Extreme Learning Machine-based Genetic Algorithm for the facility location problem with distributed demands on network edges

This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assign...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2023 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 8
Hlavní autoři: Golabi, Mahmoud, Essaid, Mokhtar, Sulaman, Muhammad, Idoumghar, Lhassane
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2023
Témata:
Aggregates
Approximation algorithms
Computational modeling
continuous demands
Evolutionary computation
extreme learning machine
Extreme learning machines
Facility location problem
genetic algorithm
Metaheuristics
Optimization methods
surrogate model
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assigned to its closest open facility. Finding the best combination for establishing facilities and using them as a basis for decomposing network edges form the main decision variables. Due to the NP-hardness of this problem, a Genetic Algorithm is used as the optimization method. This algorithm is known as one of the best metaheuristics for solving this problem. To accelerate the optimization process considering the computationally expensive fitness evaluation of the edge-based location problems, an extreme learning machine is hybridized with the implemented genetic algorithm to serve as a surrogate model for approximating the fitness of the majority of individuals. The results obtained from solving generated instances indicate that while keeping the same quality of solutions, the developed surrogate model-based genetic algorithm significantly reduces the required computational time.
DOI:10.1109/CEC53210.2023.10254196