dMFEA-II

Osaba, Eneko; Martinez, Aritz D.; Gálvez, Antonio; Iglesias, Andrés; Ser, Javier Del

doi:10.1145/3377929.3398084

Cited by 10 publications

(11 citation statements)

References 34 publications

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“…Several close variants of the MFEA ( Ban & Pham, 2022 ; Osaba et al, 2020 ; Yuan et al, 2016 ) are developed to solve permutation-based optimization problems such as the TSP and TRP. Therefore, they are direct references to our research.…”

Section: Introductionmentioning

confidence: 99%

“…The experiment results indicated the good scalability of evolutionary multitasking in many-task environments. Osaba et al (2020) then proposed a dMFEA-II framework to exploit the complementarities among several tasks, often achieved via genetic information transfer. Their dMFEA-II controls the knowledge transfer by adjusting the crossover probability value.…”

Section: Introductionmentioning

confidence: 99%

“…The technique allows good knowledge to transfer between tasks. Although the results are promising, the drawback of the above two algorithms ( Osaba et al, 2020 ; Yuan et al, 2016 ) is that there is a lack of a mechanism to exploit the good solution space explored by MFEA. Therefore, these algorithms cannot effectively balance exploration and exploitation (we visualize their issue in “Comparison with the previous MFEA algorithms” in more detail).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Ban & Pham (2022) have successfully applied the MFEA with RVNS to solve two problems such as TSP and TRP. Their algorithms maintain the exploration and exploitation better than the others in Osaba et al (2020) and Yuan et al (2016) . Its performance encourages us to use this combination to solve the TSPTW and TRPTW.…”

Section: Introductionmentioning

confidence: 99%

“… The combination between the MFEA and the RVNS is to have good transferrable knowledge between tasks from the MFEA and the ability to exploit good solution spaces from the RVNS. However, focusing only on reducing cost function maybe lead the search to infeasible solution spaces like the algorithms ( Ban & Pham, 2022 ; Osaba et al, 2020 ; Yuan et al, 2016 ). Therefore, the repair method is incorporated into the proposed algorithm to balance finding feasible solution spaces and reducing cost function.…”

Section: Introductionmentioning

confidence: 99%

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Solving