The group search optimizer (GSO)

which is inspired by animal searching behaviour and group living theory

is a novel optimization algorithm.In this paper

a novel group search optimizer called global group search optimizer (GGSO) is proposed to improve the performance of standard GSO.In the optimizing

the initial population of GGSO is generated uniformly in the search space.Early in the algorithm

GSO evolutionary strategy is retained and PSO evolutionary strategy is adopted during the later computation period.The main approaches included introducing crossover operation in each iteration to increase the diversity of individuals

breaching the restrictions of local optimization points with a new chaotic disturbance mechanism and mutation operation during the later computation period.Tests are carried out through four standard test functions on GSO

LDWPSO and GGSO independently

the results shows that GGSO has a preferable convergence rate and accuracy.The application of gasoline blending online shows that GGSO is effective.