Optimization Algorithms for Designing Complex Graphs Using Generative Adversarial Networks (GANs)

This paper explores the use of Generative Adversarial Networks (GANs) for optimizing the design of complex graphs. The design of complex graphs, particularly in systems with a large number of nodes and edges, presents challenges such as time-consuming computations and the need for high accuracy. This paper proposes the use of GANs as an innovative approach to address these issues. GANs, with their ability to model accurately and generate graphs with characteristics similar to real data, are capable of reducing computational time and optimizing graphs at larger scales. This method has significant applications in molecular graphs, social networks, and transportation systems. Furthermore, the paper compares GANs with traditional algorithms, such as Genetic Algorithms and Simulated Annealing, demonstrating that GANs can effectively handle various graph optimization problems.Experimental results indicate that the proposed GAN-based framework achieves superior performance in terms of optimization efficiency and solution quality. Additionally, the model demonstrates strong generalization capabilities across different graph structures and sizes. These findings suggest that GANs offer a scalable and robust alternative for complex graph design and optimization tasks.This paper explores the use of Generative Adversarial Networks (GANs) for optimizing the design of complex graphs. The design of complex graphs, particularly in systems with a large number of nodes and edges, presents challenges such as time-consuming computations and the need for high accuracy. This paper proposes the use of GANs as an innovative approach to address these issues. GANs, with their ability to model accurately and generate graphs with characteristics similar to real data, are capable of reducing computational time and optimizing graphs at larger scales. This method has significant applications in molecular graphs, social networks, and transportation systems. Furthermore, the paper compares GANs with traditional algorithms, such as Genetic Algorithms and Simulated Annealing, demonstrating that GANs can effectively handle various graph optimization problems.Experimental results indicate that the proposed GAN-based framework achieves superior performance in terms of optimization efficiency and solution quality. Additionally, the model demonstrates strong generalization capabilities across different graph structures and sizes. These findings suggest that GANs offer a scalable and robust alternative for complex graph design and optimization tasks.

کلیدواژه ها:

Generative Adversarial Networks ، Graph Design ، Optimization ، Complex Graphs ، Genetic Algorithms ، Simulated Annealing ، Simulated ، Graph Analysis