GAN-Based Optimization for Deep Neural Network Training
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Solution Overview
Problem
Existing function optimization algorithms lack diversity in local search, making it difficult to find global optimal solutions, especially in complex scenarios like deep network optimization where local extrema are prevalent.
Innovation Solution
A generative adversarial network-based optimization method (GAN-O) is introduced, where a discriminator and generator are trained to evaluate and generate guiding directions for solutions, enhancing the search for global optima by iteratively improving solution quality through adversarial training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gradient-based algorithms are used for function optimization, then computational efficiency is improved, but the algorithm is prone to local extrema and cannot find global optimal solutions
Solution Approach 1:
The patent combines gradient-based local search algorithms with metaheuristic global search algorithms to create a hybrid optimization algorithm. The gradient-based component efficiently refines solutions in local regions, while the metaheuristic component explores the broader solution space to avoid local extrema, achieving both computational efficiency and global optimization capability
Solution Approach 2:
The algorithm dynamically adjusts the balance between exploitation (gradient-based refinement) and exploration (metaheuristic search) during the optimization process. By adaptively controlling the interaction between local and global search mechanisms, the algorithm can transition between intensive local optimization and broader exploration based on the optimization stage and problem characteristics
2Measurement precision
If metaheuristic algorithms are used to avoid local extrema, then global optimal solution capability is improved, but the algorithms lack diversity in local search
Solution Approach 1:
The patent segments the search process into distinct phases: global exploration phase using metaheuristic algorithms to discover promising regions, and local exploitation phase using gradient-based methods to refine solutions. This segmentation allows each component to specialize in its strength while maintaining overall diversity and effectiveness
Solution Approach 2:
The patent introduces an intermediary mechanism that bridges the metaheuristic global search and gradient-based local search. This intermediary facilitates information exchange between the two approaches, allowing the global search to guide local search directions and the local search to provide feedback to the global search, thereby enhancing both global capability and local diversity
Data Source
AI summary
The present invention discloses a generative adversarial network-based optimization (GAN-O) method. The method includes: transforming an application into a function optimization problem; establishing a GAN-based function optimization model based on a test function and a test dimension of the function optimization problem, including constructing a generator G and a discriminator D based on the GAN; training the function optimization model by training the discriminator and the generator alternatively, to obtain a trained function optimization model; and using the trained function optimization model to perform iterative calculation to obtain an optimal solution. In this way, the optimal solution is obtained based on the GAN. The present invention can improve the parameter training process of a deep neural network to obtain a better local optimal solution in a shorter time, making the training of the deep neural network more stable and obtaining better local search results.