ABC-DE hybrid evolutionary algorithm based on individual preference population selection

To fully leverage the complementary advantages of the Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms for various optimization problems, this paper introduces an ABC-DE hybrid evolutionary algorithm based on individual preference for population selection. This method divides the population into subpopulations using a preference-based selection mechanism and employs ABC and DE for diverse subpopulation search strategies. Additionally, it implements a new resource allocation mechanism that increases computational efforts for promising solutions. Comparative analysis on the CEC2014 benchmark suite with five other current ABC and DE improved algorithms confirms that the proposed hybrid evolutionary algorithm effectively enhances convergence speed, accuracy, and robustness.