Dynamic multi-objective optimization using historical evolutionary learning with global alignment local descriptor matching and collaborative guidance

Abstract

Dynamic multi-objective optimization problems involve conflicting objectives that evolve over time, necessitating algorithms capable of efficiently tracking the dynamic Pareto optimal set and preserving solution diversity. To address this, the paper proposes a framework for dynamic multi-objective optimization algorithms based on Historical Evolutionary Learning (EHEL). The framework employs four strategies: using global alignment and local descriptor matching to improve the accuracy of historical individual searches; adopting a multi-history experience collaborative guidance strategy to integrate historical information and enhance the reliability of evolutionary direction; introducing a dynamic quadratic correction strategy to revise less-potential solutions; and proposing a shrinking boundary strategy to preserve directional information and enhance boundary exploration capability. Experiments on the CEC 2018 benchmark test set show that EHEL exhibits superior optimization capabilities across various dynamic environments, significantly enhancing convergence diversity and solution quality compared to existing algorithms. This research provides a robust and adaptive solution strategy for dynamic multi-objective optimization by effectively integrating historical experience with adaptive mechanisms.