Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function op- timization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo’s algorithm has many advantages,such as the sim- plicity of its structure, the higher accuracy of its results, the wide range of its applications,and the robustness or its use. Io this paper a preliminary theoretical analysis or the algorithm is given and some numerical experiments has been done by using Guo’s algorithm for demonstrating the theoretical results. Three asynchronous paral- lel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo’s Algorithm.
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机译：LTE通讯系统中针对同层干扰环境对微小型基地台功率控制与用户位置推荐演算法 =Femtocell Power Control and User Location Recommendation Algorithm for Co-Tier Interference Environment in LTE Communication System
