Patron–Prophet Artificial Bee Colony Approach for Solving Numerical Continuous Optimization Problems

The swarm-based Artificial Bee Colony (ABC) algorithm has a significant range of applications and is competent, compared to other algorithms, regarding many optimization problems. However, the ABC’s performance in higher-dimension situations towards global optima not on par with models due its deficiency balancing intensification diversification. In this research, two different strategies are applied for improvement search capability ABC multimodal space. ABC, first strategy, Patron–Prophet, assessed scout bee phase incorporate cooperative nature. This strategy works based donor–acceptor concept. addition, self-adaptability approach included balance helps optimal solutions without premature convergence. explores unexplored regions better insight, more profound occurs discovered areas. second controls trap being local diversification pulse intensification. proposed model, named PP-ABC, was evaluated mathematical benchmark functions prove efficiency comparison existing models. Additionally, standard statistical analyses show outcome over techniques. model three-bar truss engineering design problem validate model’s efficacy, results were recorded.