Reinforcement Learning Enhanced Archimedes Optimization Algorithm for Robot Path Planning

This paper introduces the Reinforcement Learning Enhanced Archimedes Optimization Algorithm (RLAOA), an improved variant of the traditional Archimedes Optimization Algorithm (AOA) that incorporates reinforcement learning via Q-learning to enhance performance. The proposed RL-AOA leverages Q-learning to dynamically balance exploration and exploitation, leading to more efficient optimization. To evaluate its effectiveness, RL-AOA has been applied to a path planning problem in a 2D environment with obstacles. The algorithm optimizes a trajectory represented by control points interpolated with cubic splines while minimizing a cost function that accounts for both path length and obstacle avoidance. Simulation results demonstrate that RL-AOA significantly outperforms the original AOA as well as other metaheuristic algorithms, namely SCA and ABC, in terms of convergence speed, mean cost, and best-case cost. Moreover, RL-AOA exhibits greater stability with the smallest standard deviation. Convergence analysis further confirms that RL-AOA achieves superior solutions in fewer iterations, validating its potential for complex path planning tasks.