Search Results - (( evolution classification parallel algorithm ) OR ( variable optimization mead algorithm ))

Single and Multiple variables control using Tree Physiology Optimization by Halim, A.H., Ismail, I.

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Optimizing the SEIRD model for COVID-19 in Malaysia using pymoo framework by Abdul Hadi, Muhammad Salihi, Amran, Muhammad Aiman Haziqh, Zulkarnain, Norsyahidah

“…However, the introduction of time-dependent coefficients in both models increases the number of optimization variables. To solve this, the Nelder-Mead and Pattern Search algorithms were recommended. …”
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Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings by Homod R.Z., Gaeid K.S., Dawood S.M., Hatami A., Sahari K.S.

Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Recognizing complex human activities using hybrid feature selections based on an accelerometer sensor by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md Nasir, Mustapha, Norwati, Perumal, Thinagaran, Mohamed, Raihani

“…Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. …”
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