A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition

Abstract

With the advent of cloud manufacturing (CMfg), more and more services in CMfg platforms may provide the same functionality but differ in performance. In order to insure the manufacturing cloud to match the complicated task requirements, composited CMfg service optimal selection (CCSOS) is becoming increasingly important. This study proposes a new approach for such CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificial bee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment. Different-scale CCSOS problems are adopted to evaluate the performance of the proposed HABC. Experimental results have shown that the HABC can find better solutions compared with such algorithms as genetic algorithm, particle swarm optimization, and basic artificial bee colony algorithm.