In industrial wireless networks data transmitted from source to destination is highly repetitive. This often leads to the queueing of the data and poor management of the queued data results in excessive delays, increased energy consumption and packet loss. Therefore, nature inspired based, dragonfly interaction optimization algorithm (DMOA) is proposed for optimization of the queue delay in industrial wireless network. The term ¡±interaction¡± herein used is characterization of the ¡±flying movement¡± of the dragonfly towards damselflies (female dragonfly) for mating. Thus, interaction is modelled as the flow of transmitted data packets, i.e. traffic, from source to base station. This includes each and every feature of dragonfly movement as well awareness of the rival dragonflies, predators and damselflies for the desired optimization of the queue delay. These features are juxtaposed as noise and interference, which are further used in the calculation of industrial wireless metrics: latency, error rate (reliability), throughput, energy efficiency and fairness for the optimization of the queue delay. Statistical analysis, convergence analysis, the Wilcoxon test, the Friedman test and the classical as well 2014 IEEE congress of evolutionary computation (CEC) on the benchmarks functions are also used for the evaluation of DMOA in terms of its robustness and efficiency. The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014. Furthermore, accuracy and efficacy of DMOA was demonstrated by means of: the convergence rate, Wilcoxon testing and ANOVA. Moreover, fairness using Jain¡¯s index in queue delay optimization in terms of throughput and latency of proposed algorithm along with computational complexity is also evaluated and compared with other algorithms. Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss. The proposed algorithm is also evaluated for the conflicting objectives at Pareto Front, and its analysis reveals that DMOA finds compromising solution between the objectives thereby optimizing queue delay. In addition, DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queueing delay for industry wireless networks.