Application of data mining in multiobjective optimization problems

Mosavi, Amir

doi:10.1051/smdo/2013002

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…The use of optimization in various applications is discussed by researchers [16,17]. Once the modeling is done, the final task is to predict the values of input features that could produce minimum exhaust gases with increase in brake thermal efficiency.…”

Section: Optimizationmentioning

confidence: 99%

Prediction and optimization of performance and emission characteristics of a dual fuel engine using machine learning

Karunamurthy

Feroskhan

Suganya

et al. 2022

Int. J. Simul. Multidisci. Des. Optim.

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The current research in engine, fuel and lubricant development are aiming towards environmental protection by reducing the harmful emissions. The testing under various conditions becomes mandatory before releasing product to meet the sustainable development goals of United Nations. This experimentation and testing under various operating conditions is time-consuming and tiresome process; it also leads to wastage of manpower, money, precious time and scarce resources. Intelligent techniques like Machine Learning (ML) has proven it's usage in almost all domains, trying to simulate the results as trained. This advantage is used to predict the performance and emission characteristics of a dual fuel engine. In this study, the experimental data are obtained from a single cylinder CI engine by operating under dual fuel mode using biogas and diesel as primary and secondary fuel respectively. The input parameters such as biogas flow rate, methane fraction (MF), torque and intake temperature are considered to predict the output parameters. The output parameters of the study includes performance attributes Brake thermal efficiency, secondary fuel energy ratio, and emissions attributes HC, CO, NOx and smoke. The proposed model uses Random forest Regressor and is trained using 324 distinct experiences recorded through physical experimentation. The model is validated using R2 score which is observed to be 0.997 for the given dataset while trained and tested in the ratio of 85:15. The outputs of the model are used to compute the output data for any new values of input attributes. The optimized values of the input parameters that could give maximum thermal efficiency and minimum emission is found using Lagrangian optimization. The optimized values are 12.48 Nm torque, 8.29 lit/min of biogas flow rate, methane fraction of 72.8%, intake temperature of 68.3 °C.

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Section: Optimizationmentioning

confidence: 99%

Prediction and optimization of performance and emission characteristics of a dual fuel engine using machine learning

Karunamurthy

Feroskhan

Suganya

et al. 2022

Int. J. Simul. Multidisci. Des. Optim.

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“…Such learning can be identified through finding meaningful patterns and correlations between variables. In this context, data science technologies such as data mining aim at getting insight and identify hidden patterns and correlations between variables to predict the forthcoming situations [7]. In the context of energy market, the accurate load prediction is extremely crucial in designing new power systems.…”

Section: Short-term Load Forecastingmentioning

confidence: 99%

“…A number of modeling techniques such as auto and linear regression moving average, as well as time series have been introduced in the last few decades, a for the STLF . Generally, these methods can be studied in two different models categories of dynamic and static.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid clustering and classification technique for forecasting short‐term energy consumption

Torabi

Hashemi

Saybani

et al. 2018

Env Prog and Sustain Energy

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119

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This paper presents a hybrid approach to predict the electric energy usage of weather‐sensitive loads. The presented method utilizes the clustering paradigm along with ANN and SVM approaches for accurate short‐term prediction of electric energy usage, using weather data. Since the methodology being invoked in this research is based on CRISP data mining, data preparation has received a great deal of attention in this research. Once data pre‐processing was done, the underlying pattern of electric energy consumption was extracted by the means of machine learning methods to precisely forecast short‐term energy consumption. The proposed approach (CBA‐ANN‐SVM) was applied to real load data and resulting higher accuracy comparing to the existing models. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 66–76, 2019

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“…Alternatively in our research utilizing RSO and visualization [21], which advocates learning for optimizing, the algorithm selection, adaptation and integration, are done in an automated way and the user is kept in the loop for subsequent refinements. Here one of the crucial issue in MCDM is to critically analyzing a mass of tentative solutions, which is visually mined to extract useful information [31][32][33]. In developing RSO in terms of learning capabilities there has been a progressive shift from the decision maker to the algorithm itself, through machine learning techniques [8].…”

Section: Combination Of Emo and Mcdmmentioning

confidence: 99%

Reactive Search Optimization; Application to Multiobjective Optimization Problems

Mosavi¹,

Vaezipour²

2012

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During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, in the framework of stochastic local search optimization, learning and optimization has been deeply interconnected through interaction with the decision maker via the visualization approach of the online graphs. Consequently a number of complex optimization problems, in particular multiobjective optimization problems, arising in widely different contexts have been effectively treated within the general framework of RSO. In solving real-life multiobjective optimization problems often most emphasis are spent on finding the complete Pareto-optimal set and less on decision-making. However the complete task of multiobjective optimization is considered as a combined task of optimization and decision-making. In this paper, we suggest an interactive procedure which will involve the decision-maker in the optimization process helping to choose a single solution at the end. Our proposed method works on the basis of Reactive Search Optimization (RSO) algorithms and available software architecture packages. The procedure is further compared with the excising novel method of Interactive Multiobjective Optimization and Decision-Making, using Evolutionary method (I-MODE). In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered.

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Application of data mining in multiobjective optimization problems

Cited by 9 publications

References 19 publications

Prediction and optimization of performance and emission characteristics of a dual fuel engine using machine learning

Prediction and optimization of performance and emission characteristics of a dual fuel engine using machine learning

A Hybrid clustering and classification technique for forecasting short‐term energy consumption

Reactive Search Optimization; Application to Multiobjective Optimization Problems

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