This study presents a particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) for the solution of optimal reactive power dispatch (ORPD) problem. The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss and the total voltage deviations are to be minimized separately. In order to evaluate the performance of the proposed algorithm, it has been implemented on IEEE 30-, 57- and 118-bus test power systems and the optimal results obtained are compared with those of the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving the ORPD problem of power system. transfer susceptance between buses i and j positive coefficients that determine the influence of the personal and neighborhood best positions, respectively conductance of branch k transfer conductance between buses i and j number of iteration for which the leading power of challenger is tested objective function to be minimized number of total buses number of possible reactive power source installation buses number of network branches number of generator buses number of buses adjacent to bus i (including bus i) number of load buses number of total buses excluding slack bus number of particles in a population number of PQ buses number of transformer branches maximum NFFEs number of fitness function evaluations or iteration cycles demanded active power at bus i injected active power at bus i shunt capacitor/inductor demanded reactive power at bus i generator reactive power injected reactive power at bus i random numbers uniformly distributed in [0,1] power flow in branch l tth iteration transformer tap vector of control variables generator voltage (continuous) voltage at bus i reference value of the voltage magnitude of the ith bus which is equal to 1.0 p.u. ith particle's velocity vector load bus voltage inertia weight maximum value of w minimum value of w vector of dependent variables historically best position of the entire swarm historically best position of particle i (i =1, 2, …, N P ) ith particle's position vector voltage angle difference between buses i and j 
Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers