A Fast Dynamic Evolutionary Multiobjective Algorithm via Manifold Transfer Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

  • Min Jiang
  • Zhenzhong Wang
  • Liming Qiu
  • Shihui Guo
  • Xing Gao

Detail(s)

Original languageEnglish
Pages (from-to)3417-3428
Journal / PublicationIEEE Transactions on Cybernetics
Volume51
Issue number7
Online published20 May 2020
Publication statusPublished - Jul 2021

Abstract

Many real-world optimization problems involve multiple objectives, constraints, and parameters that may change over time. These problems are often called dynamic multiobjective optimization problems (DMOPs). The difficulty in solving DMOPs is the need to track the changing Pareto-optimal front efficiently and accurately. It is known that transfer learning (TL)-based methods have the advantage of reusing experiences obtained from past computational processes to improve the quality of current solutions. However, existing TL-based methods are generally computationally intensive and thus time consuming. This article proposes a new memory-driven manifold TL-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA). The method combines the mechanism of memory to preserve the best individuals from the past with the feature of manifold TL to predict the optimal individuals at the new instance during the evolution. The elites of these individuals obtained from both past experience and future prediction will then constitute as the initial population in the optimization process. This strategy significantly improves the quality of solutions at the initial stage and reduces the computational cost required in existing methods. Different benchmark problems are used to validate the proposed algorithm and the simulation results are compared with state-of-the-art dynamic multiobjective optimization algorithms (DMOAs). The results show that our approach is capable of improving the computational speed by two orders of magnitude while achieving a better quality of solutions than existing methods.

Research Area(s)

  • Heuristic algorithms, Manifolds, Optimization, Sociology, Statistics, Prediction algorithms, Diversity methods, Dynamic multiobjective, manifold learning, transfer learning (TL), OPTIMIZATION, PREDICTION

Citation Format(s)

A Fast Dynamic Evolutionary Multiobjective Algorithm via Manifold Transfer Learning. / Jiang, Min; Wang, Zhenzhong; Qiu, Liming; Guo, Shihui; Gao, Xing; Tan, Kay Chen.

In: IEEE Transactions on Cybernetics, Vol. 51, No. 7, 07.2021, p. 3417-3428.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review