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Design and implementation of an optimal fuzzy logic controller using egentic algorithm
Published 2011“…The results justify the effectiveness of the approach implemented in this study and they lUnher prove toot the optimized Fuzzy Logic Controller is functioning better than the conventional PID Controller…”
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2
A new meta heuristic evolutionary programming (NMEP) in optimizing economic energy dispatch
Published 2016“…The proposed optimization algorithm, namely New Meta-Heuristic Evolutionary Programming (NMEP) algorithm is followed to Meta-Heuristic Evolutionary Programming (Meta-EP) approach with some modification where the cloning process embedded as a significant progress during the implementation. …”
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Design and implementation of an optimal fuzzy logic controller using genetic algorithm
Published 2008“…Said approach was first simulated using MATLAB/SIMULINK using the techniques of Proportional Derivative Fuzzy Logic Controller (PD-FLC) whose membership function, fuzzy logic rules and scaling gains were optimized by the genetic algorithm technique. …”
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The Determination of Pile Capacity Using Artificial Neural-net: An Optimization Approach
Published 2001“…Using the developed algorithm, the safety measures involved are such as reliability index and the probability of failure; instead of only factor of safety if conventional deterministic approach is used. …”
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Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman
Published 2025“…The research adopts a Genetic Algorithm-based approach, where examination scheduling details such as date, time, course code, program code, student group, student numbers, and available spaces are encoded as chromosomes. …”
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Application of Multi-objective Genetic Algorithm (MOGA) optimization in machining processes
Published 2020“…Multi-objectives Genetic Algorithm (MOGA) is one of many engineering optimization techniques, a guided random search method. …”
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7
An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications.
Published 2020“…Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. …”
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Optimal location and sizing of SVC using Particle Swarm Optimization technique
Published 2011“…This paper describes optimal location and sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. Particle Swarm Optimization (PSO) is population-based stochastic search algorithms approaches as the potential techniques to solving such a problem. …”
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Development of the cascading module for hydro energy decision support system (HEDSS) for Temenggor-Bersia-Kenering power plants
Published 2002“…A generalized network flow algorithm is used to find the optimal solution.…”
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10
Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
Published 2023“…By using Python in analytics projects, farmers can identify areas of concern, monitor crop health trends, and implement targeted interventions to optimize resource use and maximize yields. …”
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Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli
Published 2014“…Not much work had considered using a robust approach in improving the nonlinear autoregressive moving average inputs artificial neural network (NARMA-ANN) model - here comes the novelty of this system. …”
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Generalized Fibonacci search for optimization of unconstrained one-and two-dimensional unimodal functions
Published 2023“…Undeniably, optimization problems of smooth functions have been extensively investigated by many researchers with the use of calculus throughout the years. …”
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Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris
Published 2019“…This thesis presents multi-objective optimization approach in developing baseline energy using multi-objective Evolutionary Programming (EP). …”
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Environmentally constraint economic dispatch and reactive power planning for ensuring secure operation in power system / Elia Erwani Hassan
Published 2015“…These objective functions were first considered individually and then were combined to be one multi objective function using the weighted sum approach. …”
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15
Ensemble deep learning approach for apple fruitlet detection from digital images
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Asset liability management model: The case of selected Islamic banks in Malaysia / Chong Hui Ling
Published 2017“…The multi-faceted objectives consist of the bank‟s expected returns and risks tolerance with constraints (also known as restraining functions to reflect the limitations placed on the Islamic bank‟s operating requirements), established using computational mathematics and algorithms with the aid of the MATLAB programming software. …”
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Critical insight for MAPReduce optimization in Hadoop
Published 2014“…Apache Hadoop is one the most used cloud frame work for cloud infrastructure. The predominant philosophy behind Hadoop optimization is the optimization of MapReduce, which is a dominant programming platform effective in bringing a=bout many functional enhancements as per scheduling algorithms developed and implemented. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs
Published 2019“…The analysis showed significant differences for both the effectiveness, efficiency and APFDc of the ECRTP over existing approaches such as RanPrio, RevPrio, NonPrio, JaNaMa and EvolRTP, which make ECRTP a promising approach to use for regression testing. …”
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Logic Programming In Radial Basis Function Neural Networks
Published 2013“…The first technique is to encode the logic programming in radial basis function neural networks. …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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