Abstract: In order to solve the related problems of multi-objective optimization, this paper proposes an improved multi-objective grasshopper optimization algorithm, by combining the search mechanism of the single-target grasshopper optimization algorithm and the Pareto advantage and crowding strategy, applying the population guidance and Gaussian mutation operator in the algorithm, and adding the reverse learning mechanism. In the experimental verification, the proposed algorithm is compared with the classic MOPSO, MOCS, MOGOA, MOWOA algorithms. The experimental results  show that the improved multi-objective grasshopper optimization algorithm has good robustness, more uniform distribution of the solution, and fast convergence. It is a multi-objective evolutionary algorithm with good application prospects.

Key words: opposition-based learning, grasshopper optimization algorithm, population guidance, Gaussian mutation