Membrane Computing to Enhance Time Efficiency of Minimum Dominating Set

Mahmood, Ali Abdulkareem; Maroosi, Ali; Muniyandi, Ravie Chandren

doi:10.1007/s11786-016-0261-5

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…An efficient membrane algorithm is created based on the membrane computing paradigm to address optimization control issues in several application domains. Robot path optimization and controller optimization issues are also addressed by this optimization approach, which combines membrane structure and computation techniques to handle SAT, satisfiability, HPP function optimization, and other issues in Reference [6][7][8][9][10][11][12][13] . Meanwhile, membrane computing is becoming a current research hotspot as it steadily advances toward ground-breaking discoveries in the field of optimal control.…”

Section: Introductionmentioning

confidence: 99%

Attitude optimization of unmanned helicopters based on spike neural membrane computing

Xu,

Yu,

Hong

et al. 2023

Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023)

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The effectiveness of inspection tasks performed by unmanned helicopters during underground inspections is directly influenced by the performance of attitude control. Therefore, it is crucial to optimize the attitude control of unmanned helicopters. Spike neural membrane computing offers Turing computing versatility and high efficiency in various time collaborations. This paper presents a study on attitude optimization control of unmanned helicopters in underground coal mines using spike neural network computing. The approach involves constructing an extended spike neural membrane system based on the attitude dynamics model of an unmanned helicopter, with the aim of further optimizing the system and algorithm. The experimental results demonstrate that the optimized spike neural membrane system is capable of effectively achieving attitude control of the unmanned helicopter under normal conditions. Furthermore, it can successfully maintain the attitude angle within the desired range, even in the presence of wind interference. Additionally, a comparison with traditional sliding mode control and the Designed Optimization Model (OSNMS) reveals that the OSNMS proposed in this paper significantly enhances the attitude control performance of unmanned helicopters.

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Section: Introductionmentioning

confidence: 99%

Attitude optimization of unmanned helicopters based on spike neural membrane computing

Xu,

Yu,

Hong

et al. 2023

Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023)

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“…Research shows that the some models of P systems present the same computing power as Turing machines and is more efficient to some extent [ 3 ]. Therefore, the analysis of computing power and computational efficiency of P systems is one of the important basic studies [ 4 , 5 ]. Other studies are focused on the variation of P systems to solve optimization problems, including the variant of rules and structures [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation

Liu

Wang

et al. 2020

Computational Intelligence and Neuroscience

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A new clustering membrane system using a complex chained P system (CCP) based on evolutionary mechanism is designed, developed, implemented, and tested. The purpose of CCP is to solve clustering problems. In CCP, two kinds of evolution rules in different chained membranes are used to enhance the global search ability. The first kind of evolution rules using traditional and modified particle swarm optimization (PSO) clustering techniques are used to evolve the objects. Another based on differential evolution (DE) is introduced to further improve the global search ability. The communication rules are adopted to accelerate the convergence and avoid prematurity. Under the control of evolution-communication mechanism, the CCP can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This proposed CCP is compared with four existing PSO clustering approaches on eight real-life datasets to verify the validity. The computational results on tested images also clearly show the effectiveness of CCP in solving image segmentation problems.

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