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This paper proposes a new mutation operator current−to−better/1 to keep the population diversity. Under the control of current−to−better/1, a high-ranking individual with a larger fitness value tend to search its nearby area, while the low-ranking individual is stimulated to move towards high-ranking comity in a not fixed direction. A learning matrix is set to store successful amplification factors with their corresponding individual vectors, and then to update the amplification factors matrix for each generation. To test the effectiveness of current−to−better/1, it is embedded into JADE as a collaborator of original mutation operator DE/current−to−pbest/1. The efficiency of this new algorithm, namely, L-JADE, is proved by the performance comparisons with other already known DE-variant algorithms on 2-D, 3-D, 5-D, 10-D, and 20-D COCO testbed. Experimental results show that L-JADE performs better than most algorithms.