Design and Evaluation of a Hybrid ABC–ACO Optimization Algorithm

Abstract

Optimization issues are fundamental challenges in the fields of artificial intelligence due to the complexity of search spaces and the increasing number of decision variables, which requires the development of algorithms capable of achieving an effective balance between exploring the solution space and promoting promising solutions in the search process.

In this research, a hybrid interactive algorithm was proposed that combines the Artificial Bee Colony (ABC) algorithm and the Ant Colony Optimization (ACO) algorithm, by integrating the search mechanisms of ABC with the pheromone mechanism used in ACO, with the aim of improving the efficiency of guidance towards optimal solutions and accelerating the convergence process.