The IIoT saw rapid evolution throughout the early years of the twenty-first century. The complexity of certain services in the industrial arena, poses challenges for our understanding. The task of identifying the most suitable malicious nodes is complex due to the need to consider several quality of service (QoS) factors, such as information transmission and network. There exist a multitude of limited evolutionary optimization algorithms that have been developed to tackle these issues. The present work introduces an expanded conceptualization of Bat optimization algorithm incorporates a rapid adaption approach and an optimization-oriented design. The proposed approach is evaluated in the architecture of IIoT services and in applications based on IIoT. The results are subsequently compared utilizing cutting-edge algorithms. The findings indicate that the suggested methodology yields superior outcomes in relation to the cyber security of QoS, fitness cost, and identification of IIoT nodes inside the IIoT service network.