Optimizing 2-opt-based heuristics on GPU for solving the single-row facility layout problem

Perancangan Ulang Tata Letak Fasilitas Produksi Menggunakan Modified Squirrel Search Algorithm

“…Dalam beberapa tahun terakhir, banyak penelitian tentang optimasi tata letak telah dilakukan dengan menggunakan algoritma metaheuristik (Prayogo et al, 2018). Algoritma metaheuristik yang diusulkan untuk menyelesaikan tata letak fasilitas diantaranya algoritma Particle Swarm Optimization , Whale Optimization Algorithm (Kaveh & Vazirinia, 2019), Biogeography Based Optimization (Singh & Ingole, 2019), Salp Swarm Algorithm (Elkassas & ElWakil, 2019), Greedy Randomized Adaptive Search Procedure (Cravo & Amaral, 2019), Hybrid Coral Reefs Optimization (Garcia-Hernandez et al, 2020), Graphic Processing Units (Sun et al, 2022), Differential Evolution (Krömer et al, 2020), Evolutionary Algorithm (Senatra & Widyadana, 2022), Simulated Annealing (Kusumaningsih et al, 2022), dan Memetic Algorithm (Wang & Xu, 2023).…”

A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

“…In this article, we propose an improved approach, called "G2BBO", based on the BBO algorithm to further improve the solutions of TSP. In G2BBO, we use the Random Greedy algorithm [18,19] to give the initialized individuals a better solution first, and then use the BBO algorithm to conduct a wide-area search in these favorable spaces, 2-opt [20][21][22] is applied to conduct a regional search after each BBO, which effectively searches for solutions in both wide-area and in between regions. The random greedy algorithm addresses the problem of sensitivity to the initial population, and the 2-Opt algorithm addresses the problem of lack of global convergence.…”