This paper proposes a Kriging assisted strategy to expedite evolutionary computation for solving Optimal Power Flow (OPF) problems. First, two algorithms were developed–a Kriging Assisted Genetic Algorithm (KAGA) and a Kriging Assisted Particle Swarm Optimization (KAPSO) - and tested using unconstrained benchmark functions; it was found that both algorithms provided reliable and robust solutions. Accordingly, KAGA and KAPSO were selected and tested on the IEEE 30 and 118 bus systems for minimizing generation costs and active power losses. It is shown that the proposed KAPSO outperforms other algorithms, especially in terms of the computation time. In reference to the solution quality yielded by KAGA and KAPSO, the proposed Kriging assisted strategy offers a promising method to improve the performance of evolutionary based computation when solving OPF problems.

中文翻译：

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克里金辅助代理进化计算解决最优潮流问题

本文提出了一种克里金辅助策略，以加快求解最优潮流 (OPF) 问题的进化计算。首先，开发了两种算法——克里金辅助遗传算法（KAGA）和克里金辅助粒子群优化（KAPSO）——并使用无约束基准函数进行测试；发现这两种算法都提供了可靠和稳健的解决方案。因此，在 IEEE 30 和 118 总线系统上选择并测试了 KAGA 和 KAPSO，以最大限度地降低发电成本和有功功率损耗。结果表明，所提出的 KAPSO 优于其他算法，尤其是在计算时间方面。参考 KAGA 和 KAPSO 产生的解决方案质量，