Search Results - (( using (evolutionary OR evolution) sensor algorithm ) OR ( loading optimization bat algorithm ))

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  1. 1
    Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm

    Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm by Vincent Chung, Hamzarul Alif Hamzah, Norah Tuah, Kit, Guan Lim, Min, Keng Tan, Kenneth Tze Kin Teo

    Published 2020
    “…Existing approaches for this optimization problem have several drawbacks, including non-adaptive network configuration that may cause premature death of sensor nodes. Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. …”
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  2. 2
    Fuzzy Systems and Bat Algorithm for Exergy Modeling in a Gas Turbine Generator

    Fuzzy Systems and Bat Algorithm for Exergy Modeling in a Gas Turbine Generator by Alemu Lemma, Tamiru, Mohd Hashim, Fakhruldin

    Published 2011
    “…The models cover part load as well as full load operating conditions. The fuzzy models are trained applying locally linear model tree algorithm followed by a meta-heuristic nature inspired algorithm called bat algorithm. …”
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  3. 3
    Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building

    Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2023
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  4. 4
    Frequency stabilization in interconnected power system using bat and harmony search algorithm with coordinated controllers

    Frequency stabilization in interconnected power system using bat and harmony search algorithm with coordinated controllers by K., Peddakapu, M. R., Mohamed, P., Srinivasarao, P.K, Leung

    Published 2021
    “…To enhance the outcome of the proposed 2DOF–TIDN controller, its gain parameters are optimized with the use of a newly designed hybrid bat algorithm-harmony search algorithm (hybrid BA–HSA) technique. …”
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  5. 5
    Design, optimization and fabrication of a climbing six articulated-wheeled robot using artificial evolution and 3D printing

    Design, optimization and fabrication of a climbing six articulated-wheeled robot using artificial evolution and 3D printing by Lim, Shun Hoe, Teo, Jason Tze Wi

    Published 2015
    “…In this research, an artificial evolution approach utilizing Single-Objective Evolutionary Algorithm (SOEA) and Multi-Objective Evolutionary Algorithm (MOEA) respectively are investigated in the automatic design and optimization of the morphology of a Six Articulated-Wheeled Robot (SAWR) with climbing ability. …”
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  6. 6
    Automatic design, optimization and In-Situ Fabrication of Heterogeneous Swarm Robot bodies using 3-D printing and multi-objective evolutionary algorithms

    Automatic design, optimization and In-Situ Fabrication of Heterogeneous Swarm Robot bodies using 3-D printing and multi-objective evolutionary algorithms by Teo, Jason Tze Wi, Johnny Koh, Chin, Kim On, Chua, Bih Lii, Willey Liew, Noor Ajian Mohd. Lair, Lim, Shun Hoe

    Published 2012
    “…In this research, an artificial evolution approach utilizing Single-Objective Evolutionary Algorithm (SOEA) and Multi-Objective Evolutionary Algorithm (MOEA) respectively are investigated in the automatic design and optimization of the morphology of a Six Articulated-Wheeled Robot (SAWR) with climbing ability. …”
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  7. 7
    Assessment of energy storage and renewable energy sources-based two-area microgrid system using optimized fractional order controllers

    Assessment of energy storage and renewable energy sources-based two-area microgrid system using optimized fractional order controllers by Peddakapu, K., Mohd Rusllim, Mohamed, Srinivasarao, P., Arya, Yogendra

    Published 2024
    “…Simulation results reveal that the AOA-based CFOID-FOPIDN outperforms other existing algorithms, such as particle swarm optimization (PSO), bat algorithm (BAT), moth flame optimization (MFO), and whale optimization algorithm (WOA). …”
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  8. 8
    Efficient transmission based on genetic evolutionary algorithm

    Efficient transmission based on genetic evolutionary algorithm by Jin Fan, Kit Guan Lim, Helen Sin Ee Chuo, Min Keng Tan, Ali Farzamnia, Kenneth Tze Kin Teo

    Published 2022
    “…Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. …”
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  9. 9
    Quantum Inspired Computational Intelligence Techniques for Combined Economic Emission Dispatch Problem

    Quantum Inspired Computational Intelligence Techniques for Combined Economic Emission Dispatch Problem by MAHDI, FAHAD PARVEZ

