Pastoralist Optimization Algorithm (POA): A Culture-Inspired Metaheuristic for Uncapacitated Facility Location Problem (UFLP)

Abstract

In this paper, the performance of the recently developed Pastoralist Optimization Algorithm (POA) on classical uncapacitated Facility Location problem (UFLP) was investigated. POA is a culture-inspired metaheuristic motivated by the herding schemes of Nomadic Pastoralist (NP). The NP seek optimal herding location for their livestock using some well-defined and robust strategies. UFLP is an NP-hard problem from which many facility location and real-world problems are built around. In this paper, five UFLP datasets were used for the experiments each comprising of five cities and seven, fifteen, thirty, fifty and one hundred cities respectively. The performance of POA was compared and validated with some popular and similar metaheuristic algorithms such as ABC, BBO and PSO. The results obtained proves POA competiveness and superiority in obtaining the lowest allocation cost and convergence rate as the data size increases.