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本文提出一种带有启发式社会学习机制的人工免疫系统优化算法（AIS-HSL）。在AIS-HSL中，候选抗体被分成两个群，即精英群（ES）和普通群（CS）。不同的群执行不同的变异机制，即精英群采用自学习机制，普通群采用启发式社会学习（HSL）机制。在HSL机制中，CS中的每个抗体根据亲和力依概率选择ES中的抗体进行学习，以避免陷入局部最优。通过一些比较性实验，来评估 AIS-HSL 算法的性能。实验结果表明，与传统的opt-aiNet算法和IA-AIS算法相比，本文提出的AIS-HSL算法有着更高的收敛精度和收敛速度。“,”This paper proposes an artificial immune system with heuristic social learning (AIS-HSL) for optimization. In the AIS-HSL optimization, the candidate antibodies is separated into two swarms, i.e., the elitist swarm (ES) and the common swarm (CS). Different swarms experience different mutation processes, i.e. , a self-learning strategy is used for the ES while a heuristic social-learning (HSL) mechanism is applied to the CS. In the HSL mechanism, each antibody in CS learns from an selected antibody in ES based on the probability determined by the affinity to avoid falling into the local optima. Some comparative numerical simulations are arranged to evaluate the performance of the proposed AIS-HSL. The results demonstrate that the proposed AIS-HSL outperforms the canonical opt-aiNet, the IA-AIS and the AAIS-2S in convergence speed and solution accuracy.