Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition
作者： Zhi-Zhong Liu , Dian-Hui Chu , Cheng Song , Xiao Xue , Bao-Yun Lu
DOI: 10.1016/J.INS.2015.08.004
关键词:
摘要: Inspired by the evolution process of human intelligence and social learning theory, a new swarm algorithm paradigm named optimization (SLO) is proposed. SLO consists three co-evolution spaces: bottom micro-space, where genetic occurs; middle layer space, individuals enhance their through imitation observational learning; knowledge extracted from delivered to top layer, which called belief culture established accumulation used guide individuals' in micro-space regularly. an model for problems, concrete could be generated embodying SLO's spaces. Moreover, demonstrate how employ verify its superiority, this paper proposes specific (S-SLO) solve problem QoS-aware cloud service composition. S-SLO constructed integrating improved differential evolutionary (DE) cognitive (SCO) into respectively. Finally, experimental results performance comparison show that both effective efficient. This work expected explore novel with better search capabilities convergence rates, as well extend theory algorithm.
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