PERFORMANCE ANALYSIS OF CNN-ENHANCED GENETIC ALGORITHM FOR TOPOLOGICAL OPTIMIZATION IN METAMATERIAL DESIGN

The Combination of Convolutional Neural Networks (CNN) and Genetic Algorithms (GA) provides a promising approach for topological optimization of complex lattice structures. Lattice structures are commonly used as base in the design of high-performance metamaterials. This paper presents a review of the effectiveness and efficiency of the CNN-GA method. We will examine the ability of the method to generate optimal complex structures while minimizing material usage. CNN is utilized mainly as an analysis instrument. That can evaluate and predict key structural properties of generated lattice structures. The key purpose of the GA algorithm is to provide diverse design configurations that will be later identified as optimal structures by CNN. Key performance metrics include load-bearing capacity, strength-to-weight ratio, computational time, and scalability. These key points can be utilized as tools that will evaluate the method`s performance for a real-world application. The CNN-GA method can produce highly efficient, lightweight structures with high performance and material economy compared to traditional optimization techniques. Moreover, genetic algorithm random exploration techniques can reveal unique lattice configurations and provide an option that might be overlooked by a standard deterministic method. However, the method's effectiveness is partially constrained by its operations, which may consume a lot of computational resources and time for a significant result. Additionally, the accuracy of this method's prediction system is compromised by the inherent nature of the GA generation process. This analysis highlights the method`s strengths, potential limitations, and practical implications and provides a foundation for future research aimed at refining machine learning-based topological optimization methods.