In order to solve the problems of insufficient exploratory ability and easy to fall into local optimum of Crayfish Optimization Algorithm (COA), the study proposes a Hybrid Strategy Optimization-Based Shrimp Optimization Algorithm, HCOA. Firstly, the crayfish population is in itialised by Latin hyper cubic sampling to improve the uniformity of the population; secondly, a lens imaging reverse learning strategy is introduced to generate a new population and enhance the population diversity; finally, a differential evolutionary variation strategy and an elite retention mechanism are introduced to avoid the algorithm from falling into a local optimum, and to enhance the local search capability; the verification is carried out using a part of the standard function test set at the experimental analysis stage and the validation is carried out on four engineering design problems to verify the effectiveness. The experimental results show that the HCOA algorithm achieves good results.