Paper
7 December 2023 Enhanced grasshopper optimization algorithm for solving continuous optimization problems
Jianpeng Ye, Min Lin, Yiwen Zhong, Juan Lin
Author Affiliations +
Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294117 (2023) https://doi.org/10.1117/12.3011794
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China
Abstract
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.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jianpeng Ye, Min Lin, Yiwen Zhong, and Juan Lin "Enhanced grasshopper optimization algorithm for solving continuous optimization problems", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294117 (7 December 2023); https://doi.org/10.1117/12.3011794
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KEYWORDS
Mathematical optimization

Design and modelling

Algorithm testing

Data modeling

Nonlinear optimization

Stochastic processes

Evolutionary optimization

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