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The rapid development of Internet of Things (IoT) technology has accumulated a large amount of data, which needs to be stored, processed and deeply analyzed to meet the specific goals and needs of users. As an emerging computing model, Fog computing can allocate a large number of computing resources reasonably. In order to solve the problem of insufficient population diversity and low algorithm efficiency, Aiming at the task scheduling problem of Bag-of-Tasks(BoT) application in cloud and fog environment, a multi-strategy fusion Mayfly Algorithm was proposed. The method of improving the individual learning coefficient and the global learning coefficient is used to significantly improve the convergence speed, local search ability, and global search ability, and then the method of improving the social positive attraction coefficient is used to balance the development and exploration ability of the algorithm and help the algorithm to get rid of the local optimum. The main goal of the logarithm Mayfly Algorithm (lMA) is to complete the tasks of the IoT task package in the fog system efficiently with low cost in terms of reducing execution time and operating costs. The improved algorithms were compared with Mayfly Algorithm (MA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Tyrannosaurus Optimization Algorithm (TROA), Harris Hawks Optimization (HHO), Reptile Search Algorithm (RSA) and Red-Tailed Hawk Algorithm (RTH), and the results showed that lMA was significantly improved in terms of reducing processing time and operating cost. The performance of lMA is verified, and the results show that the model can not only save transmission energy consumption but also have good convergence.
Sui et al. (Mon,) studied this question.