Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition

Service composition and optimal selection (SCOS) is a key problem in cloud manufacturing (CMfg). The present study proposed a multi-objective hybrid artificial bee colony (HABC) algorithm to address the SCOS problem in consideration of both quality of service (QoS) and energy consumption, to which an improved solution update equation with multiple dimensions of perturbation was adopted in the employed bee phase. Likewise, a cuckoo search-inspired Lévy flight was employed in the onlooker bee phase to overcome basic artificial bee colony (ABC) drawbacks such as poor exploitation and slow convergence. Moreover, a parameter adaptive strategy was applied to adjust the perturbation rate and step size of the Lévy flight to improve the performance of the algorithm. The proposed algorithm was first tested on 21 multi-objective benchmark problems and compared with four other state-of-the-art multi-objective evolutionary algorithms (MOEAs). The effect of the improvement strategies was then experimentally verified. Finally, the HABC was applied to solve multiscale SCOS problems using comparison experiments, which resulted in more competitive results and outperformed other MOEAs.