Search Results - (( simulation optimization model algorithm ) OR ( data optimization modified algorithm ))
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1
Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
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Working Paper -
2
New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems
Published 2024“…In the fourth phase, the newly developed algorithm undergoes testing on the formulated ROOPs and compared to several contemporary optimizer algorithms. …”
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Thesis -
3
The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
Published 2023“…Balancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Support vector regressions; Support vectors machine; Simulated annealing; algorithm; mayfly; optimization; prediction; streamflow; support vector machine; Jhelum River…”
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4
Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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5
Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…he genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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6
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The new proposed method (MBPSO+MKN+GK) Gustafson- Kessel algorithm (GK)integrated with modified of Kohonen Network algorithm (MKN)and modified binary particle swarm optimization (MBPSO) was used to classify the credit scoring data. …”
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Thesis -
7
Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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8
Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment
Published 2017“…During the course of the present work, two fuzzy methods (modified Zimmermanns approach and modified angelovs approach ) and fourmeta-heuristics and hybrid meta heuristics including; simulated annealing (SA), modified simulated annealing (MSA), hybrid modified simulated annealing and simplex downhill (MSASD), hybrid modified simulated annealing and modified particle swarm optimization (MSAPSO) were proposed. …”
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Thesis -
9
Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
Published 2018“…Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. …”
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10
Application of Evolutionary Algorithm for Assisted History Matching
Published 2014“…When the differences between the observed performance data and simulated data are found, the iterations are made to modify the accuracy of the match. …”
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Final Year Project -
11
Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm
Published 2018“…The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. …”
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Thesis -
12
Modeling the powder compaction process using the finite element method and inverse optimization
Published 2011“…Thus, an integrated simulation module consisting of an inverse optimization method and a finite element method was developed for modeling the powder compaction process as a whole. …”
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13
Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…The study simulated single-input-single-output (SISO) models: ARX and NARX. …”
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Thesis -
14
Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems
Published 2009“…Approach: The implementation of Ant Colony Optimization (ACO) for solving solid waste collection problem as a VRPSD model was described. …”
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15
Ant colony optimization for solving solid waste collection scheduling problems
Published 2009“…Approach: The implementation of Ant Colony Optimization (ACO) for solving solid waste collection problem as a VRPSD model was described. …”
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16
A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics-Based COVID-19 Diagnosis
Published 2025“…While the baseline model achieves 88 accuracy on Data1 and 97.6 on Data2, the proposed ALS-IOAP-DNN4 model attains perfect accuracy (100) on both datasets, demonstrating the effectiveness of ALS and advanced optimization techniques. …”
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17
Enhanced multi-hop mechanism in vehicular communication system using swarm algorithm
Published 2021“…The paper aims to design a broadcast control system which is able to optimize the broadcast process in VANET. Vehicular network is modelled in Simulation of Urban Mobility (SUMO) and the algorithm is formulated in MATLAB. …”
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Proceedings -
18
HARQ aware scheduling algorithm for the downlink LTE system
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Proceeding Paper -
19
Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia
Published 2019“…The general objective of the study was the development of optimized hybrid debris flow models using airborne laser scanning data and Machine learning algorithms in Malaysia. …”
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Thesis -
20
Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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