Fuzzy-based modified particle swarm optimization algorithm for shortest path problems

Plenty of problems are related to the calculation of edges and nodes in the realistic networks. It also influences the realization of shortest path problem (SPP) because of its essential fuzziness. This paper presents a fuzzy-based modified particle swarm optimization (fuzzy-based MPSO) algorithm for resolving the shortest path issue. The proposed work also evaluates the uncertainties of this shortest path problem through the utilization of offered algorithm. Actually, the normal PSO algorithm is altered and estimated to tackle the fuzzy-based SPP (FSPP) with uncertain edges. The performance of the planned algorithm will be improved; also the results are compared with the existing methodologies. The early convergence of the PSO technique can be alleviated and travelled via the dynamic operation of fuzzy method. And the proposed method is compared with other metaheuristic algorithms such as evolutionary random weight networks (GA-RWNs), grasshopper optimization algorithm with evolutionary population dynamics (GOA-EPD), levy weight grey wolf optimization (LGWO) and PSO in terms of cost and time consumption. The related results and discussion is performed in the working platform of MATLAB tool for the demonstration of the proposed work to manage the FSPP in indeterminate networks.