Energy and spectrum efficient mobility-aware resource management for D2D multicasting

Ghahfarokhi, Behrouz Shahgholi; Azadmanesh, Maryam; Khorasani, Samaneh Khadem

doi:10.1016/j.comnet.2018.09.013

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…In the category of knapsack problems, the problems such as search based requirements selection [94], RAP [95], [96], optimal selection of safety measures in oil and gas facilities [97] and multi/many objective knapsack problem [98], [99], [100], [101], [102], [103], [104] are included.…”

Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

“…The researchers of [96] exploited NSGA-II to solve the multi-objective resource management problem of a device to device (D2D) multicasting, a knapsack problem concerned with the channel/power allocation to D2D links in such a way that the spectrum and energy efficiency are optimized. The outer layer crossover and designed mutation were used as genetic operators for NSGA-II.…”

Section: F) Knapsack Problemmentioning

confidence: 99%

“…The most used performance metrics in the studied literature are given in Table X. The other rarely used performance metrics are maximum sum of the objective-values (MaxSum) [102], [99], maximum distance Dmax [175], [157], epsilon indicator [45], [134], average quality [157], [175], average hypervolume [105], [121], width measure (M2 metric) [147], system performance [148], solution quality [54], size of reference set [62], relative percentage difference between objective-values [210], relative and absolute quality [59], overall non-dominated vector generation (ONVG) [175], number of unscheduled tasks [163], number of generations [68], number of channel reuses, user level fairness, and stability of the communication mode of the D2D users [96], norm based pure diversity metric [72], modified mean ideal distance measure [127], maximum Pareto front error [201], M1,M2,M3 [110], k-distance [198], hole-relative size metric, µ distance metric [73], error, ratio, distance based measure, fairness [158], empirical attainment function [45], data envelopment analysis [83], data dependency threshold [68], convex hull of the approximated efficient frontier [59], convergent metric [140], capacity measure [72], average rank index, average crowding distance and the mapping pattern of solutions [89], convergent rate metric [85], infeasibility metric [85]…”

Section: ) Performance Measuresmentioning

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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