Abstract: A new hybrid optimization algorithm is presented，which is based on the combination of the simulated annealing and cultural-based particle swarm optimization.To overcome the shortcoming of cultural-based particle swarm optimization that it is easy to trap into local minimum，the simulated annealing algorithm is embedded in the cultural algorithm framework as an evolving course from the knowledge space，which respectively has its own population to evolve independently and parallel.The mechanism improves the population diversity.Finally by comparing the result of the example，it can be found that this proposed algorithm illustrates its higher computational accuracy，convergence rate.

Key words: dual evolution, simulating annealed algorithm, cultural algorithm, hybrid algorithm, test functions

摘要： 提出一种基于模拟退火和文化粒子群的新型混合优化算法，该算法针对基本文化粒子群优化算法易陷入局部最优的缺点，将模拟退火引入文化算法框架中，作为知识空间的一个演化过程，通过模拟退火的概率突跳特性促使寻优过程跳出局部极值，保证了群体的多样性。最后通过8个标准测试函数的测试，仿真结果表明，该文算法是一种计算精度高、收敛速度快的混合优化算法。

关键词: 双演化, 模拟退火算法, 文化算法, 混合算法, 测试函数