Ariful Islam Khandaker

Khulna University of Engineering and Technology

Papers

Total Citations

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About

Ariful Islam Khandaker’s research focuses on the intersection of multi-objective optimization, fuzzy logic systems, and neural network control. His major contributions lie in developing intelligent, hybrid algorithms that enhance the performance of fuzzy rule-based controllers. Specifically, Khandaker pioneered the application of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) to automate the design of fuzzy systems, achieving a finer trade-off between interpretability and accuracy in complex control problems. His 2015 work on optimizing fuzzy rule base systems with NSGA-II (3 citations) and his 2016 study on fuzzy neural network controllers (2 citations) are foundational, demonstrating how evolutionary algorithms can tune both the structure and parameters of fuzzy logic controllers. By integrating neural network clustering with genetic optimization, Khandaker’s methods allow for more adaptive and efficient control in dynamic environments. His work is particularly notable for addressing the challenge of multi-objective design in combinatorial optimization, offering practical solutions for engineers seeking robust, high-performance controllers. Khandaker’s research continues to influence the development of smart, self-tuning systems in robotics and industrial automation.