Hybrid Equilibrium-Differential Evolution for Optimizing Robotic Gripper Design and Control

This paper introduces the EODE (Equilibrium Optimizer with Differential Evolution) algorithm a novel hybrid algorithm for solving robotic gripper optimization problem. The design and control of robotic grippers involve optimizing multiple interdependent parameters, such as grip strength, joint angles, actuator efficiency, and structural stability, while adhering to stringent operational constraints. EODE combines the exploration strength of the Equilibrium Optimizer (EO), which efficiently explores diverse regions of the search space in the early stages, with the exploitation capabilities of Differential Evolution (DE), which refines solutions in the later stages to ensure convergence toward optimal configurations. EODE was benchmarked against 19 state-of-the-art optimization techniques, including SMA, WSO, and CSA, demonstrating superior performance by achieving the best fitness value of 2.72E+00, with consistent and stable results across runs. These findings validate EODE's ability to navigate high-dimensional search spaces and handle complex constraints effectively. This makes EODE a promising tool for solving intricate engineering problems, particularly in robotic design and control, where reliability, precision, and computational efficiency are critical.