The Metaheuristic Mavericks team is a collaborative group of researchers focused on the development and application of innovative metaheuristic algorithms. Led by Dr. Saptadeep Biswas, the team includes Binanda Maiti, Gyan Singh, Dr. Uttam Kumar Bera, Dr. A. E. Ezugwu, and Dr. Laith Abualigah. Together, they are dedicated to advancing global optimization techniques through their research in various metaheuristic strategies.
The team's work emphasizes the integration of advanced methodologies such as Differential Evolution and dynamic learning strategies to tackle complex engineering design problems. Their contributions to the field are reflected in their publications, which explore novel approaches to optimization, showcasing their commitment to improving solution quality and efficiency in engineering applications. The Metaheuristic Mavericks are at the forefront of research that bridges theoretical advancements with practical applications, making significant strides in the optimization landscape.
Published Projects:
2024:
Biswas, S., Shaikh, A., Ezugwu, A. E., Greeff, J., Mirjalili, S., Bera, U. K., & Abualigah, L. (2024). Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems. Neural Computing and Applications, 36(19), 11137–11170. Link (SCIE, Q1, IF 4.5).
Singh, G., Biswas, S., Maiti, B., & Bera, U. K. (2024, November). A Novel Hybrid Gazelle Optimization Algorithm with Differential Evolution for Solving Engineering Design Problems. In 2024 IEEE Silchar Subsection Conference (SILCON 2024) (pp. 1-6). IEEE.
2025:
Biswas, S., Singh, G., Maiti, B., Ezugwu, A. E. S., Saleem, K., Smerat, A., Abualigah, L. & Bera, U. K. (2025). Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications. Computer Methods in Applied Mechanics and Engineering, 434, 117588. Link (SCIE, Q1, IF 6.9).
Maiti, B., Biswas, S., Ezugwu, A. E. S., Bera, U. K., Alzahrani, A. I., Alblehai, F., & Abualigah, L. (2025). Enhanced crayfish optimization algorithm with differential evolution’s mutation and crossover strategies for global optimization and engineering applications. Artificial Intelligence Review, 58(3), 69. (SCIE, Q1, IF 10.7).
Maiti, Binanda; Biswas, Saptadeep; Bera, Uttam Kumar; Deb, Madhujit; Jia, Heming; Saleem, Kashif; Migdady, Hazem; Smerat, Aseel; Abualigah, Laith. A Chaotic Boost: The Chaotic Crayfish Optimization Algorithm for Superior Solution Quality. Optimal Control Applications And Methods, 2025. (SCIE, IF 2).
Ongoing and Submitted Projects:
Enhanced Global Optimization Using a Novel Hybrid Sine Cosine-Gazelle Algorithm with Brownian Motion and Lévy Flight Mechanisms (Accepted, International Journal of Computational Intelligence Systems).
A Novel Hybrid Optimizer Based on Coati Optimization Algorithm and Differential Evolution for Global Optimization and Constrained Engineering Problems (Accepted, International Journal of Computational Intelligence Systems).
We are excited to share our progress and look forward to continuing our research in the field of metaheuristic optimization!
Dr. Saptadeep Biswas