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A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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2
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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3
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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4
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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5
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2022“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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6
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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7
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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8
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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9
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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10
Multi agent quality of service routing based on scheme ant colony optimization algorithm
Published 2014“…The proposed scheme has been simulated by OMNET++ and compared with standard AntNet and two well-known standard QoS routings; Widest Shortest Path (WSP) algorithm and Shortest Widest Path (SWP) algorithm. …”
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Thesis -
11
Optimised multi-robot path planning via smooth trajectory generation
Published 2024“…Particle swarm optimization (PSO) outperforms conventional methods like artificial potential fields (APF), the Dijkstra algorithm, and the A* algorithm in path planning for mobile robots. …”
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Final Year Project / Dissertation / Thesis -
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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Adaptive manet OLSR routing protocol for optimal route selection in high dynamic network
Published 2020“…The proposed Quality of Path QoP metric is incorporated into the modified relay selection algorithm, which improves the efficiency of relay selection mechanism to find optimal path with high link quality. …”
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Comparison of DSR, AODV and DSDV routing protocols in mobile ad-hoc networks: A survey
Published 2014“…A mobile ad hoc network (MANET) is a collection of mobile nodes that is connected through a wireless medium forming rapidly changing topologies.Mantes are infrastructure less and can be set up anytime, anywhere.We have conducted survey of protocol properties of various MANET routing algorithms and analyzed them.The routing algorithms considered are classified into three categories proactive (table driven) , reactive (on demand) and Hybrid protocol.The algorithms considered are Dynamic Source Routing (DSR), Ad-hoc On-Demand Distance Vector Routing (AODV) and Destination sequence Vector (DSDV) have been proposed to solve the multi hop routing problem in Ad-hoc networks.The comparison among three routing protocols are based on the various protocol property parameters such as Routing overhead, packet delivery ratio, end-to-end delay, path optimality, and throughput are some metrics commonly used in the comparisons.…”
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Conference or Workshop Item -
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Artificial Bee Colony for Minimizing the Energy Consumption in Mobile Ad Hoc Network
Published 2019“…As such, it can be concluded that optimizing the path from the source to the destination has a significant impact on the quality of the network performance. …”
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Search algorithms for path planning problems in harsh wireless sensor network environment
Published 2017“…The three addressed problems are 1) long initial computation delay in dynamic environment, 2) slow reaction towards rapid change of goal points and massive number of node depending on single source of computational power, and 3) tradeoffs between optimality and computational effort to achieve optimality in TSP path planning problem. …”
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Application of the Bees Algorithm to find optimal drill path sequence
Published 2024“…The main finding of the study is that the Bees Algorithm found optimal drill path length and minimum machining time comparable to the results of the other algorithms for the 5 × 5, 7 × 7 and 9 × 9 problems. …”
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Proceeding Paper -
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Performance comparisons between PID and adaptive PID controllers for travel angle control of a bench-top helicopter
Published 2015“…Adaptive method used in this project is self-tuning controller, which controller’s parameters are updated online. Two adaptive algorithms those are pole-placement and deadbeat have been chosen as the method to achieve optimal controller’s parameters. …”
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Autonomous mobile robots path planning with integrative edge cloud-based ant colony optimization
Published 2025“…However, traditional Ant Colony Optimization (ACO) algorithms, widely used for AMR path planning, exhibit limitations such as low convergence rates and redundant recalculations, particularly in environments with frequently changing obstacles. …”
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