Bingso: Galactic Swarm Optimization Powered by Binary Artificial Algae Algorithm for Solving Uncapacitated Facility Location Problems

Abstract

Population-based optimization methods are frequently used in solving real-world problems because they can solve complex problems in a reasonable time and at an acceptable level of accuracy. Many optimization methods in the literature are either directly used or their binary versions are adapted to solve binary optimization problems. One of the biggest challenges faced by both binary and continuous optimization methods is the balance of exploration and exploitation. This balance should be well established to reach the optimum solution. At this point, the galactic swarm optimization (GSO) framework, which uses traditional optimization methods, stands out. In this study, the binary galactic swarm optimization (BinGSO) approach using binary artificial algae algorithm as the main search algorithm in GSO is proposed. The performance of the proposed binary approach has been performed on uncapacitated facility location problems (UFLPs), which is a complex problem due to its NP-hard structure. The parameter analysis of the BinGSO method was performed using the 15 Cap problems. Then, the BinGSO method was compared with both traditional binary optimization methods and the state-of-the-art methods which are used on Cap problems. Finally, the performance of the BinGSO method on the M* problems was examined. The results of the proposed approach on the M* problem set were compared with the results of the state-of-the-art methods. The results of the evaluation process showed that the BinGSO method is more successful than other methods through its ability to establish the balance between exploration and exploitation in UFLPs.