Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Lévy flight local search and mutation

Abstract

Gravitational search algorithm (GSA) is an evolutionary algorithm developed to solve the global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. In order to improve the GSA performance, this paper develops a novel multi-strategy gravitational search algorithm (MGSA) where the Lévy flight strategy is adopted to increase the local search ability of the global best-known agent; and then the mutation strategy is used to improve the swarm diversity in the evolutionary process; finally, the elitism selection strategy is used to enhance the exploration ability and convergence speed of the swarm. The MGSA method is compared with several methods in 24 famous benchmark functions, and the results demonstrate the superiority of the MGSA method in both search ability and convergence rate. Next, the MGSA method is used to solve the ecological operation problem of the Wu hydropower system. The results indicate that compared with several existing methods, MGSA can obtain better scheduling schemes to make obvious reductions in the inappropriate ecological water volume in different scenarios. Thus, this paper provides a new effective tool for the complex engineering optimization problems.