.