Search Results - (( developing initial function algorithm ) OR ( based based optimization algorithm ))
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1
Hybrid artificial immune system-genetic algorithm optimization based on mathematical test functions
Published 2023“…Secondly, a Genetic Algorithm is typically initializing population randomly. …”
Conference paper -
2
Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…However, the HKA algorithm has its own flaws. Although it was introduced as a population-based stochastic optimization algorithm, HKA is not exactly a population-based algorithm because it initializes and updates only a single solution. …”
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3
Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…So far, limited genetic-based clustering ensemble algorithms have been developed. …”
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4
New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems
Published 2024“…In the fourth phase, the newly developed algorithm undergoes testing on the formulated ROOPs and compared to several contemporary optimizer algorithms. …”
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5
Optimization of RFID network planning for monitoring railway mechanical defects based on gradient-based Cuckoo search algorithm
Published 2020“…In conclusion, this study presented a novel hybrid evolutionary algorithm based on the combination of AHP with GBCS to specify optimal RFID reader positions and amount based on the working train station domain. …”
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6
Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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7
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…This proposed algorithm is developed based on heuristic method named modified binary particle swarm optimization (MBPSO) with kernel fuzzy clustering method as a fitness function. …”
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8
A Novel Discrete Filled Function Algorithm in Solving Discrete Optimization Problems (S/O: 12408)
Published 2016“…Several global methods have been proposed for solving discrete optimization problems. We focus our study on a recently developed method known as discrete filled function method. …”
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Monograph -
9
Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method
Published 2024“…To address the existing theoretical gap, this study focused on developing an objective function that accurately estimates the initial root parameters of Photovoltaic (PV) models. …”
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10
A modified flower pollination algorithm and carnivorous plant algorithm for solving engineering optimization problem
Published 2021“…In this study, two metaheuristic algorithms, namely, modified flower pollination algorithm (MFPA) and carnivorous plant algorithm (CPA), were proposed. …”
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11
Optimization of an extended H-infinity controller for unmanned helicopter control using multiobjective differential evolution (MODE)
Published 2015“…The problem of population initialization and objectives function computation in the conventional MODE algorithm are addressed to ensure suitability of the optimization algorithm in the formulated H-infinity controller synthesis.…”
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12
Harmony search-based robust optimal controller with prior defined structure
Published 2013“…The second level compnses of two HS optimization algorithms developed and coded for robust stability examinations. …”
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13
Modelling of multi-robot system for search and rescue
Published 2023“…One of the key aspects in multi-robot systems is the path planning problem, which involves finding collision-free paths for each robot to reach their respective destinations while optimizing various performance metrics. This report focusses on developing a novel multi-robot path planning algorithm based on the Modified Particles Swarm Optimization (MPSO) algorithm for dynamic environments. …”
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Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm
Published 2023“…Then, the TTA is developed before undergoing functionality tests by benchmarking with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA). …”
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15
Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems
Published 2015“…The WNMPC is developed by a proposed algorithm named adaptive updating rule (AUR) used with gradient descent optimization method to minimize a constrained cost function over the prediction and control horizons and to offer a robust control performances. …”
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16
Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis
Published 2023“…Finally, the genetic algorithm is utilized to predict the optimum level of design parameters based on the derived objective function. …”
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17
Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering
Published 2024“…Although the swmcan algorithm solves the noise problem in multi-view data, its initial and final graphs are independent and cannot learn from each other. …”
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18
Using genetic algorithms to optimise land use suitability
Published 2012“…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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19
PREDICTIVE ANALYTICS OF JUNCTIONLESS DOUBLE GATE STRAINED MOSFET USING GENETIC ALGORITHM WITH DOE-BASED GREY RELATIONAL ANALYSIS
Published 2024“…Finally, the genetic algorithm is utilized to predict the optimum level of design parameters based on the derived objective function. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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