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About

M.A. Mousa is a robotics and computational intelligence researcher whose work centers on trajectory optimization and path planning for multi-degree-of-freedom robotic systems. With a focused research portfolio spanning 2023 to 2025, Mousa has established a distinctive niche in applying and advancing metaheuristic optimization algorithms to solve complex robotic arm control challenges. Mousa's most significant contributions involve developing and refining optimization strategies for 6 DOF robotic arms, particularly KUKA manipulators. By leveraging the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and pioneering a novel hybrid WGA technique, Mousa has tackled the notoriously difficult inverse kinematics problem while simultaneously minimizing time and energy consumption under multi-objective constraints. The hybrid WGA approach demonstrated superior performance over its individual counterparts, representing a meaningful advancement in the field. Across four key publications accumulating 85 total citations, Mousa has consistently validated algorithms across multiple trajectory paths, ensuring practical reliability and efficiency. The rapid citation growth — notably 28 citations for a 2025 paper — signals growing recognition within the robotics and artificial intelligence communities. For students and researchers exploring intelligent motion planning, autonomous robotics, or bio-inspired optimization, Mousa's body of work offers both rigorous methodology and practical algorithmic frameworks with real-world applicability.