Enhanced grasshopper optimization algorithm for solving continuous optimization problems
Jianpeng Ye, Min Lin, Yiwen Zhong, Juan Lin · 2023
Aiming at the problems of weak ability to jump out of local optimization and lack of randomness in grasshopper optimization algorithms, this paper proposes a multi-strategy Enhanced Grasshopper Optimization Algorithm (EGOA) based on Brown random walk. The EGOA incorporates multiple strategies, including a Brownian random walk, a nonlinearly decreasing strategy with perturbation, and an improved optimal value guidance step. These innovations enhance the algorithm's ability to escape local optima, increase randomness, balance exploration and exploitation, and expand individual search ranges. Experimental results demonstrate that EGOA surpasses other swarm intelligence algorithms in benchmark function tests.