Water level management in the Great Lakes: a study of an integrated optimization algorithm based on Taboo search and genetic control
Year: 2024
Type: article
Abstract: This article presents a single-objective Taboo search optimal water level model designed to satisfy the needs of different stakeholders, including shipping and energy interest groups, environmental and ecological protection groups, and recreational and cultural interest groups. In order to optimize the water level of the lake, the article establishes an optimization model for comprehensive satisfaction and describes in detail the calculation method and constraints of satisfaction. Through the Taboo search algorithm, the optimal water levels of five large lakes in each month were obtained after five repeated experiments. In addition, an enhanced elite conservation control model based on a genetic algorithm is presented to satisfy different stakeholder needs by adjusting the water level of the Great Lakes as well as minimizing the time required for the adjustment. Genetic algorithm parameters include probability, population size, and number of termination generations. The superiority of the algorithm is verified through the sensitivity analysis of the control algorithm and the evaluation of real data. The final results show that after adjusting the water level, the stakeholders are more satisfied with the actual water level.
DOI: 10.62051/hzef2a74
Authors: Zhanghan Li, Shuming Gan, Zhuoyi Lin
Institution: Guangdong University of Technology
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405388927