A new Levy Flight Trajectory-Based Grasshopper optimization Algorithm for Multi-objective optimization Problems

This work proposes a new improved version of the nature-inspired multi-objective grasshopper optimization algorithm (MOGOA), based on a Levy flight method and called the Levy flight trajectory-based multi-objective grasshopper optimization algorithm (LMOGOA). It is worth mentioning that, the Levy flight trajectory is applied for the first time to enhance MOGOA algorithm by increasing the diversity of solution, avoiding premature convergence and local optima stagnation. The main advantage of LMOGOA is fast convergence speed to the true Pareto-optimal front while maintaining good diversity of solutions. To benchmark the performance of the proposed algorithm, a set of diverse standard multi-objective test problems is utilized. Results show that the proposed LMOGOA significantly outperforms the standard MOGOA algorithm.