A Novel Optimization Algorithm Based on Whale Optimization Algorithm with Pareto Principle
Conference: ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application
12/17/2021 - 12/19/2021 at Shenyang, China
Proceedings: ICMLCA 2021
Pages: 5Language: englishTyp: PDF
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Authors:
Cheng, Xiangqun; Gao, Cen (Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China & University of Chinese Academy of Sciences, Beijing, China)
Lu, Ming (Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China & Infrastructure & Cloud Service, SSG, Lenovo, Beijing, China)
Fu, Lijun (Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China & Beijing Zhongke Zhihe Digital Technology Co., Ltd, Beijing, China)
Dong, Zengshou (School of Electronic Information, Engineering, Taiyuan University of Science and Technology, Shanxi, China)
Abstract:
WOA was widely used in Optimization method. In global optimal solution searching, WOA has good performance, and also has special skill to skip out of local optimal solution. Some method aimed at the speed to reach the global optimal solution, some method aimed at the approach to skip out of local optimal solution. Some method aimed at the initialization without random initial method, since prior knowledge could improve the last solution to reach. Pareto Principle is called 80/20 law, we can find good things only in 20%, the other 80% has nothing to do with target. For speed up searching global optimal solution, this paper used Pareto Principle to improve Whale Optimization Algorithm. Based on the original method, at each iteration, every search agent will be calculated with a score. After comparison, the best score of search agents was stand out. Pareto Principle method is used on this best score, 20% area will be search surround the best score for another better score point than the best score point. The length of the searched area is one fifth of the global search area. Also the search agent number is one fifth of global search, so as iteration number. The experimental results show that, this method has better performance, it can reach the global optimal solution faster. Therefore, the global optimal solution could be found with less computing iteration.