In order to improve the slow convergence rate and low stability of WOA

elite opposition-based golden-sine whale optimization algorithm is proposed. Elite opposition-based learning strategy is used to improve the diversity and quality of the population so that the convergence rate can be promoted. At the same time

golden ratio is introduced to improve the optimal method of WOA

so as to ocoordinate the global exploration and local exploitation. Twenty unimodal and multimodal benchmark functions are tested and compared with that of other algorithms

and the experimental results show that EGolden-SWOA has a better performance in convergence rate and stability. The high dimensional function test shows that EGolden-SWOA perform well in solving large scale optimization problem. Finally

EGolden-SWOA is applied to the optimization design of the pressure vessel and tension/compression spring

the result shows that its performance in project optimization is better than RCSA and CPSO

it can be effectively applied to project optimization.