Artificial bee colony algorithm with strategy and parameter adaptation for global optimization

The artificial bee colony (ABC) algorithm has been successfully applied to solve a wide range of real-world optimization problems. However, the success of ABC in solving a specific problem crucially depends on appropriately choosing the foraging strategies and its associated parameters. In this paper, we propose a strategy and parameter self-adaptive selection ABC algorithm (SPaABC), in which both employed bees search strategies and their associated control parameter values are gradually self-adaptive by learning from their previous experiences in generating promising solutions. In order to verify the performance of our approach, SPaABC algorithm is compared to many recently related algorithms on eighteen benchmark functions. Experimental results indicate that the proposed algorithm achieves competitive performance on most test instances.