In this article, an evolutionary approach with different objectives based on the improvement of the hybrid GSA (golden section search algorithm) using the flower pollination algorithm (FPA) with fitness distance balance selection (FDS) is proposed. In GSFPAFDS algorithm, the optimization problem is improved in several sub-problems. The pollination of flowers in the population is enhanced in Levy flight with fitness distance to enhance convergence. Therefore, the GSA can be used as a local search in FPA. This simulation is part of the traditional GSA, FPA, GSFPA, and personnel processing to solve the consequences of sequential global optimization problems. A local update strategy is used to increase optimization capacity. Its main objectives are to minimize the overall execution time and the allocation of the balance between local and global optimal zone. It allows global parallelization with the simultaneous performance of the processor to the functions, and determine the ratio of the communication to the functions. The behavior of the GSFPAFDS with few methods such as GSA, FPA, and GSFPA understandability has been showcased to solve high-dimensional technical problems.