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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Shuffled Linear Regression with Erroneous Observations by Saab, Samer S.

“…We propose an optimal recursive algorithm that updates the estimate from the underdetermined function that is based on that permutation-invariant constraint. …”
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Morphology for Planar Hexagonal Modular Self-Reconfigurable Robotic Systems by Sadjadi, Hossein

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Iterative heuristics for multiobjective VLSI standard cellplacement by Sait, Sadiq M.

“…We employ two iterative heuristics for the optimization of VLSI standard cell placement. These heuristics are based on genetic algorithms (GA) and tabu search (TS) respectively. …”
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MoveSchedule by 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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"A Tabu Search Approach for the Design of Variable Structure Load Frequency Controller Incorporating Model Nonlinearities" by Al-Hamouz, Z.

“…The proposed method formulates the design of VSC as an optimization problem and utilizes Tabu Search Algorithm (TS) to find the optimal settings of the controller. …”
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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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Designing Cellular Mobile Networks Using Non{Deterministic Iterative Heuristics by Sait, Sadiq M.

“…Hence, a randomized, heuristic algorithm, such as Simulated Evolution is used in this work to optimize the transmission costs in cellular networks. …”
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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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Multi-UAV-Enabled Mobile Edge Computing IoT Systems: Joint Association and Resource Allocation Framework by Abu Farha, Yazan Mahmoud

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Dynamic Layout Planning Using a Hybrid Incremental Solution Method by Zouein, Pierrette

“…Construction resources, represented as rectangles, are subjected to two-dimensional geometric constraints on relative locations. The objective is to allow site space to all resources so that no spatial conflicts arise, while keeping distance-based adjacency and relocation costs minimal. …”
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A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks by Sakib Mahmud (15302404)

“…<p>The energy internet (EI) is evolving toward decentralized, data-rich, and time-critical operation, where legacy optimization often fails to meet complexity, scalability, and real-time constraints. …”

Boosting the Training of Neural Networks Through Hybrid Metaheuristics by Abou Doush, Iyad

“…In this paper, six versions of memetic algorithms (MAs) are proposed to replace gradient descent learning mechanism of MLP where adaptive β�-hill climbing (Aβ�HC) as a local search algorithm is hybridized with six population-based metaheuristics which are hybrid flower pollination algorithm, hybrid salp swarm algorithm, hybrid crow search algorithm, hybrid grey wolf optimization (HGWO), hybrid particle swarm optimization, and hybrid JAYA algorithm. …”
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A METHODOLOGY FOR NETWORK TOPOLOGY DESIGN USING FUZZY EVALUATIONS by Khan, Salman A.

“…In this paper, we present a methodology to address design issues. This methodology is based on two algorithms, namely, fuzzy simulated evolution algorithm and the augmenting path algorithm. …”
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A linear programming approach for the weighted graph matchingproblem by Almohamad, H.A.

“…A linear program is obtained by formulating the graph matching problem in L1 norm and then transforming the resulting quadratic optimization problem to a linear one. The linear program is solved using a simplex-based algorithm. …”
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GENETIC SCHEDULING OF TASK GRAPHS by Benten, M. S.

“…The problem of assigning tasks to processing elements as a combinatorital optimization is formulated, and a heuristic based on a genetic algorithm is presented. …”
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A LINEAR-PROGRAMMING APPROACH FOR THE WEIGHTED GRAPH MATCHING PROBLEM by Al-Mohamad, HA

“…A linear program is obtained by formulating the graph matching problem in L1 norm and then transforming the resulting quadratic optimization problem to a linear one. The linear program is solved using a Simplex-based algorithm. …”
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Power system output feedback stabilizer design via geneticalgorithms by Abdel-Magid, Y.L.

“…A digital simulation of the power system is then used in conjunction with the genetic algorithm to determine the output feedback gains. In the second method, the problem of selecting the output feedback gains is converted to a simple optimization problem with an eigenvalue based objective function, which is solved by a genetic algorithm. …”
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Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs by Kouatli, Issam

“…Design/methodology/approach – The design was based on two principles: selection and optimization. …”
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3D deployment of UAVs in wireless networks for traffic offloading and edge computing. (c2019) by Islambouli, Rania

“…This being said, computation tasks generated by IoT devices can be pro- cessed in less latency and with much lower energy consumption at the devices. To optimally deploy UAVs as mounted cloudlets, we formulate our problem as mixed integer program and then use an e cient meta-heuristic algorithm to generate optimized results for large scale IoT networks. …”
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