Optimizing Constrained Engineering Optimization Problems Using Improved Mountain Gazelle Optimizer

This study introduces a novel approach to engineering design optimization through the development of an improved mountain gazelle optimizer (iMGO) that incorporates variable neighborhood search (VNS) techniques. The enhanced algorithm effectively addresses engineering optimization challenges by identifying optimal design solutions within specified constraints. In particular, iMGO significantly improves solution diversity and mitigates the risk of premature convergence to local optima, thereby overcoming the limitations of the original MGO. A comprehensive analysis was conducted using 12 functions from the CEC 2022 benchmark suite, and the algorithm was applied to five engineering problems, including the design of an I-beam, pressure vessel, three-bar truss, cantilever beam, and tension spring. Comparative results indicate that iMGO outperforms established metaheuristic techniques, such as MFO, WOA, GOA, MPA, TSO, and SCSO, as well as the original MGO. The results validate iMGO's effectiveness in navigating the complexities of constrained engineering optimization. For instance, in practical applications, the manufacturing cost of the pressure vessel design was reduced from 6014.4537 to 5915.3358, and the weight of the tension spring was decreased from 0.0149154 to 0.0130101 relative to the original MGO. These enhancements underscore the significant potential of iMGO in real-world applications across aerospace engineering, structural design optimization, energy system planning, and other fields, thereby contributing to more efficient and sustainable engineering solutions.