A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights

The shortest path (SP) problem constitutes one of the most prominent topics in graph theory and has practical applications many research areas such as transportation, network communications, emergency services, fire stations to name just a few. In real-world applications, arc weights corresponding SP problems are represented by fuzzy numbers. current paper presents fuzzy-based Ant Colony Optimization (ACO) algorithm for solving with different types weights. paths involving kinds arcs approximated using α-cut method. addition, signed distance function is used compare paths. proposed implemented on three increasingly complex numerical examples results obtained compared those derived from genetic (GA), particle swarm optimization (PSO) an artificial bee colony (ABC) algorithm. confirm that enhanced ACO could converge about 50% less time than alternative metaheuristic algorithms.