This chapter introduces ASRacos, an asynchronous variant of the SRacos algorithm, designed to accelerate derivative-free optimization through parallel computing. While SRacos excels in sequential optimization, its structure limits parallelization, which is crucial for time-consuming tasks. ASRacos modifies SRacos to enable asynchronous parallelism, allowing multiple servers to evaluate solutions concurrently while maintaining the sequential update structure. The chapter provides a theoretical analysis of ASRacos, including its query complexity and conditions under which it outperforms SRacos. Empirical studies compare ASRacos with other parallel methods on synthetic functions and reinforcement learning tasks, demonstrating its ability to achieve near-linear speedup and superior solution quality. The results highlight the effectiveness of asynchronous parallelism in accelerating optimization without compromising performance. Future work may explore integrating noise-handling methods and applying ASRacos to large-scale real-world problems.