Biography

Prof. Kalyanmoy Deb

Michigan State University,USA


Email: [email protected]


Qualifications

1991 Ph.D., Engg., Mechanics, University of Alabama, USA

1989 M.Sc., Engg., Mechanics, University of Alabama, USA


Publications(Selected)

  1. Khan, A., & Deb, K. (2026). Following natural evolution closely in devising evolutionary algorithms for certain challenging problems. Philosophical Magazine Letters, 106(1), Article 2695739.
  2. Deb, K., & Khan, A. (2026). Iteration-wise intermediate points saved in an archive as outcome of an optimization algorithm considered unreliable. Swarm and Evolutionary Computation, 107, Article 102452.
  3. Razavi, H. S., Nejadhashemi, A. P., Deb, K., et al. (2026). Artificial Intelligence, Optimization, and Modeling Techniques in Water Resources Management: Challenges and Future Directions. Water Environment Research, 98(6), Article e70412.
  4. Khan, A., & Deb, K. (2026). Population versus point-based optimization algorithms for hierarchical convergence to multiple solutions. Applied Soft Computing, 199, Article 115306.
  5. Guha, R., Mckendrick, R., Feest, B., et al. (2026). Multiobjective Competitive Co-Evolutionary Optimization and Regularity-Based Decision-Making for Two-Agent Wargame Strategy Optimization. IEEE Transactions on Evolutionary Computation, 30(2), 652–666.
  6. Santoshkumar, B., & Deb, K. (2026). Handling Objectives With Heterogeneous Evaluation Times in Surrogate-Assisted Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 30(2), 564–578.
  7. Ahrari, A., Li, X. D., & Deb, K. (2026). A novel framework for generating tunable and scalable benchmark problems for multimodal multiobjective optimization. Swarm and Evolutionary Computation, 103, Article 102349.
  8. Khan, A., & Deb, K. (2026). Gradual Innovative Transitional Solutions Improving Current to a Desired Target: Innovation Path. IEEE Transactions on Evolutionary Computation, 30(1), 378–392.
  9. Ehrgott, M., Koksalan, M., Kadzinski, M., et al. (2026). Fifty years of multi-objective optimization and decision-making: From mathematical programming to evolutionary computation. European Journal of Operational Research, 330(1), 1–25.
  10. Razavi, H. S., Nejadhashemi, A. P., Deb, K., et al. (2026). Artificial Intelligence, Optimization, and Modeling Techniques in Water Resource Management: Interconnections and Emerging Synergies. Water Environment Research, 98(1), Article e70246.
  11. Santoshkumar, B., & Deb, K. (2026). Handling of objectives and constraints with heterogeneous evaluation times for surrogate-assisted evolutionary multi-and many-objective optimization. Swarm and Evolutionary Computation, 100, Article 102260.
  12. Wang, H., Zhu, S. W., Fang, W., et al. (2025). An adaptive weight optimization algorithm based on decision variable grouping for large-scale multi-objective optimization problems. Swarm and Evolutionary Computation, 99, Article 102149.
  13. Gupta, P., Pratihar, D. K., & Deb, K. (2025). Dynamic performance evaluation of evolutionary multi-objective optimization algorithms for gait cycle optimization of a 25-DOFs NAO humanoid robot. Swarm and Evolutionary Computation, 99, Article 102144.
  14. Li, X. T., Zhu, S. W., Fang, W., et al. (2026). A cooperative co-evolutionary algorithm with core-based grouping strategy for large-scale 0-1 knapsack problems. Expert Systems with Applications, 298, Article 129364.
  15. Razavi, H. S., Toscano, G., Nejadhashemi, A. P., et al. (2025). Next-generation techniques for parameter reduction for BMP multiobjective optimization in watershed planning. Environmental Modelling & Software, 193, Article 106651.
  16. Gupta, P., Pratihar, D. K., & Deb, K. (2026). Many-objective robust gait optimization for a 25-DOFs NAO robot using NSGA-III. Engineering Optimization, 58(4), 1152–1190.
  17. Zhu, S. W., Zhang, Y. M., Fang, W., et al. (2025). Regression and relation-assisted evolutionary algorithm for high-dimensional expensive multi-objective optimization. Swarm and Evolutionary Computation, 97, Article 101978.
  18. Pang, L. M., Ishibuchi, H., Deb, K., et al. (2025). MaNSGA-II: Many-Objective NSGA-II. IEEE Transactions on Emerging Topics in Computational Intelligence, 9(6), 3922–3937.
  19. Shankar, M., Ramu, P., & Deb, K. (2025). A directed batch growing self-organizing map based niching differential evolution for multimodal optimization problems. Applied Soft Computing, 172, Article 112862.
  20. Yadav, D., Ramu, P., & Deb, K. (2025). Handling objective preference and variable uncertainty in evolutionary multi-objective optimization. Swarm and Evolutionary Computation, 94, Article 101860.
  21. Kivikangas, J. M., Vilkkumaa, E., Blank, J., et al. (2025). Effects of many conflicting objectives on decision-makers' cognitive burden and decision consistency. European Journal of Operational Research, 322(1), 182–197.
  22. Suresh, A., & Deb, K. (2025). Identifier spaces for representing Pareto-optimal solutions in multi-objective optimization and decision-making. Engineering Optimization, 57(1), 234–260.

Profile Details
https://www.egr.msu.edu/~kdeb/
https://scholar.google.com/citations?user=paTAXiIAAAAJ&hl=vi
https://en.wikipedia.org/wiki/Kalyanmoy_Deb

WOS ResearcherID: AFU-4638-2022

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