A Parallel and Compact version of the Sine Cosine Algorithm (PCSCA) is proposed in this article. Parallel method can effectively improve search ability and increase the diversity of solutions. We develop three communication strategies based on parallelism idea to serve different types of optimization function to achieve the best performance. Furthermore, compact method uses statistical distribution to represent the solutions, which can save memory space and energy of the digital device. To check the optimization effect of the proposed PCSCA algorithm, it is tested on the CEC2013 benchmark function set and compared to SCA, parallel compact Cuckoo Search (PCCS) algorithms. The empirical study demonstrates that PCSCA has improved by 50.1% and 5.6%, compared to SCA and PCCS, respectively. Finally, we apply PCSCA to optimize the position accuracy of sensor node deployed in 3D actual terrain. Experimental results show that PCSCA can achieve lower localization error via Time Difference of Arrival method.

本文提出了并行和紧凑版本的正弦余弦算法 (PCSCA)。并行方法可以有效提高搜索能力，增加解的多样性。我们基于并行思想开发了三种通信策略，以服务于不同类型的优化函数以实现最佳性能。此外，紧凑方法使用统计分布来表示解决方案，可以节省数字设备的存储空间和能量。为了检查所提出的 PCSCA 算法的优化效果，在 CEC2013 基准函数集上进行了测试，并与 SCA、并行紧凑型布谷鸟搜索 (PCCS) 算法进行了比较。实证研究表明，与 SCA 和 PCCS 相比，PCSCA 分别提高了 50.1% 和 5.6%。最后，我们应用 PCSCA 来优化部署在 3D 实际地形中的传感器节点的位置精度。实验结果表明，PCSCA 可以通过到达时间差方法实现较低的定位误差。