# An Improved Artificial Lemming Algorithm Integrating Non-Uniform Mutation and Q-Learning Adaptation for Underwater Manipulator Controller Tuning

## TL;DR

This paper improves the Artificial Lemming Algorithm by integrating non-uniform mutation and Q-learning to enhance optimization performance and apply it to underwater manipulator controller tuning.

## Contribution

The novel integration of non-uniform mutation, relative advantage learning, and Q-learning in the Artificial Lemming Algorithm for improved optimization.

## Key findings

- IALA achieves a Friedman mean rank of 1.25 on CEC2022, outperforming other algorithms.
- The Q-learning adaptive mechanism is identified as the most critical contributor to IALA's performance.
- IALA successfully tunes underwater manipulator controllers, demonstrating real-world applicability.

## Abstract

To address the rapid population diversity loss and premature convergence of the Artificial Lemming Algorithm (ALA) in complex optimization problems, this paper proposes an Improved Artificial Lemming Algorithm (IALA) with multi-strategy enhancements inspired by lemming behavior. First, a non-uniform mutation operator and a nonlinear step-size strategy are introduced to strengthen local optima escape capability and optimization precision. Second, inspired by the foraging and positioning behavior of lemmings, a relative advantage learning strategy is designed to reduce dependence on the global best individual, further enhancing the algorithm’s exploration ability. Finally, a Q-learning-based adaptive mechanism is integrated to intelligently orchestrate five lemming-inspired behavioral modes through a nonlinear reward function, enabling adaptive switching among search patterns. Comparative experiments on the CEC2022 benchmark suite demonstrate that IALA achieves a Friedman mean rank of 1.25, ranking first with a significant margin. Compared with the original ALA and other six classical and state-of-the-art metaheuristic algorithms, and four recently proposed improved ALA variants (EALA, IALA_Tan, DMSALAs, and MsIALA), the Wilcoxon rank-sum test confirms that IALA is significantly outperformed in only 2 out of 120 pairwise comparisons, exhibiting remarkable advantages in optimization accuracy, convergence speed, and robustness. Ablation experiments validate the synergistic necessity of all three strategies, with the Q-learning adaptive mechanism identified as the most critical contributor. Exploration–exploitation balance analysis and search history visualization further confirm that IALA achieves a smooth adaptive transition from global exploration to local exploitation. Space complexity analysis reveals that the Q-table introduces only approximately 19.5 KB of fixed additional overhead, which becomes negligible for high-dimensional problems. Furthermore, IALA is successfully applied to the parameter tuning of underwater manipulator controllers, verifying its efficiency and reliability in real-world engineering applications.