Bayesian optimization algorithm for multi-objective scheduling of time and precedence constrained tasks in heterogeneous multiprocessor systems

Muhuri, Pranab K.; Biswas, Sajib K.

doi:10.1016/j.asoc.2020.106274

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…If the sample points increase gradually, the gap between posterior probability and true value of f (x) will be narrowed a lot. The target of defining an extract function is to extract sampling points in parameter space with purposes, while there are two directions to extract those points [28].…”

Section: Bayesian Optimizationmentioning

confidence: 99%

A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

Sun

Jiang

et al. 2020

Energies

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The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). The data of this study are derived from Daniudui gas field and the Hangjinqi gas field, which includes 2153 samples with known lithology facies class with each sample having seven measured properties (well log curves), and corresponding depth. The results show that BO significantly improves parameter optimization efficiency. The AUC values of the test sets of the two gas fields are 0.968 and 0.987, respectively, indicating that the proposed method has very high generalization performance. Additionally, we compare the proposed algorithm with Gradient Tree Boosting-Differential Evolution (GTB-DE) using the same dataset. The results demonstrated that the average of precision, recall and F1 score of the proposed method are respectively 4.85%, 5.7%, 3.25% greater than GTB-ED. The proposed XGBoost-BO ensemble model can automate the procedure of lithology identification, and it may also be used in the prediction of other reservoir properties.

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Section: Bayesian Optimizationmentioning

confidence: 99%

A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

Sun

Jiang

et al. 2020

Energies

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“…Bayesian optimization is a powerful tool for simultaneously optimizing hyperparameters while minimizing the number of expensive function evaluations required. 16 Numerous studies have considered hyperparameter optimization using a Bayesian optimization algorithm in their experimental design, 17 tuning the learning parameters, 16,18−20 scheduling tasks, 21,22 and applying computational-fluid-dynamics-based reactor designs. 23 Consequently, to improve the semicontinuous MC process operations, we propose two new operation recipes: a sequence including both a continuous and discrete flow and one involving additional reactant replenishment, and conduct recipe optimizations using Bayesian optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization of Semicontinuous Carbonation Process Operation Recipe

Lee

Park

et al. 2021

Ind. Eng. Chem. Res.

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We develop a pilot-scale semicontinuous aqueous mineral carbonation process that captures 40 tons of CO 2 per day by combining a 20 wt % aqueous Ca(OH) 2 solution with flue gas containing 15 vol % CO 2 . From the pilot-plant operation recipe (the so-called "base case"), we propose two new operation recipes to minimize the quantity of reactant used (Lt) and maximize the replenishment period (Pr): a sequence including both a continuous and discrete flow (the so-called "continuous case") and one involving additional reactant replenishment (the so-called "buffer case"). A multiobjective Bayesian optimization was adopted to optimize the operation recipe and minimize the number of simulations. Compared to the base case, the two proposed recipes were found to prolong the Pr by factors of ∼12 and ∼2.4 with increases in Lt of ∼4.6 and ∼2.6%, respectively. We anticipate that the two proposed recipes will provide operational flexibility by extending the boundaries.

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“…Multi-objective optimization problems exist widely in practical engineering problems, which are often characterized by constraints and high dimensions, such as vehicle routing planning [1], engineering optimization [2], and scheduling problems [3]. In general, the mathematical expression of the multi-objective optimization problem is defined as follows, minimize ( ) = ( 1 ( ), ⋯ , ( )) s. t ( ) ≥ 0, = 1,2, ⋯ , (1) ℎ ( ) = 0, = 1,2, ⋯ , ∈ Ω where = ( 1 , ⋯ , ) are a candidate solution, and are the numbers of inequality and equality constraints, respectively, Ω ⊆ ℝ is the decision space, : Ω → ℝ constitutes conflicting objective functions, and ℝ is called the objective space.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

Yang

Chen

et al. 2021

IEEE Access

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Constrained multi-objective optimization problems are common in practical engineering and are more difficult to handle than unconstrained problems. In general, it is necessary to find a balance between the convergence and diversity of solutions as well as the feasibility. For the constrained multi/many-objective optimization problem, a particle swarm optimization algorithm based on a two-level balance strategy is proposed. In contrast to existing views, the first level of the proposed algorithmic framework emphasizes convergence, while diversity and feasibility are considered together as the second-level scheme. An ensemble fitness ranking was used to improve the convergence of the proposed algorithm. To balance diversity and solution feasibility, the solutions are selected by combining the angles between the solutions using the constraint dominance principle. A penalty-based boundary-crossing approach is used as a utility function to calculate the fitness of the populations, which is compared with six state-of-the-art constrained multi/manyobjective evolutionary optimization algorithms on multiple constrained test suites, and the experimental results show that the proposed algorithm is highly competitive in most test problems. Furthermore, to illustrate the effect of different utility functions on the performance of the algorithm, the Chebyshev decomposition method is employed and compared with the former, and the results show that different utility functions need to be chosen to cope with problems of different characteristics.INDEX TERMS constrained multi/many-objective optimization, fitness ranking, constraint dominance principle, two-level balance scheme.

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Bayesian optimization algorithm for multi-objective scheduling of time and precedence constrained tasks in heterogeneous multiprocessor systems

Cited by 12 publications

References 24 publications

A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

Bayesian Optimization of Semicontinuous Carbonation Process Operation Recipe

A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

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