Neural-Driven Heuristic for Strip Packing Trained with Black-Box Optimization
Abstract
We address the well-known NP-hard problem of
packing rectangular items into a strip, a problem of significant
importance in electronics (e.g., packing components on
printed circuit boards and macro-cell placement in Very-Large-
Scale Integration design) and telecommunications (e.g., allocating
data packets over transmission channels). Traditional heuristics
and metaheuristics struggle with generalization, efficiency, and
adaptability, as they rely on predefined rules or require extensive
computational effort for each new problem instance. In this
paper, we propose a neural-driven constructive heuristic that
leverages a lightware neural network trained via black-box
optimization to dynamically evaluate item placement decisions.
Instead of relying on static heuristic rules, our approach adapts
to the characteristics of each problem instance, enabling more
efficient and effective packing strategies.
To train the neural network, we employ the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES), a state-ofthe-
art derivative-free optimization method. Our method learns
decision policies by optimizing fill factor improvements over a
large dataset of problem instances. Unlike conventional heuristics,
our approach dynamically adapts placement decisions based on
a broad set of features describing the current partial solution
and remaining items.
Through extensive computational experiments, we compare
our method against well-known strip packing heuristics, including
MaxRects and Skyline-based algorithms. The results
demonstrate that our approach consistently outperforms the best
traditional heuristics, achieving up to 6.74 percentage points of
improvement in packing efficiency. Furthermore, our method
improves 87.87% of tested instances. Our study highlights the
potential of machine learning-driven heuristics in combinatorial
optimization and opens avenues for further research into adaptive
decision-making strategies in packing and scheduling problems.
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