TY - GEN
T1 - Enhancing random search with surrogate models for lipschitz continuous optimization
AU - Zhang, Qi
AU - Hu, Jiaqiao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - We propose a random search algorithm for solving Lipschitz continuous optimization problems. The algorithm samples candidate from a parameterized probability distribution over the solution space and uses the previously sampled data to fit a surrogate model of the objective function. The surrogate model is then used to modify the parameterized distribution in a way that concentrates the search on the set of high-quality solutions. We prove the global convergence of the algorithm and provide numerical examples to illustrate its performance.
AB - We propose a random search algorithm for solving Lipschitz continuous optimization problems. The algorithm samples candidate from a parameterized probability distribution over the solution space and uses the previously sampled data to fit a surrogate model of the objective function. The surrogate model is then used to modify the parameterized distribution in a way that concentrates the search on the set of high-quality solutions. We prove the global convergence of the algorithm and provide numerical examples to illustrate its performance.
UR - https://www.scopus.com/pages/publications/85072969970
U2 - 10.1109/COASE.2019.8843031
DO - 10.1109/COASE.2019.8843031
M3 - Conference contribution
AN - SCOPUS:85072969970
T3 - IEEE International Conference on Automation Science and Engineering
SP - 768
EP - 773
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Y2 - 22 August 2019 through 26 August 2019
ER -