Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm

Qingyang Zhang* (Corresponding Author), Xiangyu He, Shengxiang Yang, Yongquan Dong, Hui Song, Shouyong Jiang* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)868-886
Number of pages19
JournalInformation Sciences
Early online date18 Aug 2022
Publication statusPublished - Sep 2022


  • Dynamic multi-objective optimization
  • Polynomial fitting
  • Prediction mechanism
  • Dynamic engineering design


Dive into the research topics of 'Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm'. Together they form a unique fingerprint.

Cite this