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Reinforcement learning enhanced zeroth-order optimization for large-scale multiobjective optimization problems
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DOI：10.1016/j.swevo.2026.102514.png)
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Abstract
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<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="d1e912">
Proposes an automated evolutionary zeroth-order framework for large-scale multiobjective optimization.
</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="d1e917">
Introduces reinforcement learning for dynamic configuration of the proposed optimization modules.
</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="d1e922">
Demonstrates effective and scalable performance on benchmark LSMOPs and LLM fine-tuning tasks.
</div></span></li>
</ul>
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