Showing 21 - 40 results of 52 for search '(( constrained optimization algorithm ) OR ( dog optimization algorithm ))', query time: 0.16s Refine Results
  1. 21

    Trial-based dominance for comparing both the speed and accuracy of stochastic optimizers with standard non-parametric tests by Kenneth V. Price (17877002)

    Published 2023
    “…Simulations demonstrate that “U-scores” are much more effective than dominance when tasked with identifying the better of two algorithms. We validate U-scores by having them determine the winners of the CEC 2022 competition on single objective, bound-constrained numerical optimization.…”
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    An evolutionary algorithm for network topology design by Youssef, H.

    Published 2001
    “…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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    Optimal shunt compensators at nonsinusoidal busbars by El-Amin, I.M.

    Published 1995
    “…The Direct Search Polytope algorithm is used. The solution of this model yields an optimal shunt LC compensator for the nonlinear load. …”
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  6. 26

    Tabu search based circuit optimization by Sait, Sadiq M.

    Published 1998
    “…In this paper we address the problem of optimizing mixed CMOS/BiCMOS circuits. The problem is formulated as a constrained combinatorial optimization problem and solved using an tabu search algorithm. …”
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  7. 27

    Fuzzy simulated evolution algorithm for topology design of campusnetworks by Youssef, H.

    Published 2000
    “…The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes) and links. …”
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  8. 28

    Environmental/economic power dispatch using multiobjective evolutionary algorithms by Abido, M.A.

    Published 2003
    “…The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new strength Pareto evolutionary algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable objectives. …”
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  9. 29

    A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS by Youssef, H.

    Published 2020
    “…ABSTRACT The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes), and the links. …”
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  10. 30

    OPTIMAL SHUNT COMPENSATORS AT NON-SINUSOIDAL BUSBARS by El-Amin, I.M.

    Published 2020
    “…The Direct Search Polytope algorithm is used. The solution of this model yields an optimal shunt LC compensator for the nonlinear lend. …”
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    article
  11. 31

    A new multiobjective evolutionary algorithm forenvironmental/economic power dispatch by Abido, A.A.

    Published 2001
    “…In this paper, a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) optimization problem is presented. …”
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  12. 32

    An analytical framework for high-speed hardware particle swarm optimization by El-Abd, Mohammed

    Published 2020
    “…Engineering optimization techniques are computationally intensive and can challenge implementations on tightly-constrained embedded systems. …”
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    Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

    Published 2020
    “…Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
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  15. 35

    Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

    Published 2020
    “…Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
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    article
  16. 36

    Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem by Haraty, Ramzi A.

    Published 2018
    “…Genetic algorithms were successfully useful to solve many optimization problems including the university Timetable Problem. …”
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  17. 37

    A multi-objective planning approach for optimal DG allocation for droop based microgrids by Shaaban, Mostafa

    Published 2021
    “…This paper proposes a multi-objective planning approach to determine the optimal DG locations for droop-based microgrids. A secondary control operating region is mathematically formulated and incor-porated in the multi-objective optimization problem to constrain the optimal droop characteristic within the acceptable frequency and voltage thresholds. …”
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  18. 38

    Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System by Alaa Altawallbeh (22565837)

    Published 2025
    “…We further propose a hybrid Particle Swarm Optimization (PSO) algorithm with a multi-objective cost function to optimize VSG controller gains. …”
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    Thermodynamic Analysis and Optimization of Densely-Packed Receiver Assembly Components in High-Concentration CPVT Solar Collectors by Sharaf, Omar Z.

    Published 2016
    “…Finally, using the developed design models and simulation algorithms, constrained non-linear multi-variable geometric optimization of the assembly components has been carried out to obtain minimum pumping power, maximum extractor heat transfer coefficient, and maximum thermoelectric power output. …”
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  20. 40

    The survey of cluster based data collection process for iot enabled wireless sensor network using several optimization technique by Abirami, R.

    Published 2025
    “…The study analyses nature-based metaheuristic methods such as the Genetic Algorithms, Particle Swarm Optimization, Firefly Optimization, Gray Wolf Optimization and Water-Cycle Algorithms, and specialised protocols of clustering, routing and data aggregation. …”
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