The improved grasshopper optimization algorithm with Cauchy mutation strategy and random weight operator for solving optimization problems
Wu Lei, Jiawei Wu, Tengbin Wang
Abstract
Abstract An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed CMRWGOA, which combines both Random Weight (shorted RWGOA) and Cauchy mutation (termed CMGOA) mechanism into the GOA. The GOA received inspiration from the foraging and swarming habits of grasshoppers. The performance of the CMRWGOA was validated by 23 benchmark functions in comparison with four well-known meta-heuristic algorithms (AHA, DA, GOA, and MVO), CMGOA, RWGOA, and the GOA. The non-parametric Wilcoxon, Friedman, and Nemenyi statistical tests are conducted on the CMRWGOA. Furthermore, the CMRWGOA has been evaluated in three real-life challenging optimization problems as a complementary study. Various strictly extensive experimental results reveal that the CMRWGOA exhibit better performance.