一种改进型多模态免疫优化算法的研究

Research of Improved Multi-modal Immune Optimization Algorithm

基于克隆选择原理,引入混沌机制和小生境技术,提出了一种新的人工免疫算法——改进型克隆选择算法(ICSA)。该算法设计了一种自适应混沌变异算子,有效地避免了搜索的盲目性,提高了算法的收敛速度。利用随机过程鞅理论,分析了算法所形成抗体种群的平均适应度鞅的性质,并且当种群为有限状态时,证明了该算法能以概率1确保在有限步内收敛到全局最优解。对多模态函数优化的仿真实验表明,该算法能有效地抑制早熟,具有更好的全局收敛性。

By integrating chaos mechanism and niche technique, a novel immune algorithm the Improved Clonal Selection Algorithm （ICSA） was proposed based on the clonal selection principle. An adaptive chaos mutation operator was designed, and the operator could avoid blind research effectively and enhance the convergent speed. By using stochastic processes martingale theory, the martingale characteristic of the average fitness of the population was analyzed, and it is proved that the algorithm is global convergent with probability 1 in a finite number of steps when the state space is finite. The simulation results of multi-modal function optimization show that ICSA can inhibit prematurity and has preferable global convergent performence.