The effective utilization of Smart Wireless Devices ( \(\mathbb {SWD}\) ) and the real-time processing of data in disaster environments pose substantial challenges due to stringent constraints on energy resources, communication and computational infrastructures, as well as the requirement for rapid situational awareness and response.Autonomous Aerial Vehicles ( \(\mathbb {AAV}\) ), along with Edge Computing ( \(\mathbb{E}\mathbb{C}\) ), provide a promising paradigm for tackling these challenges in data acquisition and processing in the context of disaster relief situations. We proposes an innovative algorithm called Hashing-based Quantum Inspired Differential Evolution (H-QiDe), which is specially designed for tackling complex optimization-related challenges in disaster relief scenarios, including task computation, delay, power consumption, and cost. Through the optimization of resource allocation in a heterogeneous environment of \(\mathbb {AAV}\) , \(\mathbb{E}\mathbb{C}\) infrastructures, and \(\mathbb {SWD}\) , the H-QiDe algorithm minimizes end-to-end delay, energy consumption, and operational costs, while maximizing the proficiency of \(\mathbb {SWD}\) utilization.The novelty of the suggested methodology is founded on the integration of an innovative vector encoding & decoding scheme with quantum-inspired approach and hashing, which improves the proficiency of the exploration process in the optimization space. The performance of the H-QiDe algorithm is rigorously evaluated through extensive simulation experiments under diverse operating conditions and is measured against leading-edge evolutionary optimization algorithms. Furthermore, its effectiveness is validated via comprehensive statistical analyses, including the Friedman test, analysis of variance (ANOVA), and the Taguchi method, all of which validate the superior performance of the proposed approach.
