PSO Based Constraint Optimization of Intuitionistic Fuzzy Shortest Path Problem in an Undirected Network

Abstract

Shortest Path Problem (SPP) is mainly used in network optimization; also, it has a wide range of applications such as routing, scheduling, communication and transportation. The main objective of this work is to find the shortest path between two specified nodes by satisfying certain constraints. This modified version of SP is called Constraint Shortest Path (CSP), which establishes a certain limit on selected constraints for the path. The limit for constraint values is precisely specified in traditional CSP problems. But, the precise data may vary due to environmental conditions, traffic and payload. To resolve this, the proposed CSP uses intuitionistic fuzzy numbers to deal with imprecise data. Also, finding an optimal solution in the complex search space of an undirected network is difficult. Hence, Particle Swarm Optimization (PSO) is used in the proposed work to obtain the optimal global solution within feasible regions. A numerical example and the implementation of the proposed work in Matlab 2016a working environment are also illustrated. The simulation analysis shows that the proposed PSO algorithm takes 1.8[Formula: see text]s to find the CSP in a specified undirected network graph, which is comparatively lower than the existing Genetic Algorithm (2.4[Formula: see text]s) and without optimization (5.6[Formula: see text]s).