Search Results - (( evolution classification using algorithm ) OR ( problem implementation swarm algorithm ))
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Artificial fish swarm optimization for multilayer network learning in classification problems
Published 2012“…Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN.In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems.The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems
Published 2012“…Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN. …”
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Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…In this regard, this thesis proposes five FS methods for efficient EMG signals classification. The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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Thesis -
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Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
Published 2024“…Additionally, the proposed Hybrid Differential Evolution Algorithm was implemented into the training phase to ensure that the cost function of the Discrete Hopfield Neural Network is minimized. …”
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Implementation of swarm intelligence algorithms on mobile robots
Published 2017“…This thesis focusses on the implementation of swarm intelligence algorithms on multiple mobile robots. …”
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Undergraduates Project Papers -
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Multi-State Particle Swarm Optimization for Discrete Combinatorial Optimization Problem
Published 2014“…The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are proposed to solve discrete optimization problems. …”
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HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION
Published 2023“…In this paper, we study the advantages of fusing the Meta-Heuristic Bat Algorithm with Heuristic Optimization. We have implemented the Meta- Heuristic Bat Algorithm and tested it on a heterogeneous swarm. …”
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Optimal path planning algorithm for swarm robots using bat algorithm with mutation (bam)
Published 2022“…However, there is still room for improvement such as implementing the obstacle avoidance algorithm into swarm robot. …”
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Undergraduates Project Papers -
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Pressure vessel design simulation: Implementing of multi-swarm particle swarm optimization
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Optimization of multi-holes drilling path using particle swarm optimization
Published 2022“…This research aims to model and optimise multi-hole drilling problems using Particle Swarm Optimisation (PSO) algorithm. …”
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Dynamic parameterizations of particle swarm optimization and genetic algorithm for facility layout problem
Published 2017“…Surrounded by an assortment of intelligent and efficient search entities, the hybridization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are proven to be a comprehensive tool for solving different kinds of optimization problems due to their contradictive working approaches. …”
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An improved gbln-pso algorithm for indoor localization problem in wireless sensor network
Published 2022“…To achieve the stated aims, we implemented an Improved Global best Local Neigborhood Particle Swarm Optimization (IGbLN-PSO) algorithm. …”
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An improved particle swarm optimization based on lévy flight and simulated annealing for high dimensional optimization problem
Published 2022“…Particle swarm optimization (PSO) is a simple metaheuristic method to implement with robust performance. …”
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A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
Published 2020“…The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. …”
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Studying the Effect of Training Levenberg Marquardt Neural Network by Using Hybrid Meta-Heuristic Algorithms
Published 2016“…This paper proposes an accelerated particle swarm optimization (APSO) is implemented in conjunction with Levenberg Marquardt back propagation (LMBP) algorithms to achieve faster convergence rate and to avoid local minima problem. …”
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The fusion of particle swarm optimization (PSO) and interior point method (IPM) as cooperative movement control algorithm in Swarm Robotics / Dada Emmanuel Gbenga
Published 2016“…Finally, we used our hybrid algorithms (pdPSO and pdAPSO) to solve the flocking and pattern formation problem in swarm robotics. …”
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Optimal location and size estimation of distributed generators by employing grouping particle swarm optimization and grouping genetic algorithm
Published 2017“…These two algorithms are compared to their original artificial intelligence algorithms, i.e. particle swarm optimization algorithm and genetic algorithm. …”
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