算法引入模拟退火机制，在遗传进化过程中的每一代，对最优个体进行邻域局部寻优，利用模拟退火进一步改善算法的收敛性能。
Simulated annealing mechanism is introduced to do local-search for the best chromosome in every generation of the evolution process. This improves the convergence of the algorithm.
柔性神经树模型的结构和参数优化分别由概率增强式程序进化和模拟退火算法完成。
The structure and parameters of the flexible neural tree model are optimized by probabilistic incremental program evolution and simulation annealing, respectively.
用其他随机优化算法（模拟退火算法、遗传算法、进化规划等）与免疫算法进行了比较研究，给出了他们的异同点、免疫算法的优点等。
We made improvement to the search mechanism of the traditional Ant Colony Algorithm(ACA), put forward a Random Ant Colony Algorithm(RACA), and applied it in air combat decision.
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