基于自适应海鸥优化算法的电力系统优化调度问题

Optimization Scheduling of Power Systems Based on Adaptive Seagull Optimization Algorithm

摘要: 电力系统优化调度是保障电力系统安全、经济、稳定运行的核心环节，其本质是一类多约束、非线性、多极值的复杂组合优化问题。传统优化算法易出现收敛速度慢、早熟收敛、寻优精度不足等缺陷，难以满足调度需求。针对上述问题，文章提出了基于自适应海鸥优化算法(Adaptive Seagull Optimization Algorithm, ASOA)，用于求解电力系统优化调度问题。首先，在原始海鸥优化算法(Seagull Optimization Algorithm, SOA)的基础上，引入自适应权重因子，通过非线性动态调节搜索步长，平衡算法的全局探索与局部开发能力；其次，构造融合种群平均位置与全局最优个体的迁移算子，替代原始单一最优引导机制，维持种群多样性，有效抑制早熟收敛。将改进算法应用于求解电力系统优化调度问题，验证了该算法的有效性。

Abstract: The optimization scheduling of power systems is a core aspect of ensuring the safe, economical, and stable operation of power systems. It essentially represents a complex combinatorial optimization problem characterized by multiple constraints, nonlinearity, and multiple extrema. Traditional optimization algorithms often suffer from drawbacks such as slow convergence speed, premature convergence, and insufficient optimization accuracy, making it challenging to meet scheduling requirements. To address these issues, this paper proposes an Adaptive Seagull Optimization Algorithm (ASOA) for solving the optimization scheduling problem in power systems. First, an adaptive weight factor is introduced based on the original Seagull Optimization Algorithm (SOA) to dynamically adjust the search step size nonlinearly, thereby balancing the global exploration and local exploitation capabilities of the algorithm. Second, a migration operator that integrates the average position of the population with the global optimal individual is constructed to replace the original single optimal guidance mechanism, maintaining population diversity and effectively suppressing premature convergence. The improved algorithm is applied to solve the optimization scheduling problem in power systems, demonstrating its effectiveness.