Adapting scientific workflow structures using multi-objective optimization strategies
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
An analysis of unit tests of a flight software product line
Science of Computer Programming
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Task scheduling in dynamic, heterogeneous and distributed grid environments is a complex and challenging issue. On the basis of multi-objective genetic algorithm NSGA-II, the chromosome coding schema is proposed. Also, this paper presents the generating method for the initial population and the genetic operators such as selection, crossover and mutation. The experimental results show that the proposed task scheduling algorithm based on multiobjective combinatorial optimization has higher performance in grid environment.