# Transfer Learning based Dynamic Multiobjective Optimization Algorithms

arXiv: https://arxiv.org/abs/1612.06093 · HTML: https://mlsanity.com/paper/1612.06093/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2016.

## Built on by (most cited first, 17 of 17)

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- [A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization](https://mlsanity.com/paper/2206.11526/) (2022)
- [Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation](https://mlsanity.com/paper/2306.00202/) (2023)
- [Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection](https://mlsanity.com/paper/2411.08171/) (2024)
- [Evolutionary Multitasking AUC Optimization](https://mlsanity.com/paper/2201.01145/) (2022)
- [Evolutionary Dynamic Optimization and Machine Learning](https://mlsanity.com/paper/2310.08748/) (2023)
- [Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model](https://mlsanity.com/paper/2108.04197/) (2021)
- [A Survey on Computational Intelligence-based Transfer Learning](https://mlsanity.com/paper/2206.10593/) (2022)
- [A Framework Based on Generational and Environmental Response Strategies for Dynamic Multi-objective Optimization](https://mlsanity.com/paper/2207.04047/) (2022)
- [Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation](https://mlsanity.com/paper/2305.12752/) (2023)
- [Enhancing Large-scale UAV Route Planing with Global and Local Features via Reinforcement Graph Fusion](https://mlsanity.com/paper/2412.15537/) (2024)
- [CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms](https://mlsanity.com/paper/2506.06362/) (2025)
- [Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm](https://mlsanity.com/paper/2512.18947/) (2025)

## Abstract (arXiv)

One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is the optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing the "experiences" to construct a prediction model via statistical machine learning approaches. However most of the existing methods ignore the non-independent and identically distributed nature of data used to construct the prediction model. In this paper, we propose an algorithmic framework, called Tr-DMOEA, which integrates transfer learning and population-based evolutionary algorithm for solving the DMOPs. This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications. To verify this, we incorporate the proposed approach into the development of three well-known algorithms, nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle swarm optimization (MOPSO), and the regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), and then employ twelve benchmark functions to test these algorithms as well as compare with some chosen state-of-the-art designs. The experimental results confirm the effectiveness of the proposed method through exploiting machine learning technology.