A Bayesian Optimization Algorithm for Nurse Scheduling

Abstract Small modular reactors (SMRs) are suitable for deployment in isolated underdeveloped areas to support highly localized microgrids. In order to achieve almost autonomous operation for reducing the cost of operating personnel, an autonomous control system with decision-making capability is needed. In this paper, a decision-making method based on Bayesian optimization algorithm (BOA) is proposed to explore the optimal operation scheme under fault conditions. BOA is used to adjust exploration strategy of operation scheme according to observations (operation schemes previously explored). To measure the feasibility of each operation scheme, an objective function that considers security and economy is established. BOA attempts to obtain the optimal operation scheme with maximum of the objective function in as few iterations as possible. To verify the proposed method, all main pump powered off fault is simulated by RELAP5 code. The optimal operation scheme of the fault is applied, the transient result shows that all key parameters are within safe limits and SMR is maintained at relatively high power, which means that BOA has the decision-making capability to get an optimal operation scheme on fault conditions.

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Entropy-Based Evaluation Relaxation Strategy for Bayesian Optimization Algorithm

Trends in Applied Intelligent Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-642-13025-0_14 ◽

2010 ◽

pp. 126-135

Author(s):

Hoang Ngoc Luong ◽

Hai Thanh Thi Nguyen ◽

Chang Wook Ahn

Keyword(s):

Optimization Algorithm ◽

Bayesian Optimization ◽

Bayesian Optimization Algorithm

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Real-coded Bayesian Optimization Algorithm

Towards a New Evolutionary Computation - Studies in Fuzziness and Soft Computing ◽

10.1007/11007937_3 ◽

2006 ◽

pp. 51-73

Author(s):

Chang Wook Ahn ◽

R.S. Ramakrishna ◽

David E. Goldberg

Keyword(s):

Optimization Algorithm ◽

Bayesian Optimization ◽

Bayesian Optimization Algorithm

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Black-box combinatorial optimization using models with integer-valued minima

Annals of Mathematics and Artificial Intelligence ◽

10.1007/s10472-020-09712-4 ◽

2020 ◽

Author(s):

Laurens Bliek ◽

Sicco Verwer ◽

Mathijs de Weerdt

Keyword(s):

Optimization Algorithm ◽

Surrogate Model ◽

Optimization Problems ◽

Random Search ◽

Continuous Optimization ◽

Optimal Solution ◽

Black Box ◽

Combinatorial Problems ◽

Bayesian Optimization ◽

Bayesian Optimization Algorithm

Abstract When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables.

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MODIFIED BAYESIAN OPTIMIZATION ALGORITHM FOR SPARSE LINEAR ANTENNA DESIGN

Progress In Electromagnetics Research B ◽

10.2528/pierb12091806 ◽

2013 ◽

Vol 54 ◽

pp. 385-405 ◽

Cited By ~ 3

Author(s):

Bui Van Ha ◽

Paola Pirinoli ◽

Riccardo E. Zich ◽

Marco Mussetta ◽

Francesco Grimaccia

Keyword(s):

Optimization Algorithm ◽

Bayesian Optimization ◽

Antenna Design ◽

Linear Antenna ◽

Bayesian Optimization Algorithm

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