An improved artificial bee colony algorithm based on Bayesian estimation

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