Search Results - (( parameter optimization max algorithm ) OR ( probable distribution control algorithm ))
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1
Reactive approach for automating exploration and exploitation in ant colony optimization
Published 2016“…Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. …”
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Thesis -
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Ant colony optimization in dynamic environments
Published 2010“…The last objective of this thesis is to optimize the parameter settings of the best performing ant algorithm with local search. …”
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Improvement of Centralized Routing and Scheduling Using Cross-Layer Design and Multi-Slot Assignment in Wimax Mesh Networks
Published 2009“…This thesis proposes an optimized strategy namely cross-layer design in routing algorithms used find the best route for all SSs and scheduling algorithms, used to assign a time slot for each possible node transmission. …”
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4
Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks
Published 2013“…It reduces the number of handovers by 29.7% and 26.9%, respectively, compared to the conventional RSSI based handover algorithm and the previous worked, mobility improved handover (MIHO) algorithm. …”
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5
Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms
Published 2025“…A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. …”
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Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model
Published 2023“…Electric transformers; Health; Hidden Markov models; Nonlinear programming; Probability distributions; Quality control; Viterbi algorithm; Condition parameters; Dissolved gas analysis; Distribution transformer; Emission probabilities; Health indices; Non-linear optimization; Remaining useful lives; Transition probabilities; Parameter estimation…”
Conference Paper -
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Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network
Published 2024journal::journal article -
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Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations
Published 2022“…Meta-heuristic optimizations, namely a genetic algorithm (GA) and a whale optimization algorithm (WOA), are applied to optimize the designed control system. …”
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Evaluation of MAC's distributed queuing algorithm for Internet of things applications
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Proceeding Paper -
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Agents for Fuzzy Indices of Reliability Power System with Uncertainty Using Monte Carlo Algorithm
Published 2014“…Two agents are developed based on fuzzy parameters of Monte Carlo i.e. current with its means and variances; the other agent is the probability of outage capacity for each state. These agents shall be applied in terms of the loss of load probability (LOLP) and loss of load expectation (LOLE) which when implemented and compared based on a Malaysian distribution network (DISCO-Net). …”
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DC Motor Control using Ant Colony Optimization
Published 2011“…Since 1995 various other extended versions of AS have been developed, induding Ant Colony System (ACS) and MAX-MIN Ant System (MMAS). In 1999 Dorigo proposed the Ant Colony Optimization (ACO) meta-heuristic that became the most successful and recognized algorithm based on ant behaviour [1]. …”
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Final Year Project -
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Robust PID anti-swing control of automatic gantry crane based on Kharitonov's stability
Published 2009“…The proposed method employs Genetic Algorithm (GA) in min-max optimization to find the stable robust PID. …”
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Proceeding Paper -
15
Improving neural networks training using experiment design approach
Published 2005“…There are several methods of selecting training data from input space for neural networks which include D-optimal and Max-min design approaches. Consider a function approximation problem (Neural Network using Radial Basic Function structure) and limit the amount of training data, say (m) from N amount of possible data. …”
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16
Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios
Published 2015“…In order to increase the system efficiency and maximize the power generation, constructed operation models were optimized. To determine the optimum solution in each policy, real coded genetic algorithm is used as an optimization technique. …”
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17
A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems
Published 2019“…The intention of this hybridization is to further enhance the exploratory and exploitative search capabilities involving simple concepts. The proposed algorithm adopts the combined discrete and continuous probability distribution scheme of ant colony optimization (ACO) to specifically assist genetic algorithm in the aspect of exploratory search. …”
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Service and Voltage Sag Study of Humid and Dry Weather Utilities
Published 2002“…The study has been focused to formulate algorithm for estimating probability of nuisance trip outs in industrial organizations operating in humid and hot dry environments. …”
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A multilayered convolutional sparse coding framework for modeling of pooling operation of convolution neural networks
Published 2019“…We demonstrate that with optimal parameters selected for sparsity of feature maps, the pooling operation (here max pooling) when used layered wise in ML-CSC framework improves the effective dictionaries and resulting feature maps, which in turn improves the reconstruction accuracy of images after multilayered implementation.…”
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Proceeding Paper -
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Multi-objective scientific workflow scheduling algorithm in multi-cloud environment for satisfying QoS requirements
Published 2022“…With the MOS-MWO algorithm, makespan is reduced by 40%, cost also reduced by 3 % and risk probability reduced by 86%. …”
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