多观测系统融合定位可以提高对目标位置的估计精确度，但如何提高信息融合效率目前没有得到较好的解决。针对此问题，提出一种基于多种群协同进化的分布式并行遗传算法。该算法将子观测系统转变为多个独立并行进化的子种群，通过设定离散适应度函数，使子种群收敛于一个最优值区域，通过个体的迁移操作为目标种群提供更多的测量信息进行融合，有效提高融合估计精确度。仿真结果表明，对比于集中式融合遗传算法和分布式并行Chan 融合算法，本文所提算法信息融合效果较好，定位精确度更高。

The fusion of multi-observation system can improve the estimation accuracy of target location, but there is no better solution to improve the efficiency of information fusion. To solve this problem, a distributed multi-population parallel genetic algorithm is presented by cooperative evolution. The algorithm divides observation system into multiple independent and parallel evolutionary sub populations. By setting a discrete fitness function, the sub population converges to an optimal value region, which can provide more information for the target population measurement fusion to effectively improve the fusion accuracy with the migration of individuals. The simulation results show that, in comparison with the genetic algorithm of centralized fusion and the parallel Chan algorithm of distributed fusion, the proposed algorithm can obtain better information fusion effect and a higher positioning accuracy.