Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle by Reza Jafari (3494018)

“…To this end, three model-free DRL-based methods, based on deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and TD3 enhanced with curriculum learning (CL TD3), are developed for determining optimal yaw moment control and energy optimization online. …”

On-site workshop investment problem: A novel mathematical approach and solution procedure by Nima Moradi (19418821)

“…Computational experiments show that the proposed method has solved most of the instances of the addressed problem to optimality and outperformed the existing metaheuristics, e.g., Simulated Annealing (SA) and Particle Swarm Optimization (PSO). …”

Secrecy Performance of Decode-and-Forward Based Hybrid RF/VLC Relaying Systems by Jaber Al-Khori (16855470)

Matheuristic Fixed Set Search Applied to the Two-Stage Capacitated Facility Location Problem by Denis Alicic (23073484)

“…The need for effective solution methods arises from the problem’s large-scale complexity and the strong influence of spatial and capacity constraints on solution quality. We propose a twofold contribution: first, an adaptive greedy algorithm that generates high-quality initial solutions, achieving markedly better results than traditional constructive heuristics at comparable computational costs; and second, the adaptation of the Matheuristic Fixed Set Search (MFSS) to the TSCFLP. …”

A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems by Kandil, Sarah M.

“…The formulation is decomposed into two interdependent sub-problems and solved using a combination of metaheuristic and deterministic optimization techniques. A sample case study is presented to illustrate the performance of the algorithm.…”
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Adaptive temperature control of a reverse flow process by using reinforcement learning approach by A. Binid (22046054)

“…First, a policy iteration algorithm is introduced to learn the optimal solution of the associated linear-quadratic control problem online. …”

Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring by Zina Chkirbene (16869987)

“…By formulating the video resource allocation challenge as a multi-objective optimization problem, the framework aims to minimize network delays while respecting node capacity limitations. …”

Joint Beamforming Design and Power Minimization for Friendly Jamming Relaying Hybrid RF/VLC Systems by Jaber Al-Khori (16855470)

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions by Abdulmalik Alwarafy (17984104)

Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain by Farshad Rahimi Ghashghaei (20880995)

“…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …”

Evacuation of a highly congested urban city by El Khoury, John

“…In this paper, we present a capacity-constrained routing optimization approach to maximize the total evacuees for short notice evacuation planning in case of both natural and/or man-made disasters. …”
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Swarm intelligence-based hyper-heuristic for the vehicle routing problem with prioritized customers by Tarhini, Abbas

“…The results indicate that the solution selected by the Cuckoo Search-based hyper-heuristic outperformed the modified Clarke Wright algorithm while taking into consideration the customers’ priority and demands and the vehicle capacity.…”
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Uplink Noma in UAV-Assisted IoT Networks by Mrad, Ali

“…Given the complexity of the problem and the incomplete knowledge about the environment, the problem is divided into two subproblems: the first models the UAV trajectory and the selection of the first device in the NOMA cluster at each time slot as a Markov Decision Process, and uses Proximal Policy Optimization to solve it. The second device is then selected using a heuristic algorithm based on prioritizing devices with higher bit rate requirements and strict deadlines. …”
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A new bi-objective model of the urban public transportation hub network design under uncertainty by Firoozeh Kaveh (14150877)

“…Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. …”

Effective solutions for the capacitated vehicle routing problem and for the split delivery vehicle routing problem with time windows. (c2013) by Hilal, Nathalie

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Studying cooperative multi-agent reinforcement learning in networks by Oudah, Mayada Mohamed

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Traffic Offloading with Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks by Abbas, Nadine

“…We also propose efficient sub-optimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra dense networks. …”
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Multi-UAV-Enabled Mobile Edge Computing IoT Systems: Joint Association and Resource Allocation Framework by Abu Farha, Yazan Mahmoud

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Reinforcement Learning-Based School Energy Management System by Yassine Chemingui (18891757)

“…After cloning the baseline strategy, the agent learns with proximal policy optimization in an actor-critic framework. …”

A learning approach for prioritized handoff channel allocation in mobile multimedia networks by El-Alfy, E.-S.M.

“…Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies.…”
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