Search Results - (( _ application ((bee algorithm) OR (bat algorithm)) ) OR ( some application swarm algorithm ))
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Review of Multi-Objective Swarm Intelligence Optimization Algorithms
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Application of adaptive bats sonar algorithm to minimise car side impact design
Published 2017“…This project was focusing on the modification of bats sonar algorithm (BSA) and renamed to adaptive bats sonar algorithm (ABSA) due to some limitations of previous algorithm. …”
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Undergraduates Project Papers -
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Performance evaluation of PID controller optimisation for wheel mobile robot using Bat based optimisation algorithm
Published 2022“…Three different optimization algorithms which are Bat Algorithm (BA), Bat Algorithm with Mutation (BAM) and Extended Bat Algorithm (EBA) are implemented to optimize the value of PID controller gain for wheel mobile robot. …”
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Book Chapter -
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Data Analysis using Particle Swarm Optimization Algorithm
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Final Year Project / Dissertation / Thesis -
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A novel swarm-based optimisation algorithm inspired by artificial neural glial network for autonomous robots
Published 2019“…According to [13], the two best-known swarm intelligence algorithms are Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO). …”
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Monograph -
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PID controller design for mobile robot using Bat Algorithm with Mutation (BAM)
Published 2019“…This algorithm is compared with a wellknown optimization algorithm, Particle Swarm Optimization (PSO). …”
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Conference or Workshop Item -
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Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification
Published 2017“…In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application. …”
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Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification
Published 2017“…By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks.Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima.Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues.Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. …”
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Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System
Published 2013“…Therefore, the usage of a swarming robotic system is proposed. In this thesis, a simple framework and methodology in developing a bio-inspired algorithm for cooperative swarming robotic application has been developed. …”
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Thesis -
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Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm
Published 2022“…This research will conduct comparison of hybrid Genetic Algorithm and Bat Algorithm (GA-BA) with Genetic Algorithm (GA) and Bat Algorithm (BA). …”
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Academic Exercise -
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HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION
Published 2023“…Swarm Intelligence is inspired by naturally occurring swarm systems suchas Ant Colony, Bees Hive, or Bats. The Bat Algorithm is a population-based meta-heuristic algorithm for solving continuous optimization problems. …”
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Pairwise Test Suite Generation Based on Hybrid Artificial Bee Colony Algorithm
Published 2020“…Complementing to the earlier researches, this paper proposes a new pairwise test suite generation called Pairwise Hybrid Artificial Bee Colony (PhABC) strategy based on hybridize of an Artificial Bee Colony (ABC) algorithm with a Particle Swarm Optimization (PSO) algorithm. …”
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Acoustic emission partial discharge localization in oil based on artificial bee colony
Published 2025“…Comparisons with the genetic algorithm (GA), particle swarm optimization (PSO) and bat algorithm (BA) revealed that the distance error, maximum deviation and computation time for AE PD localization based on ABC are the lowest. …”
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Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
Published 2015“…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
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