一种基于Kriging模型加点策略的多目标粒子群优化算法

针对工程问题中优化结果误差较大的不足, 提出一种基于Kriging模型的多目标粒子群优化算法. 先利用Kriging模型的响应信息对误差进行预测, 并将预测误差引入Pareto支配关系比较、 全局领导者和局部领导者的选取及变异机制的进行过程中, 再结合文中加点策略使优化过程在少量抽样的前提下快速准确地逼近Pareto前沿解集. 性能测试结果表明, 该算法可提高复杂系统模型的优化效率及准确性.

A Multi-objective Particle Swarm Optimization Algorithm Based on Kriging Model Infilling Strategy

Aiming at the large error of optimization results in engineering problems, we proposed a multi-objective particle swarm optimization algorithm based on Kriging models. Firstly, the response information of the Kriging model was used to predict errors,  the prediction errors were introduced into the comparison of Pareto dominance relationship, the selection of global and local leaders, and the process of mutation mechanism. Then, combined with the infilling strategy in the text, the optimization process could quickly and accurately approach the Pareto frontier solution set on the premise of small number of samples . The performance test results show that the proposed algorithm can improve the optimization efficiency and accuracy of complex system models.