A neighborhood centroid opposition-based grasshopper optimization algorithm

Abstract

Grasshopper Optimization algorithm (GOA) is proposed in recent years as an intelligent optimization algorithm. GOA has the advantages of simple structure, less parameters, the characteristics of rapid convergence, but it's also need to further improve the population diversity and convergence precision. Therefore, for improve the development and exploration and exploitation ability of my algorithm, my paper proposed a Neighborhood Centroid Opposition-Based Grasshopper Optimization algorithm (NCOGOA). In NCOGOA, population is divided into multiple areas, direct interaction between the individual and to calculate reference point reverse point, for sufficient the group of search experience while maintaining the diversity of population. In this paper, NCOGOA is verified by 3 reference functions. Compared with five different classical algorithms, the experimental results find that GOA can get more accurate solutions than other group algorithms and with fast convergence and good stability.