Saving computational budget in Bayesian network-based evolutionary algorithms

Scoczynski, Marcella; Delgado, Myriam Regattieri; Lüders, Ricardo; Oliva, Diego; Wagner, Markus; Sung, Inkyung; Yafrani, Mohamed El

doi:10.1007/s11047-021-09849-z

Cited by 4 publications

(1 citation statement)

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“…Since the codes of the proposed algorithms are not available for comparison to determine the efficiency of the proposed method, the results reported in the literature are used directly, which are the minimum (min), mean and standard deviation (std) values over 20 runs. In addition, to assess that the statistical differences observed among the performance of the algorithms are statistically significant, we use the Friedman's non-parametric test for multiple comparisons (Scoczynski et al, 2021a;Mousavirad et al, 2022) and the Holm's poct-hoc test for 1-to-n comparisons (Aziz et al, 2016).…”

Section: Experimental Set-upmentioning

confidence: 99%

A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

Rodríguez-Esparza¹,

Masegosa²,

Oliva³

et al. 2022

Preprint

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Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASA RL ) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The HH implemented improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.

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Section: Experimental Set-upmentioning

confidence: 99%