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Neural network-assisted evolutionary search for large-scale sparse multiobjective optimization
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DOI：10.1016/j.eswa.2026.133990.png)
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Abstract
En 中文
<ul class="list">
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0001">
A neural network-assisted evolutionary search strategy is proposed for efficient directed search.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0002">
Paired solutions are constructed as training data to learn promising Pareto descent directions.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0003">
Sparsity-based environmental selection balances convergence, diversity, and sparsity.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="p0004">
Experiments on benchmarks and real-world problems confirm NNES’s superiority in LSMOPs.
</div></span></li>
</ul>
Keywords:
Evolutionary algorithm
Neural network
Sparse
Large-scale
Multiobjective optimization
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W