In this paper, we present the Enhanced Mountain Gazelle Optimizer (EnMGO), a new variant of the mountain gazelle optimizer developed to address the persistent challenge of balancing exploration and exploitation in high-dimensional optimization. While earlier modifications of the algorithm focused on reformulating the control F-parameter to improve stability, they remained limited in adaptability and convergence efficiency. The proposed EnMGO introduces two modifications: An inertia weight based on a chaotic random technique to improve global exploration, and an exponentially decreasing formulation of the F-parameter to enhance local exploitation. These mechanisms create a more adaptive search process capable of efficiently navigating complex solution spaces. The algorithm was evaluated on a set of standard benchmark functions and engineering design problems, and the results demonstrated that EnMGO consistently outperforms previous variants, achieving faster convergence and higher-quality solutions. It achieved better results in approximately 86.96% of the total 23 benchmark functions considered in the study, thereby highlighting its robustness and effectiveness. Furthermore, in the application to engineering design optimization problems, the EnMGO consistently outperformed the comparative algorithms across both design cases, reaffirming its potential as a highly reliable and efficient optimization tool. Based on the performance, the potential of EnMGO can be explored for application in real engineering optimization problems.
Citation: Abdul-Fatawu Seini Yussif, Bright Ayasu, Toufic Seini. Enhanced mountain gazelle optimizer for global performance[J]. Applied Computing and Intelligence, 2025, 5(2): 213-235. doi: 10.3934/aci.2025013
In this paper, we present the Enhanced Mountain Gazelle Optimizer (EnMGO), a new variant of the mountain gazelle optimizer developed to address the persistent challenge of balancing exploration and exploitation in high-dimensional optimization. While earlier modifications of the algorithm focused on reformulating the control F-parameter to improve stability, they remained limited in adaptability and convergence efficiency. The proposed EnMGO introduces two modifications: An inertia weight based on a chaotic random technique to improve global exploration, and an exponentially decreasing formulation of the F-parameter to enhance local exploitation. These mechanisms create a more adaptive search process capable of efficiently navigating complex solution spaces. The algorithm was evaluated on a set of standard benchmark functions and engineering design problems, and the results demonstrated that EnMGO consistently outperforms previous variants, achieving faster convergence and higher-quality solutions. It achieved better results in approximately 86.96% of the total 23 benchmark functions considered in the study, thereby highlighting its robustness and effectiveness. Furthermore, in the application to engineering design optimization problems, the EnMGO consistently outperformed the comparative algorithms across both design cases, reaffirming its potential as a highly reliable and efficient optimization tool. Based on the performance, the potential of EnMGO can be explored for application in real engineering optimization problems.
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Abdul-Fatawu Seini Yussif, Bright Ayasu, Toufic Seini. Enhanced mountain gazelle optimizer for global performance[J]. Applied Computing and Intelligence, 2025, 5(2): 213-235. doi: 10.3934/aci.2025013
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