We propose a hybrid approach combining population- and trajectory-based metaheuristics to solve continuous optimization problems efficiently. We utilize a Self-Adaptive Binary Space Partitioning (SA-BSP) tree to divide the search space of continuous problems, guiding the hybrid framework toward the most promising subregions. To address the issue of premature convergence, we implement a “Finder-Tracker agents” mechanism. The hybrid framework is structured in three main phases. In the first phase, the SA-BSP tree serves as a memory unit within the population-based algorithm, capturing critical information about the explored areas, building the fitness landscape, and partitioning the search space. In the second phase, an intelligent controller is introduced to balance exploration and exploitation through the combined efforts of population- and trajectory-based algorithms. In the third phase, the search is confined to the most promising sub-region identified earlier. The trajectory-based algorithm then leverages the best solution’s fitness value and position to effectively explore this restricted search space. We compare the proposed approach with various metaheuristics in ten well-established unimodal and multimodal continuous optimization benchmarks. The results highlight the ability of the new hybrid approach to identify global optima while reducing execution time.