A Dynamic Regularization-Based Evolutionary Learning Algorithm for Many-Objective Structural Equation Models

The construction of structural equation models (SEMs) is fundamentally challenged by the latent nature of the constructs, which often leads to ineffective model specification and computational inefficiency in many-objective optimization scenarios. Conventional many-objective evolutionary algorithms frequently exhibit instability and a tendency to produce lots of infeasible solutions when applied to many-objective SEMs. To overcome these limitations, this paper proposes a dynamic regularization-based evolutionary learning algorithm (DR-ELA). The proposed DR-ELA incorporates a novel dual-matrix fusion model for robust latent structure discovery, coupled with an innovative tri-phase dynamic regularization mechanism. This mechanism automates sparsity control through adaptive germination, development, and maturation phases. Experimental results on many-objective SEM test instances demonstrate that the proposed DR-ELA performs significantly better than several many-objective evolutionary algorithms. Furthermore, experimental results show that DR-ELA can effectively balance theoretical fidelity and exploratory through the adaptive regularization mechanism, achieving superior performance in both solution quality and computational efficiency.