## 1 Introduction
Another critical gap is the lack of universal frameworks that can systematically tune the trade-off between exploitation and exploration based on problem complexity or runtime feedback. As real-world problems become more interconnected and volatile, there is a growing need for metaheuristics that can self-adapt, learn over time, and operate efficiently in uncertain, dynamic, and large-scale environments. Addressing these gaps is key to pushing the boundaries of what modern optimization algorithms can achieve. The Gaining-Sharing Knowledge-based (GSK) algorithm is a relatively new member of the human-inspired metaheuristic family, originally proposed by [PERSON] et al. in 2020 [16]. It models the natural process of human learning and knowledge transfer, structured into two main stages: the junior phase, where knowledge is accumulated, and the senior phase, where it is shared. This intuitive and behaviorally grounded design has allowed GSK to tackle various real-world problems with promising results. However, like many algorithms, it has its shortcomings. GSK tends to suffer from premature convergence, often getting stuck in local optima, which limits its exploration capabilities. Moreover, its performance is highly sensitive to a set of manually tuned parameters. These parameters are usually determined through trial and error, a process that can be time-consuming, inconsistent, and difficult to generalize across different types of optimization challenges. To overcome the parameter sensitivity issue in the original GSK algorithm, this paper introduces an Auto-Tuning Memory-based Adaptive Local Search (ATMALS) empowered GSK, that is, ATMALS-GSK. This improved version enhances GSK through two main innovations: memory-based automatic parameter tuning and an adaptive local search strategy. Unlike the traditional GSK, ATMALS-GSK dynamically adjusts its key parameters during the optimization process using a Gaussian-based mechanism. This mechanism updates the probability of selecting different parameter values based on their historical contribution to improving fitness. A memory-driven roulette wheel selection (RWS) method, powered by a weighted moving average, guides this process, ensuring the algorithm intelligently favors more effective parameter settings over time. In addition, an adaptive local search strategy is applied to the senior solutions, helping the algorithm better balance exploration and exploitation, escape local optima, and improve overall solution quality. These enhancements work to improve robustness and reduce premature convergence. The performance of ATMALS-GSK is evaluated through experiments on the CEC 2017 benchmark functions. Specifically, the key contributions of this paper can be mentioned as follows:  
* Proposing an adaptive Auto-Tuning Memory-based GSK (ATM-GSK) algorithm, which enhances the original GSK algorithm with dynamic parameter adaptation. * Integration of historical memory-based adaptiveness using a Gaussian-based weighted moving average mechanism that evaluates and adjusts parameter values based on their impact on the fitness function over time. * Development of a novel selection mechanism using a memory-driven RWS strategy that leverages exponential memory-based knowledge and weighted moving averages to prioritize more effective parameter values. This adaptive tuning strategy improves the ability of the algorithm in escaping local optima and enhancing the exploration-exploitation balance. * Incorporating an adaptive local search strategy to improve the exploitation capabilities of the algorithm. This approach enhances solution quality, preserves population diversity, and increases the ability of the algorithm to avoid trapping in local optima. * Validation of the ATM-GSK algorithm through experiments on CEC 2011 and CEC 2017 benchmarks. Results show that ATM-GSK outperforms the original GSK and other metaheuristic algorithms in terms of convergence accuracy and computational robustness. The remainder of this study is structured as follows.