An Efficient Hybrid Big Bang–Big Crunch Algorithm for Multi-objective Reconfiguration of Balanced and Unbalanced Distribution Systems in Fuzzy Framework

Abstract

This article presents an efficient hybrid big bang–big crunch optimization algorithm to solve the multi-objective reconfiguration of balanced and unbalanced distribution systems in a fuzzy framework. The objectives considered are the minimization of total real power losses, the minimization of buses voltage deviation, and load balancing in the feeders. First, each objective is fuzzified, and then the overall fuzzy satisfaction function formed is considered as a fitness function and maximized during the optimization process. The hybrid big bang–big crunch algorithm is an effective and powerful method that has high accuracy and fast convergence, and its implementation is easy. This algorithm using particle swarm optimization capacities improves the capability of the big bang–big crunch algorithm for better exploration. In addition, the hybrid big bang–big crunch uses a mutation operator after position updating to avoid local optimum and to explore new search areas. The effectiveness of the proposed algorithm is demonstrated on balanced and unbalanced test distribution systems. The simulation results are compared with the solutions obtained by other approaches.