Since deep neural networks (DNNs) have provided state-of-the-art results for different computer vision tasks, they are utilized as the basic backbones to be employed in many domains. Nevertheless, DNNs have been demonstrated to be vulnerable to adversarial attacks in recent researches, which will threaten the security of different DNN-based systems. Compared with white-box adversarial attacks, black-box attacks are more similar to the realistic scenarios under the constraints like lacking knowledge of model and limited queries. However, existing methods under black-box scenarios not only require a large amount of model queries, but also are perceptible from human vision system. To address these issues, this study proposes a novel method based on evolution strategy, which improves the attack performance by considering the inherent distribution of updated gradient direction. It helps the proposed method in sampling effective solutions with higher probabilities as well as learning better searching paths. In order to make generated adversarial example less perceptible and reduce the redundant perturbations after a successful attacking, the proposed method utilizes class activation mapping to group the perturbations by introducing the attention mechanism, and then compresses the noise group by group while ensure that the generated images can still fool the target model. Extensive experiments on seven DNNs with different structures suggest the superiority of the proposed method compared with the state-of-the-art black-box adversarial attack approaches (i.e., AutoZOOM, QL-attack, FD-attack, and D-based attack).