An adaptive immune optimization algorithm for energy minimization problems

作者：Shao XG^*; Cheng LJ; Cai WS

来源：Journal of Chemical Physics, 2004, 120(24): 11401-11406.

DOI：10.1063/1.1753257

Based on the immune theory of biology, a novel evolutionary algorithm, adaptive immune optimization algorithm (AIOA), is proposed. In AIOA, density regulation and immune selection is adopted to control the individual diversity and the convergence adaptively. By an application of the algorithm to the optimization of test functions, it is shown that the algorithm is a highly efficient optimization method compared with other stochastic optimization methods. The algorithm was also applied to the optimization of Lennard-Jones clusters, and the results show that the method can find the optimal structure of N less than or equal to 80 with a very high efficiency. The proposed algorithm may be a good tool for fast global optimization in chemical or biological molecular simulations.

• 出版日期2004-6-22
• 单位中国科学技术大学