基于偏好方向的区间多目标交互进化算法

区间多目标优化问题在实际应用中普遍存在且非常重要.为得到贴合决策者偏好的最满意解,采用边优化边决策的方法,提出一种交互进化算法.该算法通过请求决策者从部分非被支配解中选择一个最差解,提取决策者的偏好方向,基于该偏好方向设计反映候选解逼近性能的测度,将具有相同序值和决策者偏好的候选解排序.将所提方法应用于4个区间2目标优化问题,并与利用偏好多面体解决区间多目标优化问题的进化算法(PPIMOEA)和后验法比较,实验结果验证了所提出方法的有效性和高效性.

Interactive evolutionary algorithms for interval multi-objective optimization problems based on preference direction

Interval multi-objective optimization problems are ubiquitous and important in real-world applications. An
interactive evolutionary algorithm incorporating an optimization-cum-decision-making procedure is presented to obtain the
most preferred solution that fits a decision-maker(DM)’s preferences. In this algorithm, a preference direction is elicited by
requesting the DM to select the worst one from a part of non-dominated solutions. A metric based on the above direction,
which reflects the approximation performance of a candidate solution, is designed to rank different solutions with the same
rank and preference. The proposed method is applied to four interval bi-objective optimization problems, and compared
with PPIMOEA as well as a posteriori method. The experimental results show the effectiveness and high efficiency of the
proposed method.