The power utility industry is virtually one of the major industries of every nation. Because each power network is so widely geographically distributed, the administration of a power system is faced with a number of operational challenges that are often hard to tackle, and computational approach has shown a way to optimize some of these problems, as shown by the increased attention that the research community has paid for it and by the number of studies that have been recently published. Given that a number of nonlinear mathematical functions need to be handled for the optimization of power system operational problems, we discuss here a novel algorithm based on an adaptive quasi-oppositional differential migrated biogeography-based optimization, with the aim to identify the optimal control variables for different objectives of optimal power flow problems, and it is expected that our work could motivate further exploration of this optimization algorithm in this field by the peers. In our work, we attempted to modify the mutation operator of a Differential Evolution algorithm with migration operator of a biogeography-based optimization (BBO) algorithm so as to improve the exploration ability of the resulting model. Furthermore, a quasi-oppositional based learning technique was evoked to increase the adaptability of the mutation operator of BBO, thereby enhancing its exploitation ability. Finally, the accuracy and robustness of the proposed algorithm were tested by applying on IEEE 30-bus and IEEE 118-bus systems and also results were compared with the results reported in the recent literature.

电力行业实际上是每个国家的主要产业之一。由于每个电力网络的地域分布如此广泛，因此电力系统的管理面临许多通常难以解决的运营挑战，并且计算方法已显示出一种优化其中一些问题的方法，研究界已经为此付出了代价，并注意到最近发表的研究数量。鉴于需要处理许多非线性数学函数以优化电力系统的运行问题，因此我们在此讨论一种基于自适应准对立差分迁移生物地理优化的新算法，目的是确定针对最优潮流问题的不同目标的最优控制变量，并期望我们的工作能够激发同行们对该领域的优化算法的进一步探索。在我们的工作中，我们尝试使用基于生物地理学的优化（BBO）算法的迁移算子来修改差异进化算法的变异算子，以提高结果模型的探索能力。此外，还提出了一种基于准对立的学习技术，以提高BBO变异算子的适应性，从而提高其开发能力。最后，通过在IEEE 30总线和IEEE 118总线系统上的应用对所提算法的准确性和鲁棒性进行了测试，并将结果与​​最新文献中的结果进行了比较。