Search for common minima in joint optimization of multiple cost functions

Adachi, Daiki; Tsujimoto, Naoto; Akashi, Ryosuke; Todo, Synge; Tsuneyuki, Shinji

doi:10.1016/j.cpc.2019.02.004

Cited by 14 publications

(1 citation statement)

References 32 publications

(41 reference statements)

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“…This method is used to determine extrema of nonconvex objective functions. [10,11] SA explores multiple attractor basins as it is designed to overcome barriers (accept moves in the direction opposite to that of the optimum) with a non-zero probability which may be tuned. SA has been widely used to reconstruct stochastic heterogeneous materials, as proposed by Torquato.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Simulated Annealing Algorithms Used for Improving the In‐Plane Stiffness of Inkjet Printed Composites

Vasile,

Coropeţchi,

Constantinescu

et al. 2023

Macromolecular Symposia

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The simulated annealing (SA) method is used in this work as to optimize the in‐plane stiffness of composite materials. Various configurations of the composite differ by the spatial distribution of hard voxels. The microstructure is assumed to be periodic, with a given length of periodicity in voxels in each of the two directions. The solution is obtained using the PyAnsys software under an MIT License, which is a collection of Python packages that enable the usage of ANSYS products. The SA algorithms prove to be very efficient, as they result in obtaining the one of the first three maximum values for the stiffness objective function for at least 98 out of 100 random trials. Therefore, SA can be successfully used for the microstructural design of composites at a fraction of the computational cost required by the brute force approach.

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Section: Introductionmentioning

confidence: 99%