MMES: Improved Mayfly Algorithm Based on Electrostatic Optimization Algorithm

Cloud computing divides a huge program into countless subtasks through the network, which are calculated and analyzed by multiple servers, and then the results are returned to users. Therefore, the strategy of task scheduling is very important for computing performance. Aiming at the essence of cloud computing task scheduling and the optimization problem of seeking solutions, this paper proposes a hybrid algorithm called MMES algorithm (MA-MIX-ESDA). This algorithm not only guarantees the search space of electrostatic discharge algorithm (ESDA), but also accelerates its convergence speed, and solves the problem that mayfly algorithm (MA) is easy to fall into local optimization. Latin hypercube sampling is used for population initialization, exploration and development are balanced by the direction of the balance vector, and the step size control factor is added to jump out of local optimization. In order to evaluate the performance of the algorithm, 23 groups of test functions commonly used by CEC and 30 benchmark functions of CEC2014 are used to test the global search and local development functions of the algorithm, and the results are compared with the improved algorithm and classical algorithm. Experimental results show that the proposed MMES algorithm is more superior in search space and convergence speed.