This paper presents a novel nature inspired dragonfly based, optimization paradigm, named as dragonfly search convergence and coverage algorithm (DSCCA) for resource allocation in an industrial wireless network. This paradigm imitates the swarming behavior of the dragonfly for optimized and efficient resource allocation to fulfill multi-objective of better throughput, low error probability, and low latency. The robustness as well efficiency of the proposed DSCCA is evaluated using statistical analysis, convergence rate analysis, Wilcoxon¡¯s test and ANOVA on classical as well as modern IEEE CEC 2014 benchmark functions. For multi- as well conflicting objectives, DSCCA is evaluated using Pareto front. An extensive comparative analysis is carried out to exhibit the effectiveness of DSCCA over other well established optimization algorithms in terms of accuracy, convergence rate and efficiency. The results clearly shows that DSCCA provides more accurate solutions with high convergence rate as compared to other optimization algorithms. Along with this, DSCCA in compared with multi-objective resource management in terms fairness in the allocation of resources and convergence rate. Moreover, computational complexity for DSCCA is also evaluated and its adoption in realistic industrial wireless networks is also discussed. Comprehensive tests demonstrate that the methodology proposed contributes to fairer allocation of resources with lower complexity and the results are pragmatic, so it is easily extrapolated to real time execution in industrial wireless networks.