A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation

Zhao, Luda; Wang, Bin; Shen, Congyong

doi:10.1371/journal.pone.0252293

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…Tang 17 , et al have conceived functions (monitoring, scheduling) as microservices for data sharing among aircraft nodes. Various aspects of task scheduling in different warfare scenarios, such as operational coordination in combat clusters 18 , crossplatform task scheduling in warship networks 19 and route finding for information transmission in mobile ad hoc networks formed using UAV 20 are under study. There are also hybrid solutions 20 of autonomous intelligent combat systems for C2, including manned and unmanned systems.…”

Section: Proposed Solutionmentioning

confidence: 99%

Task Scheduling in Fog Node within the Tactical Cloud

Nair

Bhanu

2022

Def. Sc. J.

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Fog computing architecture competent to support the mission-oriented network-centric warfare provides the framework for a tactical cloud in this work. The tactical cloud becomes situation-aware of the war from the information relayed by fog nodes (FNs) on the battlefield. This work aims to sustain the network of FNs by maintaining the operational efficiency of the FNs on the battlefield at the tactical edge. The proposed solution monitors and predicts the likely overloading of an FN using the long short-term memory model through a buddy FN at the fog server (FS). This paper also proposes randomised task scheduling (RTS) algorithm to avert the likely overloading of an FN by pre-empting tasks from the FN and scheduling them to another FN. The experimental results demonstrate that RTS with linear complexity has a schedulability measure 8% - 26% higher than that of other base scheduling algorithms. The results show that the LSTM model has low mean absolute error compared to other time-series forecasting models.

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Section: Proposed Solutionmentioning

confidence: 99%

Task Scheduling in Fog Node within the Tactical Cloud

Nair

Bhanu

2022

Def. Sc. J.

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“…The centralized mission planning structure requires using a master controller as the mission’s decision-making center, and it is responsible for scheduling the tasks of multi-satellites [ 4 , 5 , 6 ]. Zhao et al proposed an improved bat algorithm to solve large-scale multi-objective combinatorial optimization problems [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An LEO Constellation Early Warning System Decision-Making Method Based on Hierarchical Reinforcement Learning

Cheng

Wei

Sun

et al. 2023

Sensors

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The cooperative positioning problem of hypersonic vehicles regarding LEO constellations is the focus of this research study on space-based early warning systems. A hypersonic vehicle is highly maneuverable, and its trajectory is uncertain. New challenges are posed for the cooperative positioning capability of the constellation. In recent years, breakthroughs in artificial intelligence technology have provided new avenues for collaborative multi-satellite intelligent autonomous decision-making technology. This paper addresses the problem of multi-satellite cooperative geometric positioning for hypersonic glide vehicles (HGVs) by the LEO-constellation-tracking system. To exploit the inherent advantages of hierarchical reinforcement learning in intelligent decision making while satisfying the constraints of cooperative observations, an autonomous intelligent decision-making algorithm for satellites that incorporates a hierarchical proximal policy optimization with random hill climbing (MAPPO-RHC) is designed. On the one hand, hierarchical decision making is used to reduce the solution space; on the other hand, it is used to maximize the global reward and to uniformly distribute satellite resources. The single-satellite local search method improves the capability of the decision-making algorithm to search the solution space based on the decision-making results of the hierarchical proximal policy-optimization algorithm, combining both random hill climbing and heuristic methods. Finally, the MAPPO-RHC algorithm’s coverage and positioning accuracy performance is simulated and analyzed in two different scenarios and compared with four intelligent satellite decision-making algorithms that have been studied in recent years. From the simulation results, the decision-making results of the MAPPO-RHC algorithm can obtain more balanced resource allocations and higher geometric positioning accuracy. Thus, it is concluded that the MAPPO-RHC algorithm provides a feasible solution for the real-time decision-making problem of the LEO constellation early warning system.

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A hybrid multi-objective bi-level interactive fuzzy programming method for solving ECM-DWTA problem

Zhao

Wang

et al. 2022

Complex Intell. Syst.

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Electronic countermeasure (ECM) has become one of the most significant factors in modern warfare, in the course of combat, the electronic jamming allocation tasks need to be flexibly adjusted with the change of combat stage, which puts forward higher requirements for the modeling and solution method of this kind of problems. To solve the ECM dynamic weapon target assignment (ECM-DWTA) problem, a hybrid multi-target bi-level programming model is established. The upper level takes the sum of the electronic jamming effects in the whole combat stage as an optimization objective, and locally optimizes the ECM weapon (ECM-WP) assignment scheme in each stage. The lower level takes the importance expectation value of the target subjected to interference and combat consumption as double optimization objectives to globally optimize the ECM-WP assignment scheme. Focus on solving this complex model, a hybrid multi-objective bi-level interactive fuzzy programming algorithm (HMOBIF) is proposed, in this method, exponential membership function is used to describe the satisfaction degree of each level. When solving the multi-objective optimization problem composed of membership functions in the upper and lower levels, we use the MOEA/D algorithm to obtain the Pareto Front (PF) solution set, and then each solution in PF is evaluated and selected by the TOPSIS multi-criteria evaluation method. This local and global interactive optimization process of bi-level model is actually the process of executing observation-orientation-decision-action loop in practical combat. According to the current example, we conduct numerical simulation on the parameters in the model and obtain the parameter values suitable for the model solution. The computational experiments on different scale ECM-DWTA problems show that HMOBIF method is superior to four bi-level programming algorithms in terms of performance index, and can better solve ECM-DWTA problems.

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A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation

Cited by 5 publications

References 56 publications

Task Scheduling in Fog Node within the Tactical Cloud

Task Scheduling in Fog Node within the Tactical Cloud

An LEO Constellation Early Warning System Decision-Making Method Based on Hierarchical Reinforcement Learning

A hybrid multi-objective bi-level interactive fuzzy programming method for solving ECM-DWTA problem

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