A Hybrid Genetic Scheduling Algorithm to Heterogeneous Distributed System

Kang, Yan; Zhang, Defu

doi:10.4236/am.2012.37111

Cited by 6 publications

(2 citation statements)

References 10 publications

(13 reference statements)

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“…A genetic algorithm is used for the optimal feature selection [38]. The pseudo-code for the genetic algorithm is expressed in Algorithm 1.…”

Section: B Feature Engineeringmentioning

confidence: 99%

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Byun

2020

IEEE Access

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Smart grids are developing rapidly, leading to the need for accurate forecasts of power consumption. However, developing a precise time series model for energy forecasting is difficult. It has to be trained using optimal meteorological features such as temperature and time lags to qualify for a beneficial model. We have proposed an approach that uses an ensemble machine learning model based on XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. We have also used the genetic algorithm (GA) to predict total load consumption from optimal feature selection. Using Jeju island's electricity consumption data as a case study shows that the proposed ensemble model optimized with GA is more accurate than the individual machine learning models. Using only the best-selected weather and time features, the proposed model records all the features of a complicated time series and shows a reduction in the mean absolute percentage error (MAPE) and the root mean square log error for the week ahead forecasts. We got 3.35 % MAPE of the three months test data by applying the proposed model. The smart grids operators can manage resources effectively to provide excellent services to the consumers based on the recommended model outcomes.

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“…A genetic algorithm is used for the optimal feature selection [38]. The pseudo-code for the genetic algorithm is expressed in Algorithm 1.…”

Section: B Feature Engineeringmentioning

confidence: 99%

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Byun

2020

IEEE Access

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“…The schedule obtained outperforms the GA and is within 9% of the optimal schedule. Kang and Zhang 20 presented a hybrid GA for static task mapping and scheduling on heterogeneous systems with satisfaction of the precedence requirements of the application.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective mapping of full-mission simulators on heterogeneous distributed multi-processor systems

Ayari

Hafnaoui

Aguiar

et al. 2016

Journal of Defense Modeling & Simulation

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Full-mission simulators (FMSs) are considered the most critical simulation tool belonging to the flight simulator family. FMSs include a faithful reproduction of fighter aircraft. They are used by armed forces for design, training, and investigation purposes. Due to the criticality of their timing constraints and the high computation cost of the whole simulation, FMSs need to run in a high-performance computing system. Heterogeneous distributed systems are among the leading computing platforms and can guarantee a significant increase in performance by providing a large number of parallel powerful execution resources. One of the most persistent challenges raised by these platforms is the difficulty of finding an optimal mapping of n tasks on m processing elements. The mapping problem is considered a variant of the quadratic assignment problem, in which an exhaustive search cannot be performed. The mapping problem is an NP-hard problem and solving it requires the use of meta-heuristics, and it becomes more challenging when one has to optimize more than one objective with respect to the timing constraints. Multi-objective evolutionary algorithms have proven their efficiency when tackling this problem. Most of the existent works deal with the task mapping by considering either a single objective or homogeneous architectures. Therefore, the main contribution of this paper is a framework based on the modeldriven design paradigm allowing us to map a set of intercommunicating real-time tasks making up the FMS model onto the heterogeneous distributed multi-processor system model. We propose a multi-objective approach based on the well-known optimization algorithm ''Non-dominated Sorting Genetic Algorithm-II'' satisfying the tight timing constraints of the simulation and minimizing makespan, communication cost, and memory consumption simultaneously.

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Scheduling Algorithms for High-Performance Computing: An Application Perspective of Fog Computing

Razzaq

Wahid

Khan

et al. 2019

EAI/Springer Innovations in Communication and Computing

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A Hybrid Genetic Scheduling Algorithm to Heterogeneous Distributed System

Cited by 6 publications

References 10 publications

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Multi-objective mapping of full-mission simulators on heterogeneous distributed multi-processor systems

Scheduling Algorithms for High-Performance Computing: An Application Perspective of Fog Computing

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