Multi-surrogate assisted differential evolution for edge-based facility location problem

• Main Authors: Sulaman, Muhammad, Golabi, Mahmoud, Essaid, Mokhtar, Brevilliers, Mathieu, Lepagnot, Julien, Idoumghar, Lhassane
• Format: Conference Proceeding
• Language: English
• Subjects: Benchmark testing; Boosting; Computational modeling; continuous demands; differential evolution; extreme gradient boosting; extreme learning machine; Extreme learning machines; Facility location problem; Forestry; Information technology; random forest; Random forests; Stability analysis; surrogate model; Training
• ISSN: 2576-3555
• DOI: 10.1109/CoDIT62066.2024.10708552

This paper addresses the computationally challenging edge-based facility location problem with the objective of minimizing total travel time while accommodating uniformly distributed demand on network edges. To enhance computational efficiency, the proposed method integrates differential evolution (DE) with three distinct surrogate models: random forest, extreme learning machines, and extreme gradient boosting. While the concept of distributed demand on network edges presents a more realistic depiction of location problems, the necessity of decomposing edges and assigning them to their nearest facilities increases the complexity of the problem at hand. Therefore, the development of an effective and efficient solution method is crucial, particularly in time-sensitive contexts where rapid decisions are essential. Empirical evaluations demonstrate the efficacy and efficiency of the proposed multi-surrogate approach when compared to traditional DE and a leading surrogate-based algorithm. The results illustrate superior computational performance while preserving solution quality across various benchmark functions.