World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
10375
World Ranking
9105
National Ranking
359

Overview

Shahryar Rahnamayan is affiliated with the University of Ontario Institute of Technology in Canada, focusing primarily on research within the field of Computer Science. Their work spans various subfields, particularly in Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, and Management Science and Operations Research.

Their research topics cover a range of areas including AI in cancer detection, metaheuristic optimization algorithms research, advanced multi-objective optimization algorithms, radiomics and machine learning in medical imaging, digital imaging for blood diseases, machine learning and data classification, and evolutionary algorithms and applications.

Among recent papers authored or co-authored by Rahnamayan are:

  • "Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant" (2021, Applied Soft Computing)
  • "Biased data, biased AI: deep networks predict the acquisition site of TCGA images" (2023, Diagnostic Pathology)
  • "Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection" (2020, Information Sciences)
  • "Semisupervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework" (2022, IEEE Transactions on Geoscience and Remote Sensing)
  • "Machine learning-based framework to cover optimal Pareto-front in many-objective optimization" (2022, Complex & Intelligent Systems)

Frequent co-authors collaborating with Rahnamayan include Azam Asilian Bidgoli, Hamid R. Tizhoosh, Taher Dehkharghanian, and Kalyanmoy Deb.

The scientist has published extensively in certain venues, with a notable presence in arXiv (Cornell University), SSRN Electronic Journal, Scientific Reports, Research Square, and Applied Soft Computing.

Rahnamayan has also contributed to book literature, with a publication through IntechOpen titled Swarm Intelligence - Recent Advances and Current Applications released in 2022.

Best Publications

  • Opposition-Based Differential Evolution

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Enhancing particle swarm optimization using generalized opposition-based learning

    Hui Wang;Zhijian Wu;Shahryar Rahnamayan;Yong Liu

  • Metaheuristics in large-scale global continues optimization

    Sedigheh Mahdavi;Mohammad Ebrahim Shiri;Shahryar Rahnamayan

  • Diversity enhanced particle swarm optimization with neighborhood search

    Hui Wang;Hui Sun;Changhe Li;Shahryar Rahnamayan

  • Quasi-oppositional Differential Evolution

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • A novel population initialization method for accelerating evolutionary algorithms

    Shahryar Rahnamayan;Hamid R. Tizhoosh;Magdy M. A. Salama

  • Opposition based learning: A literature review

    Sedigheh Mahdavi;Shahryar Rahnamayan;Kalyanmoy Deb

  • Opposition versus randomness in soft computing techniques

    Shahryar Rahnamayan;Hamid R. Tizhoosh;Magdy M. A. Salama

  • Gaussian Bare-Bones Differential Evolution

    Hui Wang;S. Rahnamayan;Hui Sun;M. G. H. Omran

  • Multi-strategy ensemble artificial bee colony algorithm

    Hui Wang;Zhijian Wu;Shahryar Rahnamayan;Hui Sun

  • Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

    Hui Wang;Zhijian Wu;Shahryar Rahnamayan

  • Opposition-Based Differential Evolution Algorithms

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Firefly algorithm with random attraction

    Hui Wang;Wenjun Wang;Hui Sun;Shahryar Rahnamayan

  • Opposition-Based Differential Evolution for Optimization of Noisy Problems

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems

    Hui Wang;Shahryar Rahnamayan;Zhijian Wu

  • Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

    Hui Wang;Zhihua Cui;Hui Sun;Shahryar Rahnamayan

  • Opposition-Based Differential Evolution (ODE) with Variable Jumping Rate

    S. Rahnamayan;H.R. Tizhoosh;M.M.A. Salama

  • Solving large scale optimization problems by opposition-based differential evolution (ODE)

    Shahryar Rahnamayan;G. Gary Wang

  • A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems

    Hui Wang;Wenjun Wang;Hui Sun;Zhihua Cui

  • Center-based sampling for population-based algorithms

    Shahryar Rahnamayan;G. Gary Wang

  • Image thresholding using micro opposition-based Differential Evolution (Micro-ODE)

    S. Rahnamayan;H.R. Tizhoosh

Frequent Co-Authors

Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
Magdy M. A. Salama
Magdy M. A. Salama University of Waterloo
G. Gary Wang
G. Gary Wang University of Science and Technology of China
Jeng-Shyang Pan
Jeng-Shyang Pan Shandong University of Science and Technology
Zhihua Cui
Zhihua Cui Taiyuan University of Science and Technology
Bekir Sami Yilbas
Bekir Sami Yilbas King Fahd University of Petroleum and Minerals
Ebrahim Esmailzadeh
Ebrahim Esmailzadeh University of Ontario Institute of Technology
Martin Agelin-Chaab
Martin Agelin-Chaab University of Ontario Institute of Technology
N. H. Ravindranath
N. H. Ravindranath Indian Institute of Science

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