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This paper introduces a novel nature-inspired optimization algorithm called the Addax Optimization Algorithm (AOA), which emulates the natural behavior of addax in the wild. The core inspiration for AOA is drawn from the addax's foraging strategy and digging skills. The theoretical foundation of AOA is expounded and mathematically modeled in two phases: (i) exploration based on modeling addax position change during foraging and (ii) exploitation based on addax position change modeling during digging. The efficiency of AOA in handling real-world engineering applications is evaluated on four engineering design problems. The optimization results show that AOA is achieved effective solutions for optimization problems with its high ability in exploration, exploitation, and establishing a balance between them during the search process. The outcomes derived from applying AOA are compared with the performance of twelve well-known optimization algorithms. The simulation results show that AOA is provided superior performance compared to competitor algorithms, by achieving better results and ranking as the first best optimizer. The simulation findings show that the proposed AOA approach has an effective performance for handling optimization tasks in engineering applications. [ABSTRACT FROM AUTHOR]