Leveraging Inter-Generational Knowledge Transfer in Large-Scale Global Optimization

Large-scale global optimization (LSGO) presents significant challenges due to the high dimensionality and complexity of the search space. We propose an IGKT (Inter-Generational Knowledge Transfer) optimization method incorporating a novel knowledge transfer mechanism to address these challenges. The proposed mechanism enables the algorithm to reduce reliance on stochastic exploration and enhance convergence efficiency by transferring information from the best-performing individuals across generations, guiding the population toward promising regions in the search space. Experimental results on the CEC2013 LSGO benchmark suite demonstrate that IGKT outperforms several state-of-the-art algorithms across various tested functions, achieving superior convergence speed and solution quality. Additionally, the IGKT framework handles both separable and non-separable functions and tasks, including those with overlapping and highly coupled variables. In summary, IGKT represents a powerful tool for addressing complex, high-dimensional optimization problems, providing a robust and adaptable solution for LSGO.