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  1. 501

    FAST FUZZY FORCE-DIRECTED/SIMULATED EVOLUTION METAHEURISTIC FOR MULTIOBJECTIVE VLSI CELL PLACEMENT by Sait, Sadiq M.

    Published 2006
    “…The cell placement problem is a proven NP hard combinatorial optimization problem. The complexity of this problem increases when multiple optimization objectives are considered simultaneously. …”
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    article
  2. 502

    "A Tabu Search Approach for the Design of Variable Structure Load Frequency Controller Incorporating Model Nonlinearities" by Al-Hamouz, Z.

    Published 2007
    “…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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    article
  3. 503

    Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs by Kouatli, Issam

    Published 2008
    “…An irregular shape may be required by some systems. Hence, a genetic algorithm was proposed as a methodology to optimize the performance of fuzzy systems by mutating different regular shapes. …”
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    article
  4. 504

    Digital circuit design through simulated evolution (SimE) by Sait, Sadiq M.

    Published 2003
    “…Area, power and delay are considered in the optimization of circuits. Results obtained by SimE algorithm are compared to those obtained by genetic algorithm (GA).…”
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    article
  5. 505

    GENETIC SCHEDULING OF TASK GRAPHS by Benten, M. S.

    Published 2020
    “…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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    article
  6. 506
  7. 507

    HPTS: heterogeneous parallel tabu search for VLSI placement by Al-Yamani, A.

    Published 2002
    “…Two parallelization strategies, i.e., functional decomposition and multi-search thread strategies are integrated. The proposed algorithm is tested on the VLSI standard cell placement problem, however, the same algorithm can be used on any combinatorial optimization problem. …”
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    article
  8. 508
  9. 509

    Uplink Noma in UAV-Assisted IoT Networks by Mrad, Ali

    Published 2022
    “…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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    masterThesis
  10. 510

    An innovative simulated annealing approach to the long-term hydroscheduling problem by Mantawy, A.H.

    Published 2001
    “…A significant reduction in the number of the objective function evaluations, and consequently less iterations are required to reach the optimal solution. The proposed algorithm has been applied successfully to solve a system with four series cascaded reservoirs. …”
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    article
  11. 511

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

    Published 2018
    “…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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    article
  12. 512

    Second-order conic programming for data envelopment analysis models by Mourad, Nahia

    Published 2022
    “…Indeed, the aforesaid method is based on mathematical optimization. This paper constructs a second-order conic optimization problem unifying several DEA models. …”
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  13. 513

    An evolutionary meta-heuristic for state justification insequential automatic test pattern generation by El-Maleh, A.H.

    Published 2001
    “…Evolutionary algorithms have been effective in solving many search and optimization problems. …”
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    article
  14. 514
  15. 515

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

    Published 2017
    “…The algorithm uses Dijkstras algorithm to find the shortest path(s) and a modified greedy algorithm to assign maximum flows to selected paths given a specific schedule per time interval. …”
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    conferenceObject
  16. 516

    Robust Kalman filter and smoother for errors-in-variables model with observation outliers based on Least-Trimmed-Squares by ALMutawa, Jaafar

    Published 2020
    “…Since the LTS is combinatorial optimization problem the randomized algorithm has been proposed in order to achieve the optimal estimate. …”
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    article
  17. 517

    Scheduling and allocation in high-level synthesis using stochastic techniques by Sait, Sadiq M.

    Published 2020
    “…Scheduling and allocation can be formulated as an optimization problem. In this work, a unique approach to scheduling and allocation problem using the genetic algorithm (GA) is described. …”
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    article
  18. 518

    Genetic Fuzzimetric Technique (GFT) by Kouatli, Issam

    Published 2012
    “…Integration of fuzzy systems with genetic algorithm has been identified by researchers as a useful technique of optimizing systems under uncertainty. …”
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  19. 519
  20. 520

    Fast force-directed/simulated evolution hybrid for multiobjective VLSI cell placement by Sait, Sadiq M.

    Published 2004
    “…VLSI standard cell placement is a hard optimization problem, which is further complicated with new issues such as power dissipation and performance. …”
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    article