Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
An efficient multi-objective HBMO algorithm for distribution feeder reconfiguration
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A novel self-adaptive learning charged system search algorithm for unit commitment problem
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.00 |
This article proposes an efficient hybrid algorithm for multi-objective distribution feeder reconfiguration. The hybrid algorithm is based on the combination of discrete particle swarm optimization (DPSO), ant colony optimization (ACO), and fuzzy multi-objective approach called DPSO-ACO-F. The objective functions are to reduce real power losses, deviation of nodes voltage, the number of switching operations, and the balancing of the loads on the feeders. Since the objectives are not the same, it is not easy to solve the problem by traditional approaches that optimize a single objective. In the proposed algorithm, the objective functions are first modeled with fuzzy sets to calculate their imprecise nature and then the hybrid evolutionary algorithm is applied to determine the optimal solution. The feasibility of the proposed optimization algorithm is demonstrated and compared with the solutions obtained by other approaches over different distribution test systems.