Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance

Abstract

Because of their efficacy in solving complicated optimization issues, metaheuristic approaches are finding more and more applications in engineering and research. When it comes to optimizing multimodal functions with actual values, Price [1] highlighted the effectiveness of diferential evolution (DE), an evolutionary technique that is both simple and powerful. Results from 11 out of 15 test functions demonstrate that DE achieves faster solutions than several modern optimization approaches. Holland also went above and beyond the limitations of traditional frameworks by developing a mathematical model that serves as a basis [2]. From economic theories to the creation of advanced technological gadgets, this paradigm has made it easier to apply genetic algorithms (GAs) to a wide range of complicated adaptive systems. In their study, Kennedy and Eberhart introduced a method called particle swarm optimization (PSO) for optimizing nonlinear functions. They covered the method’s evolution, how it is implemented, potential uses in neural network training and non-linear optimization, and how it relates to artificial life and GA. Ant colony optimization (ACO) was the subject of the groundbreaking work of Dorigo et al. [4], who established the notion of swarm intelligence. Since its debut, this method—which draws inspiration from ant foraging behavior—has attracted a lot of attention from scholars and practitioners. Since its beginnings, ACO has spawned a growing body of theoretical insights and a plethora of successful implementations. Mechanical design optimization is a challenging task, but Rao et al. [5] introduced teaching-learning-based optimization (TLBO) as a viable solution. TLBO’s efficacy was confirmed by extensive testing, where it underwent.