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As a novel computing paradigm,edge computing has become a significant approach to solve large-scale scientific applications.Aiming at scientific workflow scheduling under edge environment,task computation time and data transmission time are uncertain due to the fluctuation of server processing performance and bandwidth,respectively.In order to help capture and reflect the uncertainty during workflow execution,task computation time and data transmission time are represented as triangular fuzzy numbers (TFN),based on fuzzy theory.Simultaneously,an adaptive discrete fuzzy GA-based particle swarm optimization (ADFGA-PSO) is proposed to minimize fuzzy execution cost of workflow while satisfying deadline constraint.Besides,two-point crossover operator,neighborhood mutation and adaptive multipoint mutation operator of genetic algorithm (GA) are introduced to avoid particles being trapped in local optimum.Experimental results show that,compared with others,scheduling strategy based on ADFGA-PSO can more effectively reduce fuzzy execution cost in regard to deadline-constrained scientific workflow scheduling under edge environment.

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Aiming at the low efficiency of the existing intelligent algorithms in solving the large-scale complex distribution network reconfiguration, a new data structure, called node-depth-degree representation (NDDR), was applied to encode power distribution network, and the NDDR-based genetic operators, which have clear physical meaning, were also constructed. The proposed method could guarantee that the corresponding networks of all chromosomes in the initial population and any chromosome generated in the genetic manipulation satisfy radial constraint, which avoids repeated check and repair of network radial constraint in the existing method and significantly reduces the computational burden. Test results of three sample test systems and three large-scale complex distribution systems show that the optimal solution can be obtained with high probability, which means rapid convergence and good stability of the proposed method. Compared to loop encoding-based GA, NDDR-based GA significantly reduces the computation time and obtains high-quality solution in short time while solving large-scale complex distribution network, which indicates good practical value of the proposed method. [ABSTRACT FROM AUTHOR]