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Gokhan Serhat

Publications and source records attributed to Gokhan Serhat.

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Optimization is Not Enough: Why Problem Formulation Deserves Equal Attention

Black-box optimization is increasingly used in engineering design problems where simulation-based evaluations are costly and gradients are unavailable. In this context, the optimization community has largely analyzed algorithm performance in context-free setups, while not enough attention has been devoted to how problem formulation and domain knowledge may affect the optimization outcomes. We address this gap through a case study in the topology optimization of laminated composite structures, formulated as a black-box optimization problem. Specifically, we consider the design of a cantilever beam under a volume constraint, intending to minimize compliance while optimizing both the structural topology and fiber orientations. To assess the impact of problem formulation, we explicitly separate topology and material design variables and compare two strategies: a concurrent approach that optimizes all variables simultaneously without leveraging physical insight, and a sequential approach that optimizes variables of the same nature in stages. Our results show that context-agnostic strategies consistently lead to suboptimal or non-physical designs. In contrast, the sequential strategy yields better-performing and more interpretable solutions. These findings underscore the value of incorporating, when available, domain knowledge into the optimization process and motivate the development of new black-box benchmarks that reward physically informed and context-aware optimization strategies.

cs.NE

COMPACT: Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique

Packing optimization is a prevalent problem that necessitates robust and efficient algorithms that are also simple to implement. One group of approaches is the raster methods, which rely on approximating the objects with pixelated representations. Although they are versatile in treating irregular geometries, the raster methods received limited attention in solving problems involving rotatable objects, where available studies generally analyze only right-angled rotations. In addition, raster approximation allows the use of unique performance metrics and indirect consideration of constraints, which have not been exploited in the literature. This study presents the new Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique (COMPACT). The method relies on raster representations of the objects that can be rotated by arbitrary angles, unlike the right-angled rotation restrictions imposed in many existing packing optimization studies based on raster methods. The raster approximations are obtained through loop-free operations that improve efficiency. Besides, a novel performance metric is introduced, which favors efficient filling of the available space by maximizing the internal contact between the objects as well as the contact between the objects and domain boundaries. Moreover, the objective functions are exploited to discard overlap and overflow constraints and enable the use of unconstrained optimization methods. Several test problems involving concurrent and ordered packing of multiple rectangular and circular objects into square bins are investigated. The results show that the proposed technique performs effectively in determining the packing arrangements.

cs.CG