一种新的变异因子选择策略

New Strategy Based on Selection of Mutation Operator

在具有多层学习机制的免疫优化算法中,变异因子的选择概率对算法的有效性起着至关重要的作用。如果选择不够合理,将导致算法容易陷入局部最优,在一定程度上影响解的质量和收敛速度。针对多层学习机制的特点,讨论了各个因子之间的依赖性和相关性,提出了一种新的变异因子选择策略。选择4个基准函数作为测试函数进行了验证,结果表明,解的质量和收敛速度都有了明显的改善。

In immune optimization algorithm with multi-learning mechanism, the selection probability of mutation operators plays a vital role in the effectiveness of the algorithm. If the choice is not appropriate, the algorithm is easy to fall into local optimum, to a certain extent, affects the quality of the solution and reduces the rate of convergence. According to the characteristics of the multi-learning mechanism, the dependence and correlation between mutation operators were discussed. A new mutation operator selection strategy was proposed and four benchmark functions were selected as test functions to verify the performance of the strategy. The result shows that the quality of the solution and the convergence speed have obvious improvement.