Showing 1 - 7 results of 7 for search 'Flower optimization algorithm', query time: 0.06s Refine Results
  1. 1

    Island flower pollination algorithm for global optimization by Al-Betar, Mohammed Azmi

    Published 2019
    “…Flower pollination algorithm (FPA) is a recent swarm-based evolutionary algorithm that was inspired by the biological evolution of pollination of the flowers. …”
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  2. 2

    A Hybrid Flower Pollination with p-Hill Climbing Algorithm for Global Optimization by Abou Doush, Iyad

    Published 2021
    “…In this paper, the -hill climbing optimizer is hybridized with the flower pollination algorithm (FPA) as a local refinement operator for global optimization problems. …”
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  4. 4

    Valuation of commodity option prices under a regime-switching model with stochastic convenience yield: Model calibration using flower pollination optimization algorithm by A. Hamdi (17906918)

    Published 2025
    “…We calibrate the option pricing model parameters using the flower pollination optimization algorithm based on the European call option prices in WTI crude oil market. …”
  5. 5

    Chapter 19 - Metaheuristics for optimizing weights in neural networks by Abu Doush, Iyad

    Published 2023
    “…The proposed HOA-MLP is compared against five other comparative methods, which are bat algorithm, harmony search, flower pollination algorithm, sine cosine algorithm, and JAYA algorithm. …”
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  6. 6

    Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm by Odat, Alhaj-Saleh A.

    Published 2024
    “…The model aims to compare the performance of classical perturb and observe (P&O) algorithm, particle swarm optimization (PSO) algorithm, flower pollination algorithm (FPA), and SMC-based tracking techniques. …”
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  7. 7

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

    Published 2022
    “…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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