Learning-guided nondominated sorting genetic algorithm II for multi-objective satellite range scheduling problem

Song, Yanjie; Ma, Xin; Li, Xunjia; Xing, Lining; Wang, Pei

doi:10.1016/j.swevo.2019.06.008

Cited by 35 publications

(13 citation statements)

References 22 publications

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“…Lastly, the scheduling problems which is the most discussed category of MOCOPs consists of open shop scheduling problem [125], [126], job shop scheduling problem (JSSP) [127], [128], [129], [130], [131], [132], FSP [133], [134], [135], [136], [137], [138], project scheduling problem (PSP) [139], resource constrained PSP (RCPSP) [140], [141], [142], [143], [144], [145], timetabling problem [146], cross-docking scheduling problem [147], task scheduling problem [148], [149], [150], [151], [152], [153], [154], machine scheduling problem [155], [156], [157], [158], [159], [160], [161], satellite range scheduling problem [162], multi-objective satellite data transmission scheduling problem [163], satellite scheduling of large areal tasks [164], operating room scheduling [165], [166], harvest scheduling problem [167], energy-efficiency scheduling problem…”

Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

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A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

“…Song et al [162] proposed learning guided NSGA-II for a multi-objective satellite range scheduling problem. The algorithm contained NSGA-II and a learning mechanism to speed up the convergence.…”

Section: E) Scheduling Problemmentioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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“…Here, the optimization dynamic equation in the metaheuristics algorithm, close to the scheduling QoS (Quality of Service) model, is constructed from various QoS metrics, such as some technology or economy indexes. However, most of the existing QoS metrics related to the energyefficiencies, are quantified via fuzzy estimation [7] [8] or approximate linear mathematical models [9] [10] ; even some other equations are only the "electricity-price optimizers" [11] [12] .…”

Section: A Background and Motivationmentioning

confidence: 99%

A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion

et al. 2021

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“…Here, inspired by Darwin's natural theory or the biological immunity principles, genetic algorithms (GAs) or the artificial immune algorithms iteratively search the solution space by the meta-heuristics, with encoding/decoding the biomimetic individuals (candidate solutions) and the dynamics equation for the evolutionary mechanism [10,11] . Especially for heterogeneous scheduling, the evolutionary dynamics equations, are constructed based on the appropriate definitions of various QoS (Quality of Service) metrics; further, green heterogeneous scheduling aims for the higher energy-efficiencies with no effect on computing performance [12] .…”

Section: A Background and Motivationmentioning

confidence: 99%

An Algorithm Incarnating Deep Integration of Hardware-Software Energy Regulation Principles for Heterogeneous Green Scheduling

Liu

Gong

et al. 2020

IEEE Access

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Heterogeneous green scheduling in virtual cloud is an urgent need of human sustainable developments. However, on the one hand, there is still considerable space beyond reach of the hardware energy regulation mode; on the other hand, as the core of green software methods, meta-heuristics algorithms are still underperforming in heterogeneous scheduling, although with many achievements in homogeneous scheduling. In this paper, an efficient new meta-heuristics algorithm is presented (i.e., GHSA_di), including the co-evolutionary dynamics equation emphasizing on and taking advantage of the hardware energy-regulation principles. The experimental results show that compared with the other three meta-heuristic scheduling algorithms, GHSA_di algorithm has obvious advantages in overall performance, energy saving and scalability, for both data intensive and computing intensive instances.

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Learning-guided nondominated sorting genetic algorithm II for multi-objective satellite range scheduling problem

Cited by 35 publications

References 22 publications

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion

An Algorithm Incarnating Deep Integration of Hardware-Software Energy Regulation Principles for Heterogeneous Green Scheduling

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