An improved artificial bee colony algorithm based on Bayesian estimation
Chunfeng Wang, Pengpeng Shang, Peiping Shen
Abstract
Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is $$76\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>76</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors.