Search Results - (( data optimization based algorithm ) OR ( parameter communication system algorithm ))
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1
Efficient and scalable ant colony optimization based WSN routing protocol for IoT
Published 2020“…In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. …”
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2
Mobility management schemes based on multiple criteria for optimization of seamless handover in long term evolution networks
Published 2014“…To support mobility to a user equipment (UE) it needs to be handed over to a new eNB (evolved node base-station) while still maintaining connectivity with the network at high data rates. …”
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3
Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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5
Ant system with heuristics for capacitated vehicle routing problem
Published 2013“…Specifically, the proposed methodology in this study is called Ant System with Heuristics (ASH) and it was developed based on the first ACO metaheuristic, known as Ant System (AS). …”
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6
Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
Published 2024“…Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. …”
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7
Optimization of supply chain management by simulation based RFID with XBEE Network
Published 2015“…In order to solve this problem, a simulation based “Multi-Colony Global Particle Swarm Optimization (MC-GPSO)” algorithm was developed. …”
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8
Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
Published 2020“…We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. …”
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9
Mobile data gathering algorithms for wireless sensor networks
Published 2014“…In this research, a Mobile Data Gathering based Network Layout (MDG-NL) algorithm is proposed. …”
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10
Enhancing quality of service in Multi-Radio Multi-Channel Wireless Mesh Networks
Published 2023“…The COngestion Reduction Routing Algorithm (CORRA) tackles this challenge by evaluating link quality and interference to find optimal data paths. …”
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11
Mobility management for seamless handover in carrier aggregation heterogeneous networks deployment scenario of long term evolution-advanced
Published 2018“…Secondly, a Hybrid Handover Parameters Optimization algorithm based on Enhanced Weight Performance (HHPO) is introduced to optimize, select suitable Handover Control Parameters (HCP) and to manage the conflict that may occur among self-optimization functions. …”
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12
Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks
Published 2025thesis::doctoral thesis -
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Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi
Published 2015“…The parallel manipulator is optimized based on the performance indices to obtain on the optimal design parameters for achieved maximum performance of the parallel manipulator. …”
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14
Resourceful residual energy consumption in TDMA scheduling for IoT-based wireless sensor network
Published 2019“…This paper discusses the optimization of energy-topology (E-T) factors for distributed time division multiplexing algorithm (TDMA) slot scheduling for high-speed data link capacity. …”
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15
Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks
Published 2013“…It reduces the number of handovers by 29.7% and 26.9%, respectively, compared to the conventional RSSI based handover algorithm and the previous worked, mobility improved handover (MIHO) algorithm. …”
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16
Distributed joint power control, beamforming and spectrum leasing for cognitive two-way relay networks
Published 2017“…A search method with numerous advantages over conventional algorithms, has been designed to solve the optimization problems with an enhanced global optimality and convergence speed. …”
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17
Green anaconda optimization based energy aware clustering protocol for 6G wireless communication systems
Published 2023“…Each SN is located in particular area and wireless system process via self-organizing. Still, data communication among nodes in a potential way is impossible because of diferent complicated factors, namely wireless links, limited energy, battery operation, and so on. …”
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Detection of black hole nodes in mobile ad hoc network using hybrid trustworthiness and energy consumption techniques
Published 2017“…In this thesis, a hybrid detection algorithm mechanism has been proposed which combines two detection algorithms based on nodes’ trustworthiness and energy consumption in a parallel manner in order to detect the black hole nodes. …”
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19
Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines
Published 2020“…Other system parameter variations are 4 different fault resistances (0.001 Ω, 10 Ω, 50 Ω, 100 Ω), and two inception angles (0 oC and 30 oC). …”
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20
Green network planning and operational power consumption optimization in LTE-A using artificial intelligence
Published 2015“…The second stage of the developed algorithm optimizes the average number of relay station per base station and their distance from the respective base station to meet the capacity constraint of the network operator. …”
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