A dynamic regularization-based evolutionary learning algorithm for many-objective structural equation models

doi:10.1016/j.eswa.2026.132599

A dynamic regularization-based evolutionary learning algorithm for many-objective structural equation models

2026-05-08

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DOI：10.1016/j.eswa.2026.132599

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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.

Keywords:

structural equation models

many-objective optimization

evolutionary algorithms

dynamic regularization

latent structure discovery

A dynamic regularization-based evolutionary learning algorithm for many-objective structural equation models

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