This paper investigates the critical aspect of migration timing in hybrid island-based metaheuristic algorithms. Migration timing plays a pivotal role in balancing exploration and exploitation, ensuring that the algorithm avoids premature convergence while effectively exploring the search space. We propose and evaluate several migration timing strategies, including periodic migration, fitness-based triggers, and diversity-driven approaches. Our experiments are conducted on a set of benchmark optimization problems, including both discrete (Traveling Salesman Problem) and continuous (Black-box Optimization Benchmarking) tasks. The results demonstrate that adaptive migration strategies, which dynamically adjust based on population diversity and fitness stagnation, outperform static approaches. This study provides insights into the optimal conditions for triggering migration and offers guidelines for designing more effective hybrid metaheuristic frameworks.