Dynamic Multi-Objective Optimisation Based on Vector Autoregressive Evolution

Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation (EAH) to address env...

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Vydáno v:IEEE transactions on evolutionary computation s. 1
Hlavní autoři: Jiang, Shouyong, Wang, Yong, Hu, Yaru, Zhang, Qingyang, Yang, Shengxiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Dynamic multi-objective optimisation
environment-aware hypermutation
evolutionary algorithms
vector autoregression
ISSN:1089-778X, 1941-0026
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Shrnutí:Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation (EAH) to address environmental changes in DMO. In light of mutual dependency between decision variables in Pareto-optimal solutions, VARE builds an efficient VAR model, capturing such mutual relationship while handling dense model parameterisation with dimensionality reduction, to predict the moving solutions in dynamic environments. Additionally, VARE introduces EAH to address the blindness of existing hypermutation strategies in increasing population diversity, for scenarios where predictive approaches are unsuitable, by making hypermutation aware of the significance of environmental changes in both decision and objective spaces. A seamless integration of VAR and EAH in an environment-adaptive manner makes VARE effective to handle a variety of dynamic environments and competitive with several popular DMO algorithms, as demonstrated in extensive empirical studies. Specially, the proposed algorithm is computationally much faster than popular transfer learning based approaches while producing significantly better results.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2025.3570116