摘要：Non-convex property and huge computation of multi-objective optimal power flow (MOOPF) problems make it unsuitable to be solved by traditional approaches. A many-objective new bat (MONBA) algorithm which improves the speed updating and local searching models is proposed in this paper to handle the MOOPF problems. Moreover, an efficient constraint-priority non-inferior sorting (CPNS) strategy is put forward to seek the satisfactory-distributed Pareto Frontier (PF). Six simulation trials aimed at optimizing the power loss, emission and fuel cost are performed on the IEEE 30-node, 57-node and 118-node systems. In contrast to the classical NSGA-Ⅱ and many-objective basic bat (MOBBA) algorithms, the great edges of presented MONBA-CPNS algorithm in solving the MOOPF problems are powerfully validated. In addition, two performance criteria, which can intuitively measure the distribution and convergence of obtained Pareto optimal set (POS), provide more compelling proof for the superiority of MONBA-CPNS algorithm.
关键词：Many;objective new bat algorithm; Optimal power flow; Non;inferior sorting strategy; Performance criteria
