Mehnuma Tabassum Omar

Khulna University of Engineering and Technology

Papers

Total Citations

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About

Mehnuma Tabassum Omar is a researcher specializing in computational intelligence, multi-objective optimization, and fuzzy logic systems. Her work focuses on enhancing the performance and interpretability of fuzzy controllers through evolutionary algorithms. In her most-cited paper (2015), she applied the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to optimize fuzzy rule base systems, achieving a finer trade-off between interpretability and precision—a critical challenge in complex combinatorial optimization. Building on this, her 2016 study integrated neural networks with NSGA-II to further refine fuzzy logic controllers, demonstrating a hybrid approach that marries learning capabilities with evolutionary search. Although her citation counts are currently modest (3 and 2 citations respectively), these contributions lay important groundwork for adaptive control systems. Her research is particularly relevant for students and engineers interested in intelligent system design, multi-objective decision-making, and the practical application of genetic algorithms. Omar’s work exemplifies how evolutionary computation can be harnessed to create more efficient, transparent, and robust fuzzy controllers for real-world engineering problems.