Olivia Mendoza

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About

Olivia Mendoza is a leading researcher in computational intelligence, with a primary focus on fuzzy logic systems and bio-inspired optimization algorithms. Her most cited work, "Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot" (2016, 94 citations), exemplifies her pioneering contributions. In this study, Mendoza introduced a novel hybrid framework that integrates Type-1, Interval Type-2, and Generalized Type-2 Fuzzy Logic Systems to dynamically adjust the alpha and beta parameters of Bee Colony Optimization (BCO). This approach significantly enhances the precision and adaptability of autonomous mobile robot trajectory control, bridging the gap between theoretical fuzzy systems and real-world robotics applications. Her work has been widely recognized for advancing the state-of-the-art in fuzzy parameter adaptation, demonstrating how higher-order fuzzy sets can improve optimization performance under uncertainty. With a strong citation impact, Mendoza’s research continues to inspire new directions in intelligent control systems, making her a key figure in the intersection of fuzzy logic, swarm intelligence, and autonomous robotics.