An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration

Abstract

AbstractA new formulation based on norm2 method for the multi objective distribution feeder reconfiguration (DFR) is introduced in order to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and to balance the loads on the feeders. In the proposed method, since the objective functions are not the same and commensurable, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. The status of the tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a multi objective and non‐differentiable optimization problem so a hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called HPSO, is proposed to solve it. The feasibility of the HPSO algorithm and the proposed DFR is demonstrated and compared with the solutions obtained by other approaches and evolutionary methods such as genetic algorithm (GA), ACO and the original PSO, over different distribution test systems. Copyright © 2009 John Wiley & Sons, Ltd.