【摘要】 针对社会群体优化算法在函数优化问题中存在的求解精度不高和易陷入局部最优等问题，提出一种基于混合策略改进的社会群体优化算法。加入随机搅动变量，扩大搜索范围，增加种群多样性，以避免陷入局部最优。利用精英个体对全局最优解进行逐维改进，进一步提高算法的收敛速度和求解精度。仿真实验结果表明，改进算法在8个基准测试函数中比其他4种算法的求解精度更高，收敛速度更快，稳定性更强。更多还原

【Abstract】 In order to solve the problem of low precision and easy to fall into local optimal solution in the function optimization of social group optimization algorithm,a social group optimization algorithm based on hybrid strategy is proposed. Random stirring variables are added to expand the search range,increase the diversity of population,and avoid falling into local optimal solutions. Elite individuals are used to improve the global optimal solution in order to further improve the convergence speed and precision of the algorithm. The simulation results show that the improved algorithm has higher accuracy,faster convergence and stronger stability than the other four algorithms in 8 benchmark functions.更多还原