Search Results - (( based evaluation _ algorithm ) OR ( data optimization method algorithm ))
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1
VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern
Published 2012“…GA is a widely used optimization algorithm based on the concept of survival of the fittest. …”
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2
Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm
Published 2024“…Across all experiments, the IAOA-based method demonstrated superior performance compared to AOA and other methods, including a hybrid approach combining the average multi-verse optimizer and sine cosine algorithm, particle swarm optimizer, the sine cosine algorithm, multi-verse optimizer and grey wolf optimizer. …”
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3
Tree-based contrast subspace mining method
Published 2020“…The research works involve first preparing the real world numerical and categorical data sets. Then, the tree-based method, the genetic algorithm based parameter values identification of tree-based method, and followed by the genetic algorithm based tree-based method, for numerical data sets are developed and evaluated. …”
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4
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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5
Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost
Published 2024“…PSO-EBGWO is used to optimize the parameters of the CatBoost model. In this method, the Gray Wolf optimized algorithm (EBGWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. …”
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6
Hybrid performance measures and mixed evaluation method for data classification problems
Published 2012“…Moreover, the GA model that was optimized by OAERP2 measure (GAoe2) performed significantly and statistically differently as compared to other OAERP2-based models through win-draw-loss evaluation method and two nonparametric tests. …”
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7
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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8
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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9
Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
Published 2020“…In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. …”
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10
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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11
An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system
Published 2023“…Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods.…”
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12
Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems
Published 2023“…Stochastic gradient descent (SGD) is one of the most common algorithms used in solving large unconstrained optimization problems. …”
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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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14
Optimizing Decentralized Exam Timetabling with a Discrete Whale Optimization Algorithm
Published 2025“…In recent years, there has been increasing interest in intelligent optimization algorithms, such as the Whale Optimization Algorithm (WOA). …”
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15
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…Based on the results obtained, a better prediction result can be produced by the proposed GA-BPNN learning algorithm.…”
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16
An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data
Published 2021“…Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. …”
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Data-driven PID controller of wind turbine systems using safe experimentation dynamics algorithm
Published 2024“…SEDA, a single-agent based optimization technique, requires only one function evaluation per iteration, alleviating computational burdens. …”
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19
Optimization of RFID network planning using MDB-FA method
Published 2017“…The generated data are utilized as an input representation to apply into firefly algorithm based on Density-Based Algorithm (DBSCAN) to find the optimal network solution. …”
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20
Improved cuckoo search based neural network learning algorithms for data classification
Published 2014“…On the other hand, LM algorithms which are derivative based algorithms still face a risk of getting stuck in local minima. …”
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