Neural network-assisted evolutionary search for large-scale sparse multiobjective optimization

Zhuanlian Ding

doi:10.1016/j.eswa.2026.133990

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Neural network-assisted evolutionary search for large-scale sparse multiobjective optimization

2026-08-18

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DOI：10.1016/j.eswa.2026.133990

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Abstract

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• A neural network-assisted evolutionary search strategy is proposed for efficient directed search. • Paired solutions are constructed as training data to learn promising Pareto descent directions. • Sparsity-based environmental selection balances convergence, diversity, and sparsity. • Experiments on benchmarks and real-world problems confirm NNES’s superiority in LSMOPs. Abstract In large-scale sparse multiobjective optimization problems (LSMOPs), searching for high-quality sparse solutions in the large-scale search space is challenging. The search capability of most existing genetic operators deteriorates dramatically when directly tackling LSMOPs, as they often fail to grasp the informative search direction. Thus, this paper proposes a neural network-assisted evolutionary search (NNES) strategy to achieve efficient directed search for solving LSMOPs. NNES leverages the predictive power of neural networks (NNs) to guide each solution toward rapid convergence along its learned promising Pareto descent direction. Specifically, NNES first pairs training data from the current population based on convergence-sparsity tradeoff to represent solution improvement directions. Then an NN is employed to learn the underlying directional improvements from these paired solutions. Finally, new promising sparse solutions are generated along the direction predicted by the trained NN for assisting the optimization. In addition, an improved environmental selection is customized with balanceable convergence, diversity, and sparsity. Experimental studies validate the superior performance of the proposed algorithm compared with the state-of-the-art evolutionary algorithms.

Keywords:

Evolutionary algorithm

Neural network

Sparse

Large-scale

Multiobjective optimization