参考点自适应调整下评价指标驱动的高维多目标进化算法

Many-objective Evolutionary Algorithm Driven by Indicator Under Adaptive Reference Point Adjustment

在具有不同Pareto前沿形状的优化问题上,基于参考点的高维多目标进化算法表现出较差的通用性.为了解决这个问题,提出参考点自适应调整下评价指标驱动的高维多目标进化算法(Many-objective evolutionary algorithm driven by evaluation indicator under adaptive reference point adjustment, MaOEA-IAR). MaOEA-IAR提出Pareto前沿形状监测基础上的参考点自适应策略,利用该策略选择一组候选解作为初始参考点;然后通过曲线参数对参考点位置进行调整;将最终得到的能够适应不同Pareto前沿的参考点用于计算增强的反世代距离指标,基于指标值设计适应度函数作为选择标准.实验证明提出的算法在处理各种Pareto前沿形状的优化问题时能获得较好的性能,算法通用性高.

Many-objective evolutionary algorithms based on reference points show poor versatility on optimization problems with different shapes of Pareto fronts. To address this issue, this paper proposes a many-objective evolutionary algorithm driven by evaluation indicator under adaptive reference point adjustment(MaOEA-IAR).MaOEA-IAR proposes a reference point adaptation strategy based on the Pareto front shape monitoring, and uses this strategy to select a group of candidate solutions as the initial reference points, then adjusts their positions with the curve parameter. The final obtained reference points that can adapt to different Pareto fronts are used to calculate the enhanced inverted generational distance indicator, the fitness function is designed as selection criterion based on the indicator value. The experiment shows that the algorithm proposed in this paper can get good performance and high versatility when dealing with the optimization problems with various shapes of Pareto fronts.