1.School of Informatim and Control Engineering，China University of Mining and Technology，Xuzhou ，China；2.Institute of Big Data in Mine Industry， China Coal Research Institute， Beijing ，China

Rock burst is a typical coal mine dynamic disaster that can be warned early through monitoring microseism caused，and microseismic location is one of the most critical and basic parameters to be determined.In the process of microseismic source localization，the number of detectors involved in positioning has a great influence on positioning accuracy.When the microseismic source is detected by enough detectors，continuously increasing the number of positioning channels cannot effectively improve the positioning accuracy.Therefore，choosing an appropriate number of detectors to participate in the localization is very important to improve the accuracy of localization.In order to solve this problem，with the condition of setting homogeneous medium，based on the microseismic source localization model of observation time in this paper，we propose a multi-objective microseismic source localization model that takes into account both the number of detectors and positioning accuracy.Besides，combined the complementary advantages between Multi-Objective Particle Swarm Optimization (MOPSO) and Simulated Annealing (SA)，a hybrid algorithm based on MOPSO-SA is obtained to solve the above model.The proposed method utilizes the global search performance of the multi-objective particle swarm optimization algorithm to provide a better initial solution for the simulated annealing algorithm for local search，thereby effectively preventing the optimization process from falling into local extreme values.The experimental results illustrate that the proposed algorithm has better microseismic positioning accuracy.

国家自然科学基金项目(61973305)，中国煤炭科工集团有限公司科技创新创业资金专项重点项目（2019-2-ZD002）