用最近邻分类方法预测多目标优化d-Pareto支配性

为进一步提高预测精度, 修改候选解间原始Pareto支配性关系, 提出了d-Pareto支配性最近邻预测方法。结合多目标优化的自身特点, 给出了d-Pareto支配性最近邻预测框架, 并论证了d-Pareto支配性预测比Pareto支配性预测具有低平均预测错误率。同时也初步研究了d-Pareto支配性预测与多目标进化算法的交互作用。对几个经典多目标优化问题进行实验, 仿真结果表明d-Pareto支配性预测具有一定的可行性和有效性。

Predicting d-Pareto dominance in multi-objectiveoptimization using nearest neighbor classification method

To improve predicting accuracy further, this paper modified original Pareto dominance relation among the candidate solutions, and proposed a new method named d-Pareto dominance prediction using nearest neighbor. Combined with the characteristics of multi-objective optimization, it described the framework of d-Pareto and the conclusion that a d-Pareto dominance prediction could obtain a lower average prediction error rate comparing with Pareto demonstrated dominance prediction. Besides, it also explored the interaction between d-Pareto dominance prediction and multi-objective evolutionary algorithms. Experiments on several classic MOPs were conducted and the simulation results show that prediction of d-Pareto dominance is feasible and effective.