Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm
Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm
复制标题
使用基于多项式拟合的预测算法解决动态多目标问题
DOI：
10.1016/j.ins.2022.08.020
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发表时间：
2022-08
影响因子：
8.1
通讯作者：
Shouyong Jiang
中科院分区：
文献类型：
--
作者：
Qingyang Zhang;Xiangyu He;Shengxiang Yang;Yongquan Dong;Hui Song;Shouyong Jiang
近年来，动态多目标优化由于其在实际应用中的广泛应用而受到越来越多的关注。受多项式拟合的启发，提出了一种基于多项式拟合的预测算法（PFPA），并将其与基于模型的多目标分布估计算法（RM-MEDA）相结合，用于求解动态多目标优化问题。PFPA的主要使命是在检测到环境变化时，预测高质量的搜索种群，以便有效地跟踪移动的Pareto最优集。首先，在过去的环境中获得的非支配解决方案被用来预测高质量的解决方案的基础上的多步移动策略。其次，设计了一种基于多项式拟合的策略，根据得到的搜索种群对变量的分布进行拟合，捕捉新搜索环境下变量之间的关系。第三，根据变量的特征生成一些有效的搜索代理，以提高种群的收敛性和多样性。为了评估所提出的算法的性能，一组基准函数的实验结果，具有各种不同的动态特性和难度，和两个经典的动态工程设计问题表明，PFPA是具有竞争力的一些国家的最先进的算法。
Recently, dynamic multi-objective optimization has received growing attention due to its popularity in real-world applications. Inspired by polynomial fitting, this paper proposes a polynomial fitting-based prediction algorithm (PFPA) and incorporates it into the model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. When an environment change is detected, the main mission of PFPA is to predict high-quality search populations for tracking the moving Pareto-optimal set effectively. Firstly, the non-dominated solutions obtained in past environments are utilized to predict high-quality solutions based on a multi-step movement strategy. Secondly, a polynomial fitting-based strategy is designed to fit the distribution of variables according to the obtained search populations, and capture the relationship between variables in the new search environment. Thirdly, some effective search agents are generated for improving population convergence and diversity based on characteristics of variables. To evaluate the performance of the proposed algorithm, experimental results on a set of benchmark functions, with a variety of different dynamic characteristics and difficulties, and two classical dynamic engineering design problems show that PFPA is competitive with some state-of-the-art algorithms.
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