An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds

The Cloud workflow scheduling is to find proper Cloud resources for the execution of workflow tasks to efficiently utilize resources and meet different user's quality of service requirements. Cloud workflow scheduling is a constrained and NP-complete problem and multi-objective evolutionary algorithms have shown their excellent ability to solve such problem. But most existing works simply use static penalty function to handle constraints which usually result in premature when the constraints become strict. On the other hand, with the search space being more tremendous and chaotic, how to balance the ability of exploring the entire search space and exploiting the important regions during the evolutionary process is increasingly important. In this paper, an adaptive individual-assessment scheme based on evolutionary states is proposed to handle the constraints in multi-objective optimization problems. In addition, the evolutionary parameters are also adjusted accordingly to balance the exploration and exploitation ability. These are distinguishable from most previous studies that directly incorporate multi-objective evolutionary algorithm to search excellent solutions for Cloud workflow scheduling. Experimental results demonstrate the proposed algorithm outperforms other state-of-the-art methods in convergence and diversity, and it also achieves better optimization ability when it is applied to solve Cloud workflow scheduling problem.