改进自适应人工免疫算法求解函数优化问题

为克服经典人工免疫算法（AIA）在函数优化过程中存在的计算量大、收敛精度不高和收敛速度较慢等不足，引入多个自适应免疫算子，提出了一种改进自适应人工免疫算法（IAAIA）。在经典人工免疫算法中，引入迭代次数对抗体激励度计算算子进行自适应设计，引入种群抗体平均激励度与抗体激励度对免疫选择算子、克隆算子、变异算子与克隆抑制算子进行自适应设计，提升人工免疫算法的收敛速度、收敛精度和稳定性。选择9个典型测试函数作为实验对象，同时选择4种典型人工免疫算法作为对比算法优化实验函数，对比实验结果表明了改进的自适应人工免疫算法在求解函数优化问题的有效性和优越性。 

Improved adaptive artificial immune algorithm for solving function optimization problems

In order to overcome the shortcomings of Artificial Immune Algorithm (AIA) used in the function optimization process, such as huge calculation amount, low convergence accuracy and slow convergence speed, multiple adaptive immune operators are introduced, and an Improved Adaptive Artificial Immune Algorithm (IAAIA) is proposed. In the classic AIA, antibody excitation calculation operator is adaptively designed by introducing the number of iterations, and immune selection operator, clone operator, mutation operator and clonal inhibitory operator are adaptively designed by introducing antibody population average excitation and antibody excitation, which can improve the convergence accuracy, convergence speed and stability of AIA. Nine kinds of typical and widely used functions are chosen as experiment function, and four kinds of typical AIAs are selected as comparative algorithms to optimize the experiment functions. The comparative experiment results indicate the effectiveness and superiority of the IAAIA for solving function optimization problems.