Multi-objective optimization with adaptive reference-point updates and population prediction
DONG Hao-ming
YAO Li-zhong
WANG Ling
YIN Tao
LUO Hai-jun
Abstract：Traditional multi-objective optimization algorithms often suffer from rigid reference point distribution,weak environmental adaptability,and population diversity degradation,leading to imbalanced solution set distribution and low convergence efficiency.This paper proposes multi-objective optimization with adaptive reference-point updates and pop-ulation prediction.Firstly,an elite gene-guided reproductive crossover operator is designed to enhance global search and diversity through a triple mechanism of interference,exchange,and inheritance.Secondly,a population prediction inte-grates regularized regression with boundary perturbation to forecast new solutions,achieving dynamic fusion of historical information and new populations via error correction.Thirdly,an adaptive reference point update strategy dynamically eliminates invalid points and generates new ones to improve coverage in high-dimensional objective spaces.Finally,a complete algorithmic framework is established based on these strategies.Experimental results demonstrate the algorithm's superior performance on benchmark test problems and a real-world aluminum electrolysis process parameter optimization case.
Keywords：multi-objective optimizationenvironmental adaptabilitypopulation predictionadaptive reference point updatingaluminum electrolysis
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：11( 2136-2146 )