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Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing
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作者 Shasha Zhao Huanwen Yan +3 位作者 Qifeng Lin xiangnan feng He Chen Dengyin Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第1期1135-1156,共22页
Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall... Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental. 展开更多
关键词 Cloud computing distributed processing evolutionary artificial bee colony algorithm hierarchical particle swarm optimization load balancing
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Mediation effects of online public attention on the relationship between air pollution and precautionary behavior
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作者 Ge Xu xiangnan feng +1 位作者 Yiwei Li Jianmin Jia 《Journal of Management Science and Engineering》 2022年第1期159-172,共14页
This study investigates the mediation effects of online public attention on the relationship between air pollution and precautionary behavior based on a merged real-world data set that includes daily air quality,Inter... This study investigates the mediation effects of online public attention on the relationship between air pollution and precautionary behavior based on a merged real-world data set that includes daily air quality,Internet search and media indices,social media discussions,and product purchases.Using a Bayesian structural equation modeling approach,we show that online public attention to air pollution increases when air pollution increases,and such attention is captured by more media reports,social media discussions,and Internet searches.A comprehensive relationship involving direct and indirect effects between air pollution and precautionary behavior is established.Air pollution has a positive effect on proactive defensive behaviors,reflected in increased purchases of preventive products,and this effect is partially mediated by online media coverage and the public's Internet searches.Air pollution also motivates passive defensive behaviors,reflected in decreased purchases of outdoor sports products,and this effect is partially mediated by social media coverage.These results suggest that governments could improve the quality of policy making by considering the different roles of various forms of online public attention in the public's risk perceptions of and reactions to air pollution. 展开更多
关键词 Air pollution Precautionary behaviors Risk perception Internet search Social media Bayesian structural equation modeling
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