Adaptation of WO to the Euclidean location-allocation with unknown number of facilities

This study deals with the facility location-allocation problem with Euclidean distances and an unknown number of facilities. The problem is a harder variant of the NP-hard multisource weber problem where the number of facilities is known a priori. A worm optimization (WO) algorithm is developed for the problem, its parameters optimized using a custom design of experiments, and its performance assessed by comparing it to ant colony optimization (ACO) and genetic algorithms (GA). The extensive computational results showed that WO performed better than the other two algorithms in terms of both solution quality and convergence time, with ACO performing second and GA last.