An Efficient Multi-Objective Bayesian Optimization Approach for the Automated Analytical Design of Switched Reluctance Machines

2018 ◽  
Author(s):  
Shen Zhang ◽  
Sufei Li ◽  
Ronald G. Harley ◽  
Thomas G. Habetler
Keyword(s):  
Bayesian Optimization ◽  
Optimization Approach ◽  
Switched Reluctance ◽  
Analytical Design ◽  
Multi Objective ◽  
Switched Reluctance Machines

scholarly journals Lost in Optimization of Water Distribution Systems: Better Call Bayes

2022 ◽  
Author(s):  
Antonio Candelieri ◽  
Andrea Ponti ◽  
Ilaria Giordani ◽  
Francesco Archetti
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Probabilistic Models ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Water Distribution Systems ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Hydraulic Simulation ◽  
Multi Objective

The main goal of this paper is to show that Bayesian optimization could be regarded as a general framework for the data driven modelling and solution of problems arising in water distribution systems. Hydraulic simulation, both scenario based, and Monte Carlo is a key tool in modelling in water distribution systems. The related optimization problems fall in a simulation/optimization framework in which objectives and constraints are often black-box. Bayesian Optimization (BO) is characterized by a surrogate model, usually a Gaussian process, but also a random forest and increasingly neural networks and an acquisition function which drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible and sample efficient particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given for instance by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by the two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection on contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes. Showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms have been proposed for multi-objective detection problem using two different acquisition functions.

Machine learning-aided cost prediction and optimization in construction operations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Virok Sharma ◽  
Mohd Zaki ◽  
Kumar Neeraj Jha ◽  
N. M. Anoop Krishnan
Keyword(s):  
Machine Learning ◽  
Environmental Impact ◽  
Gaussian Process Regression ◽  
Construction Cost ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Cost Prediction ◽  
Construction Operations ◽  
Content Type ◽  
Multi Objective

PurposeThis paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.Design/methodology/approachTaking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.FindingsThe results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.Research limitations/implicationsNote that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.Originality/valueThe paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.

scholarly journals A New Multi-Objective Bayesian Optimization Formulation With the Acquisition Function for Convergence and Diversity

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Leshi Shu ◽  
Ping Jiang ◽  
Xinyu Shao ◽  
Yan Wang
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Exploration And Exploitation ◽  
Pareto Solutions ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Optimization Formulation

Abstract Bayesian optimization is a metamodel-based global optimization approach that can balance between exploration and exploitation. It has been widely used to solve single-objective optimization problems. In engineering design, making trade-offs between multiple conflicting objectives is common. In this work, a multi-objective Bayesian optimization approach is proposed to obtain the Pareto solutions. A novel acquisition function is proposed to determine the next sample point, which helps improve the diversity and convergence of the Pareto solutions. The proposed approach is compared with some state-of-the-art metamodel-based multi-objective optimization approaches with four numerical examples and one engineering case. The results show that the proposed approach can obtain satisfactory Pareto solutions with significantly reduced computational cost.

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