关键词: 双演化, 模拟退火算法, 文化算法, 混合算法, 测试函数

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