Search Results - "Khanum, Rashida Adeeb"

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  1. 1

    Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization by Khanum, Rashida Adeeb, Jan, Muhammad Asif, Aldegheishem, Abdulaziz, Mehmood, Amjad, Alrajeh, Nabil, Khanan, Akbar

    Published in IEEE access (2020)
    “…Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing…”
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    Journal Article
  2. 2

    Near Feasibility Driven Adaptive Penalty Functions Embedded MOEA/D by Khan, Akhtar Munir, Jan, Muhammad Asif, Sagheer, Muhammad, Khanum, Rashida Adeeb, Uddin, Muhammad Irfan, Ahmad, Shafiq, Huda, Shamsul

    Published in IEEE access (2023)
    “…This work extends a near feasibility threshold (NFT) based adaptive penalty function for constrained multiobjective optimization. The NFT zone adjoining the…”
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    Journal Article
  3. 3

    Boosting constrained teaching–learning based optimization algorithm through group discussion by Sagheer, Muhammad, Jan, Muhammad Asif, Bonyah, Ebenezer, Mashwani, Wali Khan, Shah, Zahir, Khanum, Rashida Adeeb

    Published in Scientific African (01-06-2024)
    “…Teaching–learning based optimization algorithm (TLBO) is an efficient population-based nature-inspired meta-heuristic designed initially to solve unconstrained…”
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    Journal Article
  4. 4

    Hybrid Stochastic Ranking for Constrained Optimization by Jan, Muhammad Asif, Sagheer, Muhammad, Khan, Hidayat Ullah, Uddin, Muhammad Irfan, Khanum, Rashida Adeeb, Mahmoud, Marwan, Ikramullah, Mast, Noor

    Published in IEEE access (2020)
    “…Teaching learning based optimization (TLBO) algorithm is a distinguished nature-inspired population-based meta-heuristic, which is basically designed for…”
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    Journal Article
  5. 5

    On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution by Javed, Hassan, Jan, Muhammad Asif, Tairan, Nasser, Mashwani, Wali Khan, Khanum, Rashida Adeeb, Sulaiman, Muhammad, Khan, Hidayat Ullah, Shah, Habib

    Published in Mathematics (Basel) (01-07-2019)
    “…Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution…”
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    Journal Article
  6. 6

    The Physical Significance of Time Conformal Minkowski Spacetime by Ali, Farhad, Jan, Muhammad Asif, Mashwani, Wali Khan, Khanum, Rashida Adeeb, Khan, Hidayat Ullah

    “…The Minkowsiki spacetime is flat and there is no source of gravitation. The time conformal factor is adding some cuvature to this spacetime which introduces…”
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    Journal Article
  7. 7

    A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D by Jan, Muhammad Asif, Khanum, Rashida Adeeb

    Published in Applied soft computing (01-01-2013)
    “…Penalty functions are frequently employed for handling constraints in constrained optimization problems (COPs). In penalty function methods, penalty…”
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    Journal Article
  8. 8

    Threshold Based Dynamic and Adaptive Penalty Functions for Constrained Multiobjective Optimization by Jan, Muhammad Asif, Tairan, Nasser, Khanum, Rashida Adeeb

    “…Penalty functions are frequently used for dealing with constraints in constrained optimization. Among different types of penalty functions, dynamic and…”
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    Conference Proceeding
  9. 9

    Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization by Khanum, Rashida Adeeb, Jan, Muhammad Asif, Aldegheishem, Abdulaziz, Mehmood, Amjad, Alrajeh, Nabil, Khanan, Akbar

    Published in Access, IEEE (2020)
    “…Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing…”
    Get full text
    Standard
  10. 10

    Centroid-based Initialized JADE for global optimization by Khanum, R. A., Jan, M. A.

    “…JADE is a recent adaptive version of nature inspired algorithm, Differential Evolution (DE). It has shown considerable performance improvement on a set of well…”
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    Conference Proceeding