| dc.description.abstract | 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. In the other words, the depth of the hierarchical layer of the neural networks model increases and the number of neurons in each layer increases, then the difficulty of tuning model is coming with that the model parameters raise steeply and become more difficult to find the optimal solution in the machine learning process. Therefore, the research on the optimization algorithm that can deal with optimizing high-dimensional parameters becomes more important. This study proposed an improved algorithm called “Gaussian Distribution based Whale Optimization Algorithm (GD-WOA).” Although the original whale optimization algorithm (WOA) has a good optimization ability and it has simple optimization strategy, but we found through experiments that the optimization ability gradually becomes insufficient as the parameter dimension increases. In addition, WOA has shortcomings in the ability to handle local optimal solutions and the versatility of optimization problems. In light of this, the GD-WOA improves WOA with two strategies. One 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. Especially when the search process encounters a local optimal solution, it can mitigate the risk of optimization stagnation through this strategy. In this study, we use 38 unconstrained functions and 30 constrained functions to test the optimization ability and generality of GD-WOA when searching optimal solution. Most of these functions have designs that can be adjusted in a variety of different dimensions, from 50 dimensions to 10,000 dimensions; a small number of functions are fixed dimensions ranging from 2 dimensions to 13 dimensions. The results after experiments show that the GD-WOA proposed in this study has excellent search performance and good stability, especially in the optimization of high dimensional functions. The results of the experiment are compared with the performance of several well-known optimization methods in the literature, showing that the GD-WOA algorithm proposed in this study has excellent performance. | en_US |