Abu Zitar, Raed

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Name
Abu Zitar, Raed
Main Affiliation
Email
raed.zitar@sorbonne.ae
ORCID
0000-0003-2693-2132
Scopus Author ID
8858522100
Status
Currently affiliated

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  • Publication
    Correction to: Multiclass feature selection with metaheuristic optimization algorithms: a review
    (2023)
    Akinola, Olatunji O.
    ;
    Ezugwu, Absalom E.
    ;
    Agushaka, Jeffrey O.
    ;
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
      8
  • Publication
    Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review
    (2023)
    Chakraborty, Sanjoy
    ;
    Saha, Apu Kumar
    ;
    Ezugwu, Absalom E.
    ;
    Agushaka, Jeffrey O.
    ;
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multiobjective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.
      24  13Scopus© Citations 4
  • Publication
    Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems
    (2023)
    Agushaka, Jeffrey O.
    ;
    Ezugwu, Absalom E.
    ;
    Olaide, Oyelade N.
    ;
    Akinola, Olatunji
    ;
    Zitar, Raed 
    ;
    Abualigah, Laith
    This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
      26  6