Search Results - (( development process function algorithm ) OR ( combining simulation optimization algorithm ))
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1
Simulated annealing for solving economic dispatch problem / Wan Khairulizuan Wan Ismail
Published 2010“…In the development of the algorithm, transmission losses are first discounted and they are subsequently incorporated in the algorithm through the use of the B-matrix loss formula. …”
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2
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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3
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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4
Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching
Published 2014“…Forward model was also involved in the process of defining the objective function. Next, using simulated data together with historical data, objective function will be computed. …”
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5
Nomadic people optimizer (NPO) for large-scale optimization problems
Published 2019“…The proposed algorithm simulates the behavior of the nomads when they are searching for life sources (water or grazing fields). …”
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6
Artificial neural controller synthesis for TORCS
Published 2015“…The results showed: (1) DE hybrid FFNN could generate optimal controllers, (2) the proposed fitness function had successfully generated the required car's racing controllers, (3) the proposed minimization algorithm had been successfully minimize the number of RF sensors used, (4) the PDE algorithm could be implemented to generate optimal solutions for car racing controllers, and (5) the combination of components for average car speed and distance between the car and track axis is very important compared to other components. …”
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7
Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…DE has effectively solved various global optimization problems, including benchmark functions. …”
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8
Integration Of Travel Time Zone For Optimal Siting Of Emergency Facilities
Published 2008“…Two algorithms, Greedy Adding (Add) and Greedy Adding with Travel Time Evaluation (GAT), were applied to solve the optimization problem of the MSAP. …”
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9
An autonomous wheelchair speed control using fuzzy logic controller / Muhammad Ashraf Mohamad Ghazali
Published 2025“…The simulation was performed to model and validate the FLC with comparing the standards function that is triangular and trapezoidal MFs to determine optimal performance. …”
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10
Cryogenic Purification of Natural Gas under High Pressure Using Hybrid Multiple Bed Network
Published 2015“…In present study the optimal temperature and pressure conditions for natural gas processing are explored by performing optimization on each node using Golden Section Search algorithm. …”
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11
Test case generation from state machine with OCL constraints using search-based techniques / Aneesa Ali Ali Saeed
Published 2017“…The whole constraint analyzer and the fitness function were combined with four SBTs (genetic algorithm, evolutionary algorithm, simulating annealing, and quantum genetic algorithm). …”
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12
Predictive-reactive job shop scheduling for flexible production systems with the combination of optimization and simulation based algorithm
Published 2020“…It would be a promising approach to combine the advantages of simulation with optimization algorithm.…”
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13
Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems
Published 2022“…In this case, the effectiveness and reliability of RMS is increase by combining the simulation with the optimization algorithm.…”
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14
Water level predictio for Limbang basin using multilayer perceptron (mlp) and radial basis function (rbf) neural network
Published 2010“…Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were adopted in this study. MLP is trained with conjugate gradient algorithms, trainscg and RBF with newrb. …”
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15
Spatio-Temporal processing for Optimum Uplink-Downlink WCDMA Systems
Published 2008“…In this research, also, we develop, simulate and evaluate the SINR and Signal to Noise Ratio (Et!…”
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16
IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks
Published 2024“…The proposed algorithm, named Intelligent Water Drops with Simulated Annealing (IWDSA), combines two powerful optimization methods: Intelligent Water Drops (IWD) and Simulated Annealing (SA). …”
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17
Modeling and Optimization of Tapered Rectangular Thin-walled Columns Subjected to Oblique Loading for Impact Energy Absorption
Published 2013“…RS model using quadratic polynomial function is given as equation (1) below. The optimal design is obtained by using the constrained nonlinear multivariable optimization algorithm provided by MATLAB. …”
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18
Asset liability management model: The case of selected Islamic banks in Malaysia / Chong Hui Ling
Published 2017“…Algorithms established for the optimisation processes are documented for the study. …”
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19
A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo...
Published 2025“…The Crossover Probability Factor (CPF) as the Certain Ratio (CR) and the Mutation Factor (MF) of the algorithm are gradually altered during algorithm iteration to enhance the method's performance during optimization. …”
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