文献类型：期刊文献

英文题名：A Multi-Strategy Gravitational Search Algorithm for Solving the Optimal Power Flow Problem

作者：Liang, Ziyuan[1]; Wang, Zhenlei[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

年份：2025

起止页码：1883

外文期刊名：Chinese Control Conference, CCC

收录：EI(收录号：20254419432796)

语种：英文

外文关键词：Computation theory - Evolutionary algorithms - Optimization - Problem solving

摘要：In this paper, a multi-strategy gravitational search algorithm is proposed to solve the optimal power flow (OPF) problem, named MGSA. The MGSA integrates three novel strategies: an adaptive co-evolutionary selection strategy to improve solution diversity and prevent premature convergence; a phased gravitational constant decay strategy to effectively balance exploration and exploitation; and a dynamic escape strategy to evade local optima, ensuring continuous progress toward optimal solutions. Comprehensive experiments on the IEEE 30-bus system compare MGSA against the standard GSA and some advanced optimization algorithms. The results show that MGSA outperforms others in reducing fuel costs, managing the valvepoint effect, minimizing real active power losses, and enhancing voltage stability. These findings demonstrate the effectiveness of MGSA in solving the OPF problem, establishing it as a competitive tool for tackling complex optimization challenges within power systems. ? 2025 Technical Committee on Control Theory, Chinese Association of Automation.