桁架结构多目标优化的免疫克隆选择算法

为了解决带有约束的结构多目标优化问题，将免疫克隆选择算法应用于桁架结构的多目标优化设计中. 根据免疫学基本原理，采用非支配邻域选择机制、比例克隆和精英策略，使算法很好地保持了所得解的多样性、均匀性和收敛性.在桁架结构优化的数学模型中，采用惩罚函数法处理违反约束的情况.为了验证所提算法的可行性和有效性，对经典桁架进行了优化，并与其它方法作比较，数值结果表明，该算法在收敛速度、时间消耗和求解质量上均具有一定的优势.

Immune Clonal Selection Algorithm for Truss Structure Multi-objective Optimization

In order to solve the multi-objective optimization of structures with constrains, the immune clonal selection algorithm was applied. Based on the immunology theory, the non-dominated neighbor-based selection, proportional cloning and elitism strategy were introduced in the multi-objective immune clonal selection algorithm (MOICSA) to enhance the diversity, the uniformity and the convergence of the solution obtained. Penalty function method was used to deal with violated constraints. Several classical problems were solved to demonstrate the feasibility and effectiveness of the MOICSA algorithm, and the results were compared with other optimization methods. The simulation results show that the algorithm has advantages in convergence speed, time consuming and solution quality.