A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization

2022 ◽  
Author(s):  
Qian-Long Dang ◽  
Wei Xu ◽  
Yang-Fei Yuan
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Dynamic Resource Allocation ◽  
Multi Objective Optimization ◽  
Allocation Strategy ◽  
Multi Objective ◽  
Resource Allocation Strategy ◽  
Dynamic Resource

scholarly journals Interference resilient stochastic prediction based dynamic resource allocation model for cognitive MANETs

2020 ◽  
Vol 13 (41) ◽  
pp. 4332-4350
Author(s):  
K Shashi Raj ◽  
Keyword(s):  
Resource Allocation ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Dynamic Resource Allocation ◽  
Allocation Strategy ◽  
Secondary Users ◽  
Allocation Model ◽  
Resource Allocation Strategy ◽  
Dynamic Resource ◽  
Mobile Ad Hoc

Background/Objectives: Being dynamic in nature, Mobile Ad-hoc Network (MANET) requires robust resource allocation strategy that can ensure both optimal transmission reliability and resource efficiency to meet Quality of Service (QoS) demands. The objective of this research is to address interference resilience requirement in MANETs which is must due to greedy nature of nodes especially when accessing resource or bandwidth and develop a highly robust stochastic prediction based resource allocation strategy. Methods: The proposed Interference Resilient Stochastic Prediction based Dynamic Resource Allocation model for Cognitive MANET (ISP-DRACM) intends to enable optimal resource allocation under interweave and underlay network setup with instantaneous as well as average interference conditions. It employs a joint power management and resource allocation strategy where it intends to maximize the weighted sum-rate of the secondary users under certain defined conditions like average power and stochastic interference level. Findings/Novelty: Inculcating resource allocation problem as controlled Markov Decision Process using Hidden Markov Model (HMM) and Lagrange relaxation, our proposed model achieves better resource allocation under limited noise or interference condition and hence achieves both costeffectiveness as well as QoS provision. This method has exhibited satisfactory performance towards spectrum allocation to the secondary users without imposing any significant interference for both interweave as well as underlay Cognitive Radio setup. Keywords: Cognitive mobile ad-hoc network; stochastic prediction; interference resilience; channel state information; dynamic resource allocation; underlay and overlay cognitive MANET

scholarly journals Developing a pathway-independent and full-autonomous global resource allocation strategy to dynamically switching phenotypic states

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Junjun Wu ◽  
Meijiao Bao ◽  
Xuguo Duan ◽  
Peng Zhou ◽  
Caiwen Chen ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Metabolic Flux ◽  
Dynamic Resource Allocation ◽  
Grand Challenge ◽  
Allocation Strategy ◽  
Chemical Production ◽  
Resource Allocation Strategy ◽  
Dynamic Resource ◽  
Cell Phenotypes ◽  
Host Genes

Abstract A grand challenge of biological chemical production is the competition between synthetic circuits and host genes for limited cellular resources. Quorum sensing (QS)-based dynamic pathway regulations provide a pathway-independent way to rebalance metabolic flux over the course of the fermentation. Most cases, however, these pathway-independent strategies only have capacity for a single QS circuit functional in one cell. Furthermore, current dynamic regulations mainly provide localized control of metabolic flux. Here, with the aid of engineering synthetic orthogonal quorum-related circuits and global mRNA decay, we report a pathway-independent dynamic resource allocation strategy, which allows us to independently controlling two different phenotypic states to globally redistribute cellular resources toward synthetic circuits. The strategy which could pathway-independently and globally self-regulate two desired cell phenotypes including growth and production phenotypes could totally eliminate the need for human supervision of the entire fermentation.

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