# Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation

arXiv: https://arxiv.org/abs/2305.12752 · HTML: https://mlsanity.com/paper/2305.12752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 75 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 5 of 5)

- [Transfer Learning based Dynamic Multiobjective Optimization Algorithms](https://mlsanity.com/paper/1612.06093/) (2016)
- [Dynamic Multi-Objectives Optimization with a Changing Number of Objectives](https://mlsanity.com/paper/1608.06514/) (2016)
- [Analysis of PCA Algorithms in Distributed Environments](https://mlsanity.com/paper/1503.05214/) (2015)
- [A Scalable Test Suite for Continuous Dynamic Multiobjective Optimisation](https://mlsanity.com/paper/1903.02510/) (2019)
- [Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine](https://mlsanity.com/paper/1910.08751/) (2019)

## Abstract (arXiv)

Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. Such problems pose various challenges to evolutionary algorithms, which have popularly been used to solve complex optimisation problems, due to their dynamic nature and resource restrictions in changing environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation to address environmental changes in DMO. VARE builds a VAR model that considers mutual relationship between decision variables to effectively predict the moving solutions in dynamic environments. Additionally, VARE introduces EAH to address the blindness of existing hypermutation strategies in increasing population diversity in dynamic scenarios where predictive approaches are unsuitable. A seamless integration of VAR and EAH in an environment-adaptive manner makes VARE effective to handle a wide range of dynamic environments and competitive with several popular DMO algorithms, as demonstrated in extensive experimental studies. Specially, the proposed algorithm is computationally 50 times faster than two widely-used algorithms (i.e., TrDMOEA and MOEA/D-SVR) while producing significantly better results.