基于激素调节的量子免疫克隆函数优化算法

为了提高量子免疫克隆算法（quantum inspired immune clone algorithm, QICA）对函数全局寻优的精确性和稳定性，引入了内分泌激素的调节规律，根据当前个体适应度值和上一代种群的平均适应度值重新设计克隆规模，按照种群多样性和Hill函数的上升规律对其进行自适应调整，使进化各代中优秀个体的克隆得到扩增，同时减少不良个体的规模，从而提出了一种基于内分泌激素调节的量子免疫克隆算法（hormone adjustment based QICA, HAQICA）。利用标准测试函数对算法进行了验证，50次随机独立实验结果表明，HAQICA算法的收敛速度与QICA算法相当，最优解的均值与方差等数据，证明了HAQICA算法在提高函数全局寻优性能上的有效性。

Hormone adjustment based quantum-inspired immune clone algorithm for function optimization

Based on the adjustment regulation of endocrine hormone, a novel algorithm, called the hormone adjustment based quantum-inspired immune clone algorithm (HAQICA), is proposed to improve the accuracy and stability of quantum-inspired immune clone algorithm (QICA) on global optimization. In HAQICA, the clone size is calculated according to the individual fitness of the current generation and the average fitness of the previous generation, and is adjusted adaptively in terms of the population diversity and the rise law of Hill function that is the basic model of endocrine networks. HAQICA also increases the clone number of better individuals and decreases the clone number of worse individuals. Standard test functions are used to verify the algorithm, and the results of 50 random independent experiments show that the convergence speed of HAQICA is comparative with that of QICA and HAQICA is more efficient in global optimization according to the mean and variance values of optimal solutions.