基于免疫克隆的蚁狮算法及其桁架优化

Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

摘要: 蚁狮优化算法具有较好的搜索和开发能力，但是在寻优后期精英蚁狮的影响权重减小，导致算法收敛速度较慢且易于陷入局优，对此提出一种免疫克隆的蚁狮优化算法。前期使用反向学习策略初始化蚂蚁种群，提高种群多样性；在精英蚁狮更新中加入柯西变异算子，提高算法后期的开发能力；最后结合免疫克隆选择算法对蚁狮进行克隆变异，改变蚁狮的位置和适应度值，进一步提高算法全局寻优能力以及收敛精度。通过10个测试函数、0~1背包来评估算法的寻优能力，并运用到桁架结构的尺寸、布局优化问题中，通过受力效果图发现优化效果良好，验证了ICALO应用于组合优化问题中收敛速度更快、精度更高的特点，为结构优化提供了一种新的方法。

Abstract: Antlion optimization algorithm has good search and development capabilities, but the influence weight of elite ant lions is reduced in the later stage of optimization, which leads to slower algorithm convergence and easy fall into local optimization. For this purpose, an antlion optimization algo-rithm based on immune cloning was proposed. In the early stage, the reverse learning strategy was used to initialize the ant population. The Cauchy mutation operator was added to the elite antlion update to improve the later development ability of the algorithm; finally, the antlion was cloned and mutated with the immune clone selection algorithm to change the position and fitness value of the antlion, and further improved the algorithm’s global optimization ability and convergence ac-curacy. 10 test functions and 0~1 backpack were used to evaluate the optimization ability of the al-gorithm, and applied to the size and layout optimization problems of the truss structure. The opti-mization effect was found to be good through the force effect diagram. It is verified that ICALO is ap-plied to combinatorial optimization problems with faster convergence speed and higher accuracy. It provides a new method for structural optimization.