A Novel Hybrid Grasshopper Optimization Algorithm for Numerical and Engineering Optimization Problems

When solving practical optimization problems bymetaheuristic algorithms (MAs), an important issue is how to balance exploration and exploitation. This article develops a novel hybrid grasshopper optimization algorithm (HGOA) to solve the above issue. InHGOA, an enhanced grasshopper optimization algorithm with the nonlinear control parameter and a modified butterfly optimization algorithm with the dynamic inertia weight are hybridized based on the probabilistic selection mechanism. Then the centroid opposition-based learning method is utilized to select the best solution between the original individual and its opposite solution. Furthermore, we introduce a self-adaptive pattern search technique as a local search engine to reinforce the exploitation capacity of the algorithm. The comparison of the proposed method on several benchmark problems and five engineering optimization issues with selected state-of-the-art techniques demonstrates that the developed method performs competitively and effectively. The source code of HGOA is publicly available at https:// github. com/denglingyun123/A-novel- hybrid-grasshopper-optimization- algorithm.