Compressed sensing is a signal processing method that performs the compressing and sensing processes at the same time. Sparse signal reconstruction is one of the most important issues of compressed sensing. The developments in sparse signal reconstruction methods directly affect the performance of the compressed sensing process. Many sparse signal reconstruction methods have been proposed in the literature. In general, these algorithms are classified as convex optimization, non-convex optimization, and greedy algorithms. In addition, multi-objective optimization algorithms have started to be used in sparse signal reconstruction lately. A sparse signal reconstruction method based on a Multi-objective Artificial Bee Colony algorithm is proposed in this study. The proposed algorithm optimizes the sparsity and measurement error at the same time. Furthermore, it uses the iterative half thresholding algorithm to improve the convergence acceleration of the method. The proposed method was evaluated by using various test signals. Additionally, it was compared with other sparse signal reconstruction algorithms. According to the obtained results, the proposed method has some superiority over the compared algorithms.

压缩感知是一种同时进行压缩和感知过程的信号处理方法。稀疏信号重建是压缩感知最重要的问题之一。稀疏信号重建方法的发展直接影响压缩感知过程的性能。文献中已经提出了许多稀疏信号重建方法。一般来说，这些算法分为凸优化、非凸优化和贪心算法。此外，多目标优化算法最近开始用于稀疏信号重建。本研究提出了一种基于多目标人工蜂群算法的稀疏信号重建方法。该算法同时优化了稀疏性和测量误差。此外，它使用迭代半阈值算法来提高方法的收敛速度。通过使用各种测试信号来评估所提出的方法。此外，它还与其他稀疏信号重建算法进行了比较。根据得到的结果，所提出的方法相对于比较算法有一定的优势。