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D-Index & Metrics

Computer Science

D-Index
35
Citations
4171
World Ranking
11822
National Ranking
361

Overview

Saber M. Elsayed is affiliated with the University of New South Wales in Australia. Their research primarily spans the fields of Computer Science and Engineering, with a focus on several subfields including Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Industrial and Manufacturing Engineering, and Nuclear and High Energy Physics.

The scientist's recent papers include the following:

  • Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling (2021, IEEE Access)
  • Large-scale evolutionary optimization: A review and comparative study (2024, Swarm and Evolutionary Computation)
  • Solving electric vehicle-drone routing problem using memetic algorithm (2023, Swarm and Evolutionary Computation)
  • An evolutionary approach for resource constrained project scheduling with uncertain changes (2020, Computers & Operations Research)
  • Weighted pointwise prediction method for dynamic multiobjective optimization (2020, Information Sciences)

The scientist's research covers several main topics such as Advanced Multi-Objective Optimization Algorithms, Metaheuristic Optimization Algorithms Research, Resource-Constrained Project Scheduling, Evolutionary Algorithms and Applications, Scheduling and Optimization Algorithms, Particle Detector Development and Performance, and Robotic Path Planning Algorithms.

Frequent co-authors include:

  • Ruhul Sarker
  • Daryl Essam
  • Carlos A. Coello Coello
  • Kyle Robert Harrison
  • Ivan L. Garanovich

Elsayed has published extensively in several venues, with the most prominent being IEEE Access and Swarm and Evolutionary Computation. Other frequent publication venues include the Journal of Instrumentation, arXiv (Cornell University), and Applied Soft Computing.

In addition to journal articles, Saber M. Elsayed has authored a book titled Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling, published by Springer Nature in 2021.

Best Publications

  • Differential Evolution With Dynamic Parameters Selection for Optimization Problems

    Ruhul A. Sarker;Saber M. Elsayed;Tapabrata Ray

  • A new genetic algorithm for solving optimization problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems

    Karam M. Sallam;Saber M. Elsayed;Ripon K. Chakrabortty;Michael J. Ryan

  • Multi-operator based evolutionary algorithms for solving constrained optimization problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems

    S. M. Elsayed;R. A. Sarker;D. L. Essam

  • Evolutionary Algorithms for Dynamic Economic Dispatch Problems

    M. F. Zaman;Saber M. Elsayed;Tapabrata Ray;Ruhul A. Sarker

  • GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Consolidated optimization algorithm for resource-constrained project scheduling problems

    Saber Elsayed;Ruhul Sarker;Tapabrata Ray;Carlos Coello Coello

  • Self-adaptive mix of particle swarm methodologies for constrained optimization

    Saber M. Elsayed;Ruhul A. Sarker;Efrén Mezura-Montes

  • Landscape-based adaptive operator selection mechanism for differential evolution

    Karam M. Sallam;Saber M. Elsayed;Ruhul A. Sarker;Daryl Leslie Essam

  • Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization

    Saber M. Elsayed;Ruhul A. Sarker;Daryl Leslie Essam;Noha M. Hamza

  • Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems

    Forhad Zaman;Saber M. Elsayed;Tapabrata Ray;Ruhul A. Sarker

  • A self-adaptive combined strategies algorithm for constrained optimization using differential evolution

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Large-scale evolutionary optimization: A review and comparative study

    Unknown

  • A genetic algorithm for solving the CEC'2013 competition problems on real-parameter optimization

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • On an evolutionary approach for constrained optimization problem solving

    Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam

  • Quantum-Inspired Genetic Algorithm for Resource-Constrained Project-Scheduling

    Hatem M. H. Saad;Ripon K. Chakrabortty;Saber Elsayed;Michael J. Ryan

  • Landscape-assisted multi-operator differential evolution for solving constrained optimization problems

    Karam M. Sallam;Saber M. Elsayed;Ruhul A. Sarker;Daryl Leslie Essam

  • Adaptive Sorting-Based Evolutionary Algorithm for Many-Objective Optimization

    Chao Liu;Qi Zhao;Bai Yan;Saber Elsayed

  • Testing united multi-operator evolutionary algorithms-II on single objective optimization problems

    Saber Elsayed;Noha Hamza;Ruhul Sarker

  • Differential evolution framework for big data optimization

    Saber M. Elsayed;Saber M. Elsayed;Ruhul A. Sarker

  • Neurodynamic differential evolution algorithm and solving CEC2015 competition problems

    Karam M. Sallam;Ruhul A. Sarker;Daryl L. Essam;Saber M. Elsayed

Frequent Co-Authors

Ruhul A. Sarker
Ruhul A. Sarker University of New South Wales
Daryl Essam
Daryl Essam University of New South Wales
Tapabrata Ray
Tapabrata Ray University of New South Wales
Hussein A. Abbass
Hussein A. Abbass University of New South Wales
Michael J. Ryan
Michael J. Ryan University of New South Wales
Ripon Kumar Chakrabortty
Ripon Kumar Chakrabortty University of New South Wales
Qi Zhao
Qi Zhao University of Minnesota
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University

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