Optimal power flow (OPF) is one of the complex problems in power system operation that includes multi-modal, large-scale, non-convex and non-linear constrained optimization problems. Due to these features, solving the OPF problem is becoming an active topic to be solved by power engineers and researchers. In this paper, recent metaheuristic algorithms namely Grasshopper Optimization Algorithm (GOA), Black Widow Optimization Algorithm, Grey Wolves Optimizer, Ant Lion Optimizer, Particles Swarm Optimization, Gravitational Search Algorithm, Moth-Flame Optimization and Barnacles Mating Optimizer (BMO) will be used to solve three objective functions of OPF problem viz. (1) cost minimization of the power generation that consists of thermal, stochastic wind and solar power generations, (2) power loss minimization, and (3) combined cost and emission minimization of power generations. To assess the performance of these selected metaheuristic algorithms on OPF, a modified IEEE 30-bus system that incorporate the stochastic wind and solar power generators will be used. Statistical studies are performed to identify the effectiveness of algorithms under consideration. Test results suggest that BMO performs better compared to the rest of algorithms and demonstrate that it can be effective alternative for the OPF problem solution.

最优潮流（OPF）是电力系统运行中的复杂问题之一，其中包括多模式，大规模，非凸和非线性约束优化问题。由于这些特性，解决OPF问题正成为电源工程师和研究人员要解决的一个活跃主题。在本文中，将使用最近的元启发式算法，例如蚱hopper优化算法（GOA），黑寡妇优化算法，灰狼优化器，蚂蚁狮子优化器，粒子群优化，引力搜索算法，飞蛾优化和藤壶交配优化器（BMO）。解决OPF问题的三个目标功能。（1）最小化由热，随机风力和太阳能发电组成的发电成本，（2）最小化功率损耗，（3）使发电成本和排放最小化。为了评估这些选定的元启发式算法在OPF上的性能，将使用结合了随机风力发电机和太阳能发电机的改良IEEE 30总线系统。进行统计研究以确定正在考虑的算法的有效性。测试结果表明，与其他算法相比，BMO的性能更好，并证明它可以作为OPF问题解决方案的有效替代方案。