Diversity-driven Cooperating Portfolio of Metaheuristic Algorithms

The paper introduces a novel hybrid island-based framework in which diverse metaheuristics cooperate to effectively explore the search space. A core component of the framework is a diversity-driven migration mechanism, enabling adaptive management of the information flow between islands. Three fundamental aspects of migration - what to migrate, when to migrate, and where to migrate - are thoroughly analyzed, leading to the development of strategies that foster synergy between heterogeneous algorithms. These strategies balance exploration and exploitation, ensuring effective global and local search. The framework was evaluated on a set of diverse optimization benchmarks, both discrete (Traveling Salesman Problem instances) and continuous (BBOB functions). Experimental results demonstrate that the proposed approach surpasses traditional algorithms and their island-based variants in convergence speed, solution quality, and resilience to stagnation. Adaptive mechanisms dynamically adjust migration strategies during the optimization process, further enhancing the framework's effectiveness. The proposed method represents an advancement in hybrid metaheuristic systems, offering scalability and flexibility that are essential for solving complex optimization tasks.