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In a deregulated electricity market transmission congestion occurs when there is insufficient transmission capacity to simultaneously accommodate all requests for transmission service within a region. One of the most important tasks of independent system operator (ISO) is to manage congestion as it threatens system security and may cause rise in electricity price resulting in market inefficiency. In corrective action of congestion management schemes, it is crucial for ISO to select the most sensitive generators to reschedule their optimal real and reactive powers in congestion management. As the real and reactive power dispatches play a vital role to relieve the congestion at low congestion cost, in this paper, the reactive support of generators, in addition to the rescheduling of real power generation, has been considered to manage congestion. The re-dispatch of transactions for congestion management in a pool model is formulated as a non-linear programming (NLP). The fitness distance ratio particle swarm optimization (FDRPSO) based optimal power flow (OPF) is introduced for congestion management problem first time in this paper to solve the NLP. This paper has utilized the method of selection of generators from the most sensitive cluster/zone to re-dispatch the real and reactive powers simultaneously using two distribution factors, viz. real and reactive power transmission congestion distribution factors (PTCDFs and QTCDFs). The proposed method has been tested on a practical 75-bus Indian System for single and multi line congestion cases. The results are compared with the conventional particle swarm optimization (CPSO), real coded genetic algorithm (RCGA) and binary coded genetic algorithm (BCGA) based OPFs.