Adaptive Metaheuristic Selection in Island-Based Optimization

The optimization of complex problems remains a significant challenge across various domains of science and engineering. This paper introduces a novel approach to island-based optimization that dynamically adapts metaheuristic selection during runtime, extending the Diversity-driven Cooperating Portfolio of Metaheuristics (DdCPM) framework. Our method integrates additional metaheuristics beyond the original implementation and proposes adaptation strategies that dynamically reconfigure the algorithm portfolio based on performance indicators and population characteristics. Experimental results across both discrete and continuous optimization benchmarks demonstrate that adaptive metaheuristic selection enhances solution quality and convergence rates compared to static approaches. The proposed framework represents an advancement in hybrid optimization systems, offering improved performance through intelligent adaptation mechanisms that correspond to the evolving state of the search process.