Grasshopper Optimization Algorithm Based on Adaptive Curve and Reverse Learning

The disadvantage of the grasshopper optimization algorithm (GOA) is its insufficient ability in global exploration, relatively slow convergence speed, and easy to obtain the local optimal solution. Aiming at the poor convergence accuracy of GOA algorithm, a new grasshopper optimization algorithm(OLCZGOA) based on adaptive fusion curve and reverse learning was proposed. Firstly, an improved curve adaptive formula is introduced to replace the linear adaptive formula of parameter C in the grasshopper optimization algorithm to improve the convergence speed of the algorithm. Secondly, considering that grasshopper optimization algorithm is easy to obtain local optimal solutions, three selection strategies are introduced to reverse learning, which makes grasshopper optimization algorithm have stronger global optimization ability. In this paper, nine test functions are selected to test the proposed improved algorithm. The results show the effectiveness of the proposed improved strategy, and the OLCZGOA algorithm has better solution accuracy compared with other comparison algorithms.