A New Binary Grasshopper Optimization Algorithm for Solving Uncapacitated Facility Location Problem

The uncapacitated facility location problem (UFLP) is a real-world binary optimization problem that aims to find the number of opened facilities that minimizes the total cost of exchange between customer and facilities and opening cost of these facilities. UFLP is in class of NP-Hard and, there are exact methods used in solving UFLP. However, metaheuristic methods are often preferred to solve UFLP because of its capability to solve problems in reasonable time with acceptable results. Most of the metaheuristic methods in the literature, especially population-based optimization algorithms, have been proposed for continuous optimization problems. To apply these methods on discrete optimization problems, these should be modified on working binary-structured solution space. In recent years many nature-inspired optimization algorithms developed and one of them is Grasshopper Optimization Algorithm (GOA). Despite the fact that some binary versions of the GOA have been developed in the literature to solve various problems such as feature selection, knapsack and set covering, none of them is applied to solve UFLP. In this study, a new binarization procedure has been applied to GOA for solving the UFLPs, effectively. The performance of the proposed algorithm is investigated on Cap and M* series datasets and the obtained results are compared with the other state of the art binary optimization algorithms. It is seen from the experimental results that the proposed method is superior to other compared algorithms and, hence it can be used as an effective alternative tool for solving UFL problems