Auto-Tuning Memory-Based Adaptive Local Search Gaining–Sharing Knowledge-Based Algorithm for Solving Optimization Problems

The Gaining–Sharing Knowledge-based (GSK) algorithm is a human-inspired metaheuristic that models how people learn and disseminate knowledge across their lifetime. It has shown promising results across a range of engineering optimization problems. However, one of its major limitations lies in the use of fixed parameters to guide the search process, which often causes the algorithm to get stuck in local optima. To address this challenge, we propose an Auto-Tuning Memory-based Adaptive Local Search (ATMALS) empowered GSK, that is, ATMALS-GSK. This enhanced version of GSK introduces two key improvements: adaptive local search and memory-driven automatic tuning of parameters. Rather than relying on fixed values, ATMALS-GSK continuously adjusts its parameters during the optimization process. This is achieved through a Gaussian distribution mechanism that iteratively updates the likelihood of selecting different parameter values based on their historical impact on the fitness function. This selection process is guided by a weighted moving average that tracks each parameter’s contribution to fitness improvement over time. To further reduce the risk of premature convergence, an adaptive local search strategy is embedded, facilitating the algorithm’s escape from local traps and guiding it toward more optimal regions within the search domain. To validate the effectiveness of the ATMALS-GSK algorithm, it is evaluated on the CEC 2011 and CEC 2017 benchmarks. The results indicate that the ATMALS-GSK algorithm outperforms the original GSK, its variants, and other metaheuristics by delivering greater robustness, quicker convergence, and superior solution quality.