Now showing 1 - 2 of 2
  • Publication
    Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications
    (2023) ;
    Daoud, Mohammad Sh.
    ;
    Shehab, Mohammad
    ;
    Al-Mimi, Hani M.
    ;
    Abualigah, Laith
    ;
    Shambour, Mohd Khaled Yousef
    This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.
      22  11
  • Publication
    Revolutionizing sustainable supply chain management: A review of metaheuristics
    (2023) ;
    Abualigah, Laith
    ;
    Hanandeh, Essam Said
    ;
    Thanh, Cuong-Le
    ;
    Khatir, Samir
    ;
    Gandomi, Amir H.
    This paper reviews the application of metaheuristics for optimized sustainable supply chain management (SSCM). This paper explores the potential of metaheuristics to improve the supply chain’s sustainability while enhancing its efficiency and competitiveness. The paper provides an overview of the principles of SSCM and the challenges businesses face in achieving sustainable supply chain management. It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. The paper also identifies the key factors that influence the success of using metaheuristics for SSCM, such as the choice of algorithm, problem complexity, and data quality. Finally, the paper provides recommendations for future research in this area and highlights the potential of metaheuristics to promote sustainable supply chain management. The review suggests that metaheuristics can be a valuable tool for optimizing sustainable supply chain management and improving supply chain operations’ sustainability, efficiency, and competitiveness.
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