The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem

Cerf, Sacha; Doerr, Benjamin; Hebras, Benjamin; Kahane, Yakob; Wietheger, Simon

doi:10.24963/ijcai.2023/613

Cited by 17 publications

(2 citation statements)

References 12 publications

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“…The GKS algorithm is a scheduling approach proposed in Cerf et al (2023) that uses knapsack to deploy modules on fog devices. Using knapsack approaches, PEs are allocated to modules.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.

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“…The GKS algorithm is a scheduling approach proposed in Cerf et al (2023) that uses knapsack to deploy modules on fog devices. Using knapsack approaches, PEs are allocated to modules.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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“…Two results showing that crossover can give asymptotic performance gains appeared in parallel [37], [38]. The first runtime analysis for a combinatorial optimization problem, the biobjective minimum spanning tree problem previously analyzed for the GSEMO [39], appeared in [40]. A runtime analysis in the presence of noise (together with the independent parallel work [16] the first mathematical runtime analysis of a MOEA in a noisy environment) was conducted in [41].…”

mentioning

confidence: 99%

Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)

Zheng,

Doerr

2024

IEEE Trans. Evol. Computat.

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Pareto‐Optimal Design of Automotive Battery Systems with Tabless Cylindrical Lithium‐Ion Cells: Resolving the Trade‐Off Between Energy, Performance, Weight, and Cost for Variable Vehicle Requirements

Pegel,

Jany,

Sauer

2024

Energy Tech

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Large‐format tabless cylindrical cells have been a top research subject within recent years. However, research so far has exclusively focused on isolated understanding of individual aspects such as the performance, safety, or cost. This study introduces a global optimization framework for battery systems with tabless cylindrical cells based on the groundwork laid within recent years. The framework is applied to gain comprehensive understanding of cross interactions between different design variables and the key performance indicators of the battery system. It was found that a well‐defined diameter exists which optimizes the battery energy for given boundary conditions. The multiobjective trade‐off between energy, performance, weight, and cost however might lead to different solutions with respect to the desired properties of the system. Small cylindrical cells with diameter less than 25 mm provide enhanced performance but lower energy and higher cost. Very large cylindrical cells with diameter more than 50 mm have less options for interconnection but provide the best cost‐saving potential. With realistic constraints, only diameters larger than 40 mm achieve Pareto‐optimal solutions. Aluminum housings are found to outmatch steel housings in nearly all properties, especially for larger diameters. Considering the widespread introduction of aluminum housings is recommended for automotive manufacturers.

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The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem

Cited by 17 publications

References 12 publications

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)

Pareto‐Optimal Design of Automotive Battery Systems with Tabless Cylindrical Lithium‐Ion Cells: Resolving the Trade‐Off Between Energy, Performance, Weight, and Cost for Variable Vehicle Requirements

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