This paper explores a novel distributed multi-agent optimization algorithm for solving constrained optimization
problems. The performance of the algorithm is analyzed and the search capability is found to be influenced by the limitation
of the sample number during iteration. Therefore, an improved algorithm is proposed with compensate sampling technique
and smooth factors, which maintains the benefits of the original algorithm while improves its global and local search
capability. Experimental results show that the proposed algorithm achieves obvious improvement in convergence speed,
solution quality and long term stability.