An multi-task optimization algorithm based on PLS subspace alignment and reuse population

WANG Zhen-yu; WANG Lei

doi:10.7641/CTA.2025.40598

Abstract：By using cross-task knowledge transfer,multi-task optimization can achieve better convergence performance than traditional single-task optimization.However,in multi-task optimization,the deviation of the search space and opti-mization scenarios,as well as noise that may interfere with knowledge transfer,can lead to a decrease in the efficiency of effective knowledge transfer and even negative transfer.An multi-task optimization algorithm based on partial least squares(PLS)subspace alignment and reuse population mechanism(PR-MTEA)is proposed to solve the problem.Firstly,by intro-ducing the PLS subspace projection strategy,the high-dimensional task search space is transformed into a low-dimensional space and specific low-dimensional subspaces are established for each task's population.Secondly,real-time adjustment of the Bregman divergence of the subspace is used to obtain an alignment matrix and achieve cross-task knowledge transfer.Finally,a population reuse mechanism based on the Residual structure is designed to avoid negative transfer and getting stuck in local optima,as well as to improve the convergence of the algorithm.Comparative experimental results with four other advanced multi-task algorithms show that PR-MTEA has better convergence performance and faster search ability.In addition,sensor coverage problem is conducted to test and analyze the feasibility and applicability of the improved algorithm.