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

Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

Subjects:

Evolutionary Algorithms for VLSIMultiobjective Netlist Partitioning by Sait, Sadiq M.

“…Further, we compared the results of the iterative heuristics with a modified FM algorithm, named PowerFM, which targets power optimization. …”
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Stochastic evolution algorithm for technology mapping by Al-Mulhem, A.S.

“…SELF-Map is based on the Stochastic Evolution (SE) algorithm. The state space model of the problem is defined and suitable cost function which allows optimization for area, delay, or area-delay combinations is proposed. …”
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An evolutionary algorithm for network topology design by Youssef, H.

“…The topology design of campus networks is a hard constrained combinatorial optimization problem, dictated by physical and technological constraints and must optimize several objectives. …”
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Exam scheduling algorithms. (c1999) by Mikati, Ziad

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An Adaptive Sliding Mode Control for a Dual Active Bridge Converter With Extended Phase Shift Modulation by Farzaneh Bagheri (17947811)

“…The conventional single-phase shift (SPS) modulation-based DAB converter is known to be inefficient. Hence, an optimization algorithm based on the Lagrange multiplier method (LMM) is proposed to minimize both backflow power and inductor current stress simultaneously. …”

Parallel metaheuristic algorithm for exam timetabling by Mansour, Nashat

“…For large numbers of exams and students, sequential algorithms are likely to be time consuming. The purpose of this work is to present a parallel metaheuristic algorithm for producing good sub-optimal exam timetables in a reasonable time. …”
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Application of Heuristic Algorithms for Multiuser Detection by Morra, H.

“…The new proposed structure brings several improvements when compared to both the conventional (matched filter) detector and the decorrelating detector. Additionally, the algorithm proposed here fairly approximates the performance of the optimal MUD detector with much reduced computational complexity.…”
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Scalable Parallel Algorithms for FPT Problems by Abu-Khzam, Faisal N.

“…As a case study, optimal solutions to very large instances of the NP-hard vertex cover problem are computed. …”
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An Uncertainty Based Genetic Algorithm Approach for Project Resource Scheduling by ALKETBI, SAIF

“…The results showed the proposed methodology outperformed existing algorithms in optimizing project durations and resources allocation. …”
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Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas by Marwan Dhuheir (19170898)

“…We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. …”

Incremental & classical genetic algorithm. (c2001) by Awad, Mohamad M.

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Scalable parallel algorithms for difficult combinatorial problems by Abu-Khzam, Faisal N.

“…A novel combination of emergent algorithmic methods, powerful computational platforms and supporting infrastructure is described. …”
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A simulated annealing algorithm for unit commitment by Mantawy, A.H.

“…This paper presents a simulated annealing algorithm (SAA) to solve the unit commitment problem (UCP). …”
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A new multimodulus blind equalization algorithm by Abrar, Shafayat

“…The phase-recovery and intersymbol interference optimization properties, exhibited by the proposed algorithm, are also discussed.…”
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Genetic algorithms applications in load frequency control by Abdel-Magid, Y.L.

“…This paper deals with the application of genetic algorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. …”
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A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM by Selim, S.Z.

“…In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions which are usually what the K-means algorithm obtains. …”
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Adaptive bias simulated evolution algorithm for placement by Youssef, H.

“…Simulated Evolution (SE) is a general meta-heuristic for combinatorial optimization problems. A new solution is evolved from current solution by relocating some of the solution elements. …”
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A New Parallel Genetic Algorithm Model by Al-Somani, Turki F.

“…This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. …”
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