RG-BAAA: A ReliefF-guided Binary Artificial Algae Algorithm with Adaptive Energy-loss Strategy for High-dimensional Feature Selection

Metaheuristic algorithms offer advantages over traditional methods in handling complex feature dependencies and avoiding local optima, making them increasingly favored in feature selection tasks.The Artificial Algae Algorithm (AAA), a metaheuristic inspired by the behavior of natural algae populations, has demonstrated rapid convergence and efficient feature selection in low-dimensional problems.However, in high-dimensional feature selection tasks, the algorithm often suffers from premature convergence, leading to entrapment in local optima.To address this issue, this paper proposes a ReliefF-Guided Binary Artificial Algae Algorithm (RG-BAAA) specifically designed for high-dimensional feature selection, which effectively guides the search process to overcome premature convergence.Specifically, the ReliefF algorithm is first used to assess feature importance, and the evaluation results are leveraged to guide the initialization of the population, ensuring that the algorithm focuses on key feature regions from the outset, thereby avoiding inefficient searches caused by random initialization.Next, the feature evaluation scores drive a weighted dynamic algae cell selection strategy, enhancing the directional focus of the search process and helping the algorithm quickly avoid local optima, reducing the risk of premature convergence.Additionally, an adaptive energy loss strategy is introduced to balance the exploration and exploitation phases, further improving the search direction and convergence performance.The experimental results using the random forest (the most effective classifier) show that RG-BAAA achieved the highest accuracy rate on all ten high-dimensional biomedical datasets.On the 9Tumors dataset, its accuracy rate was as high as 0.8324, surpassing the accuracy rates of the top ten most advanced meta-heuristic benchmark methods recently.