A Generational Difference Vector based Tri-Entropy Structure Optimizer for large-scale multiobjective optimization

Abstract

The increasing complexity of large-scale multiobjective optimization problems in engineering and scientific fields in recent years has imposed higher demands on the computational efficiency of algorithms. This paper introduces a novel algorithm named the Generational Difference Vector based Tri-Entropy Structure Optimizer (GDVTSO), which is designed for the efficient execution of large-scale multi-objective optimization tasks. The core idea is to determine a more effective search direction by calculating the local information entropy within the decision space and analyzing the changes in clusters before and after iterations. To this end, the Tri-Entropy Structure Optimizer (TSO) has been designed to more efficiently utilize information entropy for vector updates. Furthermore, the Generational Difference Vector (GDV) mechanism is introduced to provide guidance on search direction for vectors within each cluster. The GDVTSO algorithm demonstrates exceptional compatibility and extensive application potential. In this study, GDVTSO is integrated with two established large-scale optimization techniques, and a hybrid algorithm designated as GDVTSF is proposed through this methodological fusion. Moreover, GDVTSF’s performance exhibits a lower sensitivity to the dimensionality of optimization problems. Experimental results on standard large-scale multiobjective optimization benchmarks demonstrate that GDVTSF outperforms the current state-of-the-art optimization algorithms. Furthermore, it remarkably maintains its superior performance even when applied to high-dimensional problems with up to 10,000 decision variables.