Solving Optimal Power Flow Problems Using Adaptive Quasi-Oppositional Differential Migrated Biogeography-Based Optimization

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.