Search Results - (( using function methods algorithm ) OR ( user optimization model algorithm ))
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1
A firefly algorithm based hybrid method for structural topology optimization
Published 2020“…The proposed method was validated using two-dimensional benchmark problems and the results were compared with results using the OC method. …”
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DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS
Published 2012“…The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. …”
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Optimal demand response of solar energy generation using Genetic Algorithm / Muhammad Asyraaf Adlan
Published 2025“…The aim of this study is to optimize the demand response of solar energy generation using Genetic Algorithm (GA) to minimize the daily yield loss caused by load shedding. …”
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HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
Published 2018“…To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. …”
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Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH
Published 2021“…A hybrid population – based stochastic optimization method named MVMO-SH algorithm is proposed to optimize PVDG locations and sizes in the grid system network. …”
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Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach
Published 2022“…The result indicates the performance of the optimized model outperforms the baseline model.…”
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3D virtual modelling and stabilization control of triple links inverted pendulum on two-wheeled system using enhanced interval type-2 fuzzy logic control
Published 2020“…Two optimization algorithms are presented in this work which are Spiral Dynamic Algorithm (SDA) and Particle Swarm Optimization (PSO). …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks
Published 2025thesis::doctoral thesis -
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An intelligent framework for modelling and active vibration control of flexible structures
Published 2004“…The second controller design strategy is based on a cost function optimization using GAS. This approach bypasses modelling of the plant and results in direct estimation of the controller characteristics. …”
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10
Batch Scheduling Using Matrix Approach under Demand Fluctuation
Published 2010“…One of the state-of-the-art scheduling approaches is the application of simplitied matrix modelling from sophisticated time algorithms. However, this method does not address the realistic scenario of fluctuating product demand from clients. …”
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Distributed power control and beamforming for cognitive two-way relay networks using a game-theoretic approach
Published 2016“…The strategic competition among multiple decision makers is modeled as a non-cooperative game where each secondary user (SU) acts selfishly in the sense of maximizing its own utility. …”
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Modelling transmission dynamics of covid-19 during Pre-vaccination period in Malaysia: a predictive guiseird model using streamlit
Published 2023“…The mathematical model is solved using Scipy odeint function, which uses Livermore Solver for Ordinary Differential Equations with an Automatic method switching (LSODA) algorithm. …”
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Proceeding Paper -
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A Compromise Programming to Task Assignment Problem in Software Development Project
Published 2021“…The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents' fitness function. …”
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Modelling of home energy management system using heuristic optimization for sustainable energy utilization
Published 2023“…To solve the MF constraint of FLC, an improved particle search optimization (PSO) algorithm is proposed for HEMS to determine the optimal schedule operation of home devices at specific times of the day. …”
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Model predictive control based on Lyapunov function and near state vector selection of four-leg inverter / Abdul Mannan Dadu
Published 2018“…This dissertation has focused on Lyapunov model predictive control (L-MPC) methods, in which Lyapunov control law is employed in the cost function to minimize the error between the desired control variables and the actual control variables of a three-phase four-leg inverter to optimize closed-loop system performance. …”
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Study of hand gesture recognition using impulse radio ultra wideband (IRUWB) radar sensor
Published 2023“…Second objective is to develop and assess hand gesture recognition models using seven different classifiers to achieve accurate and reliable recognition of hand gestures. …”
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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model
Published 2023“…The Transformer model with transferred weights outperformed models trained from scratch using supervised learning. …”
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Graphical user interface test case generation for android apps using Q-learning / Husam N. S. Yasin
Published 2021“…Instead of randomly selecting the inputs, the test generator learns how to act in an optimal way that explores new states by using new actions to gain more rewards. …”
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