Search Results - (( developing fraction function algorithm ) OR ( based optimization techniques algorithm ))
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1
Feedforward neural network for solving particular fractional differential equations
Published 2024“…This research aims to develop a scheme based on a feedforward neural network (FNN) with a vectorized algorithm (FNNVA) for solving FDEs in the Caputo sense (FDEsC) using selected first-order optimization techniques: simple gradient descent (GD), momentum method (MM), and adaptive moment estimation method (Adam). …”
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Thesis -
2
Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations
Published 2024“…Initially, the algorithm utilized a truncated series. The values of the unknown coefficients in this truncated power series were then determined using an optimization technique to minimize the criterion function. …”
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3
A novel softsign fractional-order controller optimized by an intelligent nature-inspired algorithm for magnetic levitation control
Published 2025“…The performance of the proposed approach was extensively benchmarked against four modern metaheuristic algorithms (greater cane rat algorithm, catch fish optimization algorithm, RIME algorithm and artificial hummingbird algorithm) under identical conditions. …”
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An algorithm for the selection of planting lining technique towards optimizing land area: an algorithm for planting lining technique selection
Published 2012“…This algorithm solution generated the dataset based coordinates areas to analyze the techniques. …”
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5
Liquid slosh suppression by implementing data-driven fractional order pid controller based on marine predators algorithm
Published 2023“…Thus, this research paper proposed the development of a data-driven fractional-order PID controller based on marine predators algorithm (MPA) for liquid slosh suppression system. …”
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6
Artificial neural networks based optimization techniques: A review
Published 2023“…In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. …”
Review -
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Successor selection for Ant Colony Optimization technique algorithm / Muhammad Iskandar Isman
Published 2017“…ACO algorithm is the best solution because it included the optimization technique to optimized the result based on the data criteria needs. …”
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8
PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
Published 2020“…To evaluate the PMT’s performance and adaptability, the PMT was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. …”
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9
Optimizing optimal path trace back system for Smith-Waterman algorithm using structural modelling technique: article
Published 2012“…back system for Smith-Waterman Algorithm using Structural Modelling Technique. The objectives for this paper are to optimize the best trace back scanning performance and also to design the simple architecture in order to reduce the runtime. …”
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Liquid slosh suppression by implementing data-driven fractional order PID controller based on marine predators algorithm
Published 2023“…Thus, this research paper proposed the development of a data-driven fractional-order PID controller based on marine predators algorithm (MPA) for liquid slosh suppression system. …”
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11
LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting
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Comparative Investigation of Optimization Techniques for Photovoltaic Based Multilevel Inverter
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Final Year Project -
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
Published 2023Conference Paper -
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Computational intelligence based power tracing for nondiscriminatory losses charge allocation and voltage stability improvement. / Zulkifli Abdul Hamid
Published 2013“…At first, in producing a good optimization algorithm, a hybridization technique was proposed for adopting the finest features of two different algorithms; namely the Genetic Algorithm (GA) and continuous domain Ant Colony Optimization (ACOR). …”
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Hybrid evolutionary-dolphin echolocation programming for sizing optimization of stand-alone photovoltaic systems / Zulkifli Othman
Published 2021“…This thesis presents the “Hybrid Evolutionary-Dolphin Echolocation Programming (EDEP) for Sizing Optimization of Stand-Alone Photovoltaic Systems”. The objectives are 1) to formulate an iterative-based algorithm for sizing optimization of SAPV and (Hybrid Stand-Alone Photovoltaic) HSAPV systems, 2) to develop a hybrid EDEP technique for sizing optimization of SAPV and HSAPV systems and 3) to formulate a hybrid EDEP technique for determining optimal solar fraction in sizing optimization of SAPV and HSAPV system. …”
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19
Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…A new meta-heuristic optimization technique called the Slime Mould Algorithm (SMA) approach has a high convergence rate or a few iterations and superior optimization indices analyzed against other algorithms. …”
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