Search alternatives:
algorithm optimize » algorithm optimizer (Expand Search)
Showing 121 - 140 results of 209 for search 'genetic ((algorithm optimize) OR (algorithm (optimizer OR optimized)))', query time: 0.11s Refine Results
  1. 121

    Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem by Abido, M. A.

    Published 2006
    “…The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. …”
    Get full text
    article
  2. 122

    Multiobjective evolutionary algorithms for electric power dispatch problem by Abido, M.A.

    Published 2006
    “…The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. …”
    Get full text
    Get full text
    article
  3. 123

    Simultaneous stabilization of multimachine power systems viagenetic algorithms by Abdel-Magid, Y.L.

    Published 1999
    “…The problem of selecting the parameters of power system stabilizers which simultaneously stabilize this set of plants is converted to a simple optimization problem which is solved by a genetic algorithm with an eigenvalue-based objective function. …”
    Get full text
    Get full text
    article
  4. 124

    Computational Experience On Four Algorithms For The Hard Clustering Problem by AlSultan, K.S.

    Published 2020
    “…Several algorithms have been developed to solve this problem which include the k-means algorithm, the simulated annealing algorithm, the tabu search algorithm, and the genetic algorithm. …”
    Get full text
    article
  5. 125

    Evolutionary algorithms for VLSI multi-objective netlist partitioning by Sait, Sadiq M.

    Published 2006
    “…In this paper, we engineer three iterative heuristics for the optimization of VLSI netlist bi-partitioning. These heuristics are based on Genetic Algorithms (GAs), Tabu Search (TS) and Simulated Evolution (SimE). …”
    Get full text
    article
  6. 126

    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. …”
    Get full text
    Get full text
  7. 127
  8. 128

    An evolutionary algorithm for solving the geometrically constrained site layout problem by Zouein, P.

    Published 2017
    “…The proposed algorithm is two-phases: an initialization phase that generates an initial population of layouts through a sequence of mutation operations, and a reproduction phase that evolve the layouts generated in phase one through a sequence of genetic operations aiming at finding an optimal layout. …”
    Get full text
    Get full text
    Get full text
    Get full text
    conferenceObject
  9. 129
  10. 130
  11. 131
  12. 132

    Evolutionary algorithms, simulated annealing and tabu search: a comparative study by Youssef, H.

    Published 2020
    “…All rights reserved. Keywords: Genetic algorithms; Simulated annealing; Tabu search; Fuzzy logic; Floorplanning; Combinatorial optimization; VLSI…”
    Get full text
    article
  13. 133

    On-site workshop investment problem: A novel mathematical approach and solution procedure by Nima Moradi (19418821)

    Published 2023
    “…Next, due to the NP-hardness of the problem, an enhanced Genetic Algorithm (GA)-based metaheuristic with efficient problem-specific improvement rules as local search and effective crossover and mutation operators is proposed. …”
  14. 134

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

    Published 2001
    “…A new nondominated sorting genetic algorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and noncommensurable objectives. …”
    Get full text
    Get full text
    article
  15. 135
  16. 136

    Environmental/economic power dispatch using multiobjective evolutionary algorithms: a comparative study by Abido, M.A.

    Published 2003
    “…A comparative study of newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) applied to a nonlinear power system multiobjective optimization problem is presented in this paper. …”
    Get full text
    Get full text
    article
  17. 137
  18. 138

    A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

    Published 2022
    “…The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. …”
  19. 139

    Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks by Najam Us Sahar Riyaz (22927843)

    Published 2025
    “…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”
  20. 140

    Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach by Haitham Osman (11737057)

    Published 2025
    “…A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. …”