A Hybrid Moth Flame Optimization and Golden Jackal Optimization Algorithm Based Opposition for Global Optimization Problems

Abstract

The Golden Jackal Optimization (GJO) algorithm has found applications in various fields such as feature selection, image segmentation, mechanical engineering, and Internet of Things intrusion detection. However, the original GJO algorithm suffers from several limitations, including premature convergence, slow convergence speed, and low calculation accuracy. To address these shortcomings, we propose a hybrid Moth Flame Optimization (MFO) and GJO algorithm based on opposition learning (OMGJO), which combines opposition learning and spiral path search technology of MFO to enhance the algorithm’s performance and convergence speed. To evaluate the effectiveness of the proposed algorithm, we compare it with 10 other meta-heuristic algorithms using 30 benchmark functions, and perform statistical tests on the results. The experimental outcomes demonstrate that the OMGJO algorithm offers highly competitive results. Compared to the traditional GJO algorithm and other optimization methods, the improved algorithm exhibits superior performance in terms of convergence speed and search efficiency. Additionally, when tested on various engineering problems, the algorithm demonstrates its competitiveness and adaptability in practical applications.