scholarly journals Improving Search Quality with Automatic Ranking Evaluation and Tuning

2020 ◽  
Author(s):  
Larícia Cavalcante ◽  
Ullayne Lima ◽  
Luciano Barbosa ◽  
Ana Luiza Gomes ◽  
Éden Santana ◽  
...  
Keyword(s):  
Search Engine ◽  
Optimization Algorithm ◽  
Experimental Evaluation ◽  
Evaluation System ◽  
Relevant Information ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm ◽  
Customer Support ◽  
Ranking Performance ◽  
Search Quality

Search is a common feature available in document-based applications. It allows users to find information of interest easier. Two essential aspects for building an effective search is to evaluate the ranking quality and be able to efficiently tune it based on this evaluation. In this paper, we present our Automatic Ranking Tuning and Evaluation System (ARTES) that measures the ranking performance based on users’ clicks on search resulting pages and automatically tunes the search ranking function by applying a Bayesian Optimization algorithm. Our system is integrated with Elasticsearch, a widely-used search engine, which provides the search functionality. The whole solution is currently used by our customer support platform to help users effectively find relevant information, as our experimental evaluation confirms.

An improved 2-pentanone low to high-temperature kinetic model using Bayesian Optimization algorithm

2021 ◽  
Vol 231 ◽  
pp. 111453
Author(s):  
Qianjin Lin ◽  
Chun Zou ◽  
Shibo Liu ◽  
Yunpeng Wang ◽  
Lixin Lu ◽  
...  
Keyword(s):  
High Temperature ◽  
Kinetic Model ◽  
Optimization Algorithm ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm

A decision-making method based on Bayesian optimization algorithm for small modular reactor

Kerntechnik ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. 109-121 ◽  
Author(s):  
B. Zhang ◽  
M. Peng ◽  
S. Cheng ◽  
L. Sun
Keyword(s):  
Decision Making ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimal Operation ◽  
Bayesian Optimization ◽  
Operating Personnel ◽  
Bayesian Optimization Algorithm ◽  
Autonomous Operation ◽  
Small Modular Reactor ◽  
The Cost

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.

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

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.

scholarly journals MODIFIED BAYESIAN OPTIMIZATION ALGORITHM FOR SPARSE LINEAR ANTENNA DESIGN

2013 ◽  
Vol 54 ◽  
pp. 385-405 ◽  
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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