基于免疫规划的模拟退火算法

通过对模拟退火算法优缺点的分析，提出了一种新型的模拟退火算法——基于免疫规划的模拟退火算法。该算法借鉴了生物免疫概念与理论，将免疫规划的全局寻优能力与模拟退火算法的局部寻优能力相结合，克服了模拟退火算法运算效率低的缺点。理论分析和仿真结果表明，该算法不仅能够有效地保持种群的多样性，而且收敛速度和稳定性都有了明显提高，收敛到最优值的比例可达到91%。

Simulated Annealing Algorithm Based on Immune Programming

This paper proposes a novel simulated annealing algorithm based on the immune programming——IPSA, after analyzing the advantages and disadvantages of the simulated annealing algorithm. With analogies to the concept and theory of biological immunity, the algorithm combines the global optimal capability of the immune programming with the local optimal capability of simulated annealing algorithm, overcomes inefficient speed of simulated annealing algorithm. Theory analysis and experimental results show that the algorithm keeps population diversity, and increases convergent speed and stability greatly. The proportion of converging to the global optimization can reach 91%.