Search Results - (( dynamic simulation learning algorithm ) OR ( java simulation optimization algorithm ))
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…The simulation is implemented with iFogSim and java programming language. …”
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Interactive framework for dynamic modelling and active vibration control of flexible structures
Published 2008“…This paper presents the implementation of an interactive learning environment for dynamic simulation and active vibration control of flexible structures. …”
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Dynamic path planning algorithm in mobile robot navigation
Published 2011“…MATLAB simulation is developed to verify and validate the algorithm before they are real time implemented on Team AmigoBotTM robot. …”
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Conference or Workshop Item -
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Monograph -
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Wavelet network based online sequential extreme learning machine for dynamic system modeling
Published 2013“…Wavelet network (WN) has been introduced in many applications of dynamic systems modeling with different learning algorithms. …”
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Conference or Workshop Item -
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Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications
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OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT
Published 2023“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
Review -
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Fog-cloud scheduling simulator for reinforcement learning algorithms
Published 2023“…This study presents a developed simulator that captures all mentioned realistic scenarios by providing the feature of integrability with the reinforcement learning (RL) algorithm. …”
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Hierarchical multi-agent system in traffic network signalization with improved genetic algorithm
Published 2019“…Instead of using classical offline data-driven optimization technique in traffic network signal control, this work aims to explore the potential of implementing an online data-driven optimization technique. A dynamic modeling technique is proposed using Q-learning (QL) algorithm to online observe and learn the inflow-outflow traffic behaviors and extract the model parameters to update the evaluation model used in the fitness function of genetic algorithm (GA). …”
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Proceedings -
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Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition
Published 2024“…Recently, more robust algorithms based on deep reinforcement learning (DRL) have been proposed. …”
Conference Paper -
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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13
Fast and efficient sequential learning algorithms using direct-link RBF networks
Published 2003“…Simulation results for two benchmark problems show the feasibility of the new training algorithms.…”
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Book Section -
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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15
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…The ML approach, which predicts optimal design parameters with a trained dataset, is more efficient with reduced duration than conventional finite element analysis (FEA) tools and stochastic methods. The learning algorithms consider variables such as core structure, cross-coupling effect, and coil flux pipe length. …”
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Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
Published 2010“…ANN is biological inspired and it has dynamic characteristic which is learning. ANN is able to learn through experience and adaptation. …”
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An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks
Published 2015“…The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. …”
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Collision prediction based genetic network programming-reinforcement learning for mobile robot navigation in unknown dynamic environments
Published 2017“…Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). …”
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Reinforcement learning in risk management for pharmaceutical construction projects: frontiers, challenges, and improvement strategies
Published 2025“…Therefore, this paper reviews the practical applications of six algorithms—Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximity Policy Optimization (PPO)—in construction safety, temperature control, resource scheduling, and automated equipment optimization, validating the potential of reinforcement learning to effectively manage dynamic risks through adaptive learning. …”
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