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A Comparison Of New Methods For Generating Energy-Minimizing Configurations Of Patchy Particles, Eric Jankowski, Sharon C. Glotzer
A Comparison Of New Methods For Generating Energy-Minimizing Configurations Of Patchy Particles, Eric Jankowski, Sharon C. Glotzer
Eric Jankowski
Increasingly complex particles are pushing the limits of traditional simulation techniques used to study self-assembly. In this work, we test the use of a learning-augmented Monte Carlo method for predicting low energy configurations of patchy particles shaped like “Tetris®” pieces. We extend this method to compare it against Monte Carlo simulations with cluster moves and introduce a new algorithm—bottom-up building block assembly—for quickly generating ordered configurations of particles with a hierarchy of interaction energies.
A Neural Network: Family Competition Genetic Algorithm And Its Application In Electromagnetic Optimization, Chien Hsun Chen, P. Y. Chen, H. Weng
A Neural Network: Family Competition Genetic Algorithm And Its Application In Electromagnetic Optimization, Chien Hsun Chen, P. Y. Chen, H. Weng
Chien Hsun Chen
This study proposes a neural network-family competition genetic algorithm (NN-FCGA) for solving the electromagnetic (EM) optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN) and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA) to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are …