Trend-Aware Mechanism for Metaheuristic Algorithms

In metaheuristic algorithms, historical search-position data often remain underutilized despite their potential to reveal valuable movement trends and promising search directions. To address this limitation, we propose the Trend-Aware Mechanism (TAM), which leverages historical position information to enhance the position updating process. TAM identifies the primary direction of movement by deriving a trend line from the population’s positions over the two most recent iterations. It evaluates candidate optimal positions by assessing the fitness of the K nearest points along this trend line. To effectively balance exploration and exploitation, TAM employs an adaptive covariance mechanism to generate high-dimensional random vectors, dynamically adjusting the update strategies. We integrate TAM with four prominent metaheuristic algorithms – PSO, SHADE, JaDE, and CMA-ES – and conduct an extensive parameter sensitivity analysis to ensure robustness. Comparative evaluations across five performance metrics demonstrate that TAM significantly improves search efficiency and consistently achieves superior results on standard benchmark functions. Moreover, TAM’s practical applicability is validated through real-world problems in engineering design, feature selection, and photovoltaic model parameter extraction. The open-source implementation of TAM will be publicly available at https://github.com/junbolian/Trend-Aware-Mechanism.