Power-constrained system-on-a-chip test scheduling using a genetic algorithm حسب Harmanani, Haidar M.

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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms حسب Marwan Dhuheir (19170898)

"…However, the IIoT inference distribution raises challenges in ensuring reliable data transmission with minimal latency while respecting the practical UAVs’ constraints. To address these issues, we formulate the problem of CNN layer distribution and UAV trajectory planning (LDTP) as an optimization problem to improve latency, reliability, and resource usage. …"

An evolutionary algorithm for network topology design حسب 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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Fuzzy simulated evolution algorithm for topology design of campusnetworks حسب Youssef, H.

"…We present an approach based on the simulated evolution algorithm for the design of campus network topology. …"
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A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS حسب Youssef, H.

"…To intensify the search, we have also incorporated Tabu Search-based characteristics in the allocation phase of the SE algorithm. …"
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A new multiobjective evolutionary algorithm forenvironmental/economic power dispatch حسب Abido, A.A.

"…The EED problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. …"
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Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem حسب Haraty, Ramzi A.

"…Genetic algorithms were successfully useful to solve many optimization problems including the university Timetable Problem. …"
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Uplink Noma in UAV-Assisted IoT Networks حسب 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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MoveSchedule حسب Zouein, Pierette

"…The layout construction algorithm that underlies MoveSchedule uses Constraint Satisfaction to find the set of all positions that meet the constraints on resources' positions and Linear Programming to find the optimal positions that minimize resource transportation and relocation costs. …"
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