Linear LSA-NSGAII optimization: A case study in optimal switch placement in distribution network

Abstract

In this paper, a new hybrid of lightning search algorithm (LSA) and non-dominated sorting genetic algorithm II (NSGAII) is proposed in order to increase the accuracy and efficiency of the conventional multi-objective NSGAII. In the proposed method, the important parameters of the NSGAII are optimized using LSA. To verify the effectiveness of the proposed algorithm, it is applied to 19 well-known standard test functions, including unconstrained functions, UF1–UF7, constrained functions, CF1–CF7, Schaffer1, Schaffer2, ZDT1, ZDT2, and ZDT3. The inverted generational distance (IGD) is used to check the efficiency of the proposed algorithm. Accordingly, the proposed algorithm is compared with the most used multi-objective algorithms. Besides, the proposed algorithm is applied to a practical case study of optimal switch, including reclosers and disconnectors, placement in the presence of distributed generation (DG) sources. The distribution networks are the Roy-Billinton test system (RBTS) and a part of the real network in Mazandaran province, in Iran. The simulation results confirm the superiority of the proposed method. For example, in practical case studies, the proposed algorithm for optimal recloser placement in the real network shows 47.09% and 4.691% cost improvement compared with multi-objective particle swarm optimization (MOPSO) and NSGAII, respectively. These improvements for RBTS-BUS2 are 3.725% and 3.502%, respectively. Also, for the RBTS-BUS2, the proposed algorithm result shows 1% and 0.732% reliability improvement compared with MOPSO and NSGAII, respectively.