英文摘要 |
In recent years, machine learning has significantly improved in terms of capabilities, resulting in better performance for artificial intelligence systems, such as neural networks, which means that the number of parameters in the model has increased significantly. This means that the number of parameters in the models raises steeply, so the study of the optimization algorithm for optimizing high-dimensional parameters becomes more important. The improved optimization algorithm proposed in this study, called“Gaussian Distribution based Whale Optimization Algorithm (GD-WOA)”, which improves the Whale Optimization Algorithm (WOA) by two main strategies. One of improving strategy is to establish a Gaussian random distribution at the position of the best whale during the searching process, and to generate a new position, thus making it as a new position that whales try to approach. Another strategy is to use a randomized approach to expand search. In this research, we use 8 unconstrained functions and 11 constrained functions to test the optimization ability and generality of GD-WOA when searching optimal solution. The results show that GD-WOA has excellent search performance and good stability, especially in the optimization of high-dimensional functions. |