高斯分布鯨群演算法於最佳化問題之研究

A Study on the Gaussian Distribution Based Whale Optimization Algorithm for Optimization Problems

10.6188/JEB.202304_25(1).0004

李俊賢(Chunshien Li)；王伯倫(Po-Lun Wang)

高斯分布 ； 鯨群最佳化演算法 ； 最佳化演算法 ； 無約束型測試函數 ； 約束型測試函數 ； Gaussian distribution ； whale optimization algorithm ； optimization algorithm ； unconstrained test function ； constrained test function

電子商務學報

25卷1期（2023 / 04 / 30）

89 - 126

繁體中文;英文

近年來機器學習在能力上有顯著提升，為人工智慧系統，例如類神經網路，帶來更佳的效能表現；這意味著計算模型的參數數量大量增加。因此具有搜尋高維度參數解的最佳化演算法之研究，越顯出其重要性。本研究提出之改良式最佳化演算法，稱為高斯分布鯨群最佳化（GD-WOA）演算法，係以兩種策略改良鯨群演算法（WOA），其一是在表現最佳的鯨魚位置建立高斯隨機分布並由此分布產生一個新位置，使之成為鯨群趨近之標的，另一策略是使用隨機擴大搜尋方式。並使用8個無約束型函數與11個約束型函數檢驗GD-WOA搜尋最佳解之優化能力與泛用性。實驗結果顯示GD-WOA具有優異的搜尋能力表現而且具有良好的穩定性，特別是在高維度函數最佳化。

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.

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