    Published 2017
    “…In this research work, we at first separately optimize ELD and emission dispatch problem using particle swarm optimization (PSO), quantum-behaved bat algorithm (QBA) and quantum particle swarm optimization (QPSO) for different number of units. …”
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  10. 10
    Evolution strategy for collaborative beamforming in wireless sensor networks

    Evolution strategy for collaborative beamforming in wireless sensor networks by Wong, Chen How

    Published 2013
    “…An iterative algorithm using evolution strategy (ES) is proposed to achieve phase alignment at the intended location in static channels, which require one-bit feedback from the receiver destination. …”
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  11. 11
    Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm

    Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm by Banadkooki F.B., Ehteram M., Ahmed A.N., Teo F.Y., Ebrahimi M., Fai C.M., Huang Y.F., El-Shafie A.

    Published 2023
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  12. 12
    Sensor Node Placement in Wireless Sensor Network Using Multi-objective Territorial Predator Scent Marking Algorithm

    Sensor Node Placement in Wireless Sensor Network Using Multi-objective Territorial Predator Scent Marking Algorithm by Zainol Abidin H., Din N.M., Yassin I.M., Omar H.A., Radzi N.A.M., Sadon S.K.

    Published 2023
    “…Simulation results show that WSN deployed using the MOTPSMA sensor node placement algorithm outperforms the performance of the other two algorithms in terms of coverage, connectivity and energy usage. © 2014 King Fahd University of Petroleum and Minerals.…”
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  13. 13
    Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

    Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction by Ehteram M., Ahmed A.N., Latif S.D., Huang Y.F., Alizamir M., Kisi O., Mert C., El-Shafie A.

    Published 2023
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  14. 14
    WSN sensor node placement approach based on multi-objective optimization

    WSN sensor node placement approach based on multi-objective optimization by Abidin H.Z., Din N.M., Radzi N.A.M.

    Published 2023
    “…A sensor node placement algorithm that utilizes Multi-objective Territorial Predator Scent Marking Algorithm (MOTPSMA) is presented in this paper. …”
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  15. 15
    A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles

    A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles by Abdulhasan Al-Jarah, Ali Husam

    Published 2017
    “…These wireless sensor nodes has been used in many applications at many areas. …”
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  16. 16
    Multi-sensor fusion based on multiple classifier systems for human activity identification

    Multi-sensor fusion based on multiple classifier systems for human activity identification by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Alo, Uzoma Rita, Al-garadi, Mohammed Ali

    Published 2019
    “…The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. …”
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  17. 17
    Predicting Petroleum Reservoir Properties from Downhole Sensor Data using an Ensemble Model of Neural Networks

    Predicting Petroleum Reservoir Properties from Downhole Sensor Data using an Ensemble Model of Neural Networks by Fatai Adesina, Anifowose, Jane, Labadin, Abdulazeez, Abdulraheem

    Published 2013
    “…One of such is the difficulty in determining the most suitable learning algorithm for optimal model performance. To save the cost, effort and time involved in the use of trial-and-error and evolutionary methods, this paper presents an ensemble model of ANN that combines the diverse performances of seven "weak" learning algorithms to evolve an ensemble solution in the prediction of porosity and permeability of petroleum reservoirs. …”
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  18. 18
    Fuzzy logic controller optimized by MABSA for DC servo motor on physical experiment

    Fuzzy logic controller optimized by MABSA for DC servo motor on physical experiment by Nurainaa, Elias, Nafrizuan, Mat Yahya

    Published 2022
    “…Both speed and position cannot be balanced when loading and unloading materials. Therefore, the fuzzy logic controller will be designed using the Matlab toolbox and then will be optimized by the modified adaptive bats sonar algorithm (MABSA) to solve this problem. …”
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  19. 19
    User authentication for mobile devices using motion sensors based on Moore’s neighborhood / Muhammad Asyraf Akop

    User authentication for mobile devices using motion sensors based on Moore’s neighborhood / Muhammad Asyraf Akop by Akop, Muhammad Asyraf

    Published 2017
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  20. 20
    Evoluation strategy for collaborative beamforming in wireless sensor networks

    Evoluation strategy for collaborative beamforming in wireless sensor networks by Wong, Chen How

    Published 2013
    “…An iterative algorithm using evolution strategy (ES) is proposed to achieve phase alignment at the intended location in static channels, which require one-bit feedback from the receiver destination. …”
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