Search Results - (( evolution optimization testing algorithm ) OR ( parameter optimization based algorithm ))
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1
Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…A new self-adaptive hybrid algorithm (CSCMAES) is introduced for optimization. …”
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2
An improved method using fuzzy system based on hybrid boahs for phishing attack detection
Published 2022“…The algorithms involved were Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Optimization, Butterfly Optimization Algorithm, Teaching-Learning-Based Optimization Algorithm, Harmony Search Algorithm and Gravitational Search Algorithm. …”
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3
Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
Published 2023“…To show the effectiveness of EMA-DL, comparison studies were conducted among other metaheuristic optimizers that were also used to optimize the DL parameters viz, Particles Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) as well as the Adaptive Moment Estimation (ADAM). …”
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4
Voltage constrained optimal power flow based using genetic algorithm
Published 2015“…Effectiveness of the proposed method was tested on IEEE 30 bus system and it has been compared to other optimization of power fl ow using other methods, for example the Evolutionary Programming (EP), Differential Evolution (DE) and Particle Swarm Optimization (PSO) methods. …”
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5
Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio...
Published 2023“…Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. …”
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6
Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)
Published 2019“…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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7
Application of manta ray foraging optimization with gradient-based mutation (cMRFO) for solving power system problems
Published 2023“…In this paper, the Manta Ray Foraging Optimization (MRFO) algorithm is applied to solve real parameter constrained optimization problems, using the Gradient-based Mutation MRFO (cMRFO) variant. …”
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8
Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
Published 2018“…GP is a meta-heuristic search and optimization technique based on natural evolution. The control and optimization parameters in this study are tuned. …”
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9
Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems
Published 2022“…In this paper, a new variant of Manta Ray Foraging Optimization (MRFO) algorithm is introduced to deal with real parameter constrained optimization problem. …”
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10
A hybrid optimization technique for solving economic emission load dispatch problems
Published 2023text::Final Year Project -
11
VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.
Published 2012“…The analysis and comparison of VIGA-20 is based on convergence test against the results obtained from the Simple Genetic Algorithm. …”
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12
Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
Published 2023“…GP is a meta-heuristic search and optimization technique based on natural evolution. The control and optimization parameters in this study are tuned. …”
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13
A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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14
Parameter extraction of solar photovoltaic modules using penalty-based differential evolution
Published 2012“…This paper proposes a penalty based differential evolution (P-DE) for extracting the parameters of solar photovoltaic (PV) modules at different environmental conditions. …”
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15
A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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16
Novel farmland fertility algorithm based PIDPSS design for SMIB angular stability enhancement
Published 2020“…A new metaheuristics method called Farmland Fertility Algorithm (FFA) inspired by nature is proposed for optimal design of PIDPSS using a robust ISTSE objective function which had to be minimized. …”
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17
Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons
Published 2018“…The rolling mechanism method is applied to smooth out the dataset based on the antecedent values of the model inputs before being applied to the GM algorithm. …”
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18
Ant colony optimization and genetic algorithm models for suspended sediment discharge estimation for gorgan-river, Iran
Published 2011“…The training and testing data sets were chosen based on the K-fold method of cross validation to find the optimal classifier. …”
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19
Crossover-first differential evolution for improved global optimization in non-uniform search landscapes
Published 2015“…The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-based optimizers for global optimization due to its simplicity, robustness and efficiency. …”
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20
Exploring dynamic self-adaptive populations in differential evolution
Published 2006“…Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. …”
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