Analysis of Crossover Techniques in Modification of Grasshopper Optimization Algorithm This article made a description of a novel approach that presents the utility of the various crossover techniques in the Grasshopper algorithm. The Grasshopper optimization algorithm is referred as one of the swam intelligence algorithm in the recent years. This algorithm was already applied in several field of engineering optimization. In order to provide further reinforcement to quality of the results without increasing the complexity of optimization, the crossover technique is applied in this paper. A comparison of several crossover techniques is done with a variety of benchmarking functions in order to provide a comparable platform for different category of optimization. All the technique of crossover are followed by Gaussian mutation for the enhancement of quality of results. The simulation results show the demonstration of the modified versions of the algorithm in the domain of unimodal as well as multimodal category of optimization. The results presented in this paper verified the quality of the outcome presented after suggested modification of the original algorithm. This paper helps the researchers with an elaborate idea about the planned algorithm and can act as base algorithm for several optimization applications.