Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation

TL;DR

This paper introduces VARE, a novel evolutionary algorithm combining vector autoregression and environment-aware hypermutation to effectively solve dynamic multi-objective optimization problems with improved speed and solution quality.

Contribution

VARE uniquely integrates VAR and environment-aware hypermutation for better adaptation to environmental changes in DMO problems.

Findings

VARE is 50 times faster than TrDMOEA and MOEA/D-SVR.

VARE achieves significantly better results in dynamic environments.

VARE effectively handles a wide range of dynamic scenarios.

Abstract

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.