Search Results - (( pressure distribution ant algorithm ) OR ( parameters variation metric algorithm ))*

Model-based hybrid variational level set method applied to object detection in grey scale images by Wang, Jing

“…This algorithm leverages an improved multi-scale mean filter to mitigate grayscale inhomogeneity while reducing the impact of scale parameter selection. …”
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Optimization of an extended H-infinity controller for unmanned helicopter control using multiobjective differential evolution (MODE) by Tijani, Ismaila Bayo, Akmeliawati, Rini, Legowo, Ari, Budiyono, Agus

“…The result of performance evaluation shows robustness to parameter variation of up to 20 per cent variation in nominal values, and in addition provides satisfactory disturbance rejection to wind disturbance in all the three axes. …”
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Stochastic And Modified Sequent Peak Algorithm For Reservoir Planning Analysis Considering Performance Indices by Oskoui, Issa Saket

“…In the next stage, the modified Sequent Peak Algorithm (SPA) is employed for the Storage-yield planning analysis of reservoir systems at different demands, reliability and vulnerability performance metrics employing the synthetic streamflow data. …”
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Optimization of pressure vessel design using pyopt by Woldemichael, D.E., Woldeyohannes, A.D.

“…In this study, we used pyOpt to solve pressure vessel design problem. Among the available optimizers in pyOpt, SLSQP (Sequential least squares programming), COBYLA (Constrained Optimization by Linear Approximation), ALPSO (Augmented Lagrangian Particle Swarm Optimizer), NSGAII (Non Sorting Genetic Algorithm II), MIDACO (Mixed Integer Distributed Ant Colony Optimization), and ALGENCAN (Augmented Lagrangian with GENCAN) were used. …”
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Optimization of pressure vessel design using pyopt by Woldemichael, D.E., Woldeyohannes, A.D.

“…In this study, we used pyOpt to solve pressure vessel design problem. Among the available optimizers in pyOpt, SLSQP (Sequential least squares programming), COBYLA (Constrained Optimization by Linear Approximation), ALPSO (Augmented Lagrangian Particle Swarm Optimizer), NSGAII (Non Sorting Genetic Algorithm II), MIDACO (Mixed Integer Distributed Ant Colony Optimization), and ALGENCAN (Augmented Lagrangian with GENCAN) were used. …”
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Multi leader particle swarm optimization for optimal placement and sizing of multiple distributed generation for a micro grid by Ariya Sinhalage Buddhika Eshan Karunarathne

“…In addition, the solutions have been evaluated based on pre-defined performance metrics and the outcomes of the optimization framework were compared with the other existing optimization techniques to evaluate the potency and the productivity of the developed MLPSO algorithm. …”

Restoration of blurred images using geometric and chebichef moments / Ahlad Kumar by Ahlad, Kumar

“…In the third method, Tchebichef moments (TM) of low order are selected as features used as inputs to ELM to estimate the Gaussian blur parameters. Once the blur parameters are estimated, image restoration of the proposed method is carried out using split Bregman algorithm. …”
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Designing of prediction model for parameter optimization in cnc machining based on artificial neural network / Armansyah ... [et al.] by Armansyah, Puji, Muhammad Nurul, Mardhani, Muhammad Destri, Suartana, I Putu Eka, Desmawati, Ferdyanto, Kusumah, Muhammad Afiff, Yafi, Muhammad Umar, Saedon, Juri

“…The consistent surface finishes in polishing remain a significant challenge. Variations in machining parameters often lead to inconsistent results, negatively impacting both the appearance and functionality of polished components. …”
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Host Based Intrusion Detection and Prevention Model Against DDoS Attack in Cloud Computing by Jaber, Aws Naser, Mohamad Fadli, Zolkipli, Shakir, Hasan Awni

“…The prevention model uses principal component analysis and linear discriminant analysis with a hybrid, nature-inspired metaheuristic algorithm called Ant Lion optimisation for feature selection and artificial neural networks to classify and configure the cloud server. …”
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Forecasting of meteorological drought using ensemble and machine learning models by Pande C.B., Sidek L.M., Varade A.M., Elkhrachy I., Radwan N., Tolche A.D., Elbeltagi A.

“…Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. …”