Search Results - (( deviation selection based algorithm ) OR ( based optimization based algorithm ))
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A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments
Published 2013“…The motion planning problem poses the question of how a robot can move from an initial to a final position. Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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Optimal input features selection of wavelet-based EEG signals using GA
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Efficient Time-Varying q-Parameter Design for q-Incremental Least Mean Square Algorithm with Noisy Links
Published 2022“…Motivated by this fact, a quantum calculus-based noisy links incremental least mean squares (NL-qILMS) algorithm is proposed. …”
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Reactive approach for automating exploration and exploitation in ant colony optimization
Published 2016“…The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. …”
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Modified ant colony optimization algorithms for deterministic and stochastic inventory routing problems / Lily Wong
Published 2018“…The computational results also show that the algorithms of population based ACO performs better than the algorithms of non-population based ACO. …”
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An enhanced sequential exception technique for semantic-based text anomaly detection
Published 2019“…ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. …”
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Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
Published 2024“…Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.…”
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Pairwise clusters optimization and cluster most significant feature methods for anomaly-based network intrusion detection system (POC2MSF) / Gervais Hatungimana
Published 2018“…In this paper, we use two-step features selection and Quality Threshold with Optimization methods to design anomaly-based HIDS and NIDS separately. …”
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Optimization of process parameters for rapid adsorption of Pb(II), Ni(II), and Cu(II) by magnetic/talc nanocomposite using wavelet neural network
Published 2016“…After minimizing this error, the topologies of the algorithms were compared based on the coefficient of determination and absolute average deviation. …”
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Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithm
Published 2019“…Subsequently, firefly algorithm is applied to attain near-optimal solution for NR and DG size. …”
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Neural Network Model and Finite Element Simulation of Spring back in Plane-Strain Metallic Beam Bending
Published 2006“…To validate the finite element model physical experiments were conducted. A neural network algorithm based on the backpropagation algorithm has been developed. …”
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Metacarpal phantom radiograph edge detection using genetic algorithm gradient based genotype / Norharyati Md Ariff
Published 2007“…Fitaess fimction is calculated based on the gradient which is the length fi*om each pixel. …”
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Optimal distribution system reconfiguration incorporating distributed generation based on simplified network approach / Mohammad Al Samman
Published 2020“…In addition, this work considered non-dispatchable renewable energy resources and load variations for daily operation. The selected meta-heuristic techniques in this research involve the Firefly Algorithm (FA) and Biogeography-Based Optimization (BBO). …”
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Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm
Published 2020“…The existing data-driven neuroendocrine-PID (NEPID) utilizes the simultaneous perturbation stochastic approximation (SPSA) algorithm as the data-driven tool. However, this SPSA-based method is unable to find the optimal value of the design parameter due to unstable convergence obtained that degrades the controller performance in MIMO systems. …”
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Multivariate Optimization of Biosynthesis of Triethanolamine-Based Esterquat Cationic Surfactant Using Statistical Algorithms
Published 2011“…All process parameters are selected to conduct the optimization by using some statistical algorithms such as Artificial Neural networks (ANNs), Response Surface Methodology (RSM), Wavelet Neural Network (WNN) and Partial Least Squares (PLS). …”
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A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio
Published 2018“…Then the proposed model is solved by a fuzzy simulation-based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times-based method. …”
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A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio
Published 2018“…Then the proposed model is solved by a fuzzy simulation-based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times-based method. …”
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LASSO-type estimations for threshold autoregressive and heteroscedastic time series models.
Published 2020“…We develop an active-set based block coordinate descent algorithm (BCD) to optimize exactly the group LASSO. …”
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UMK Etheses -
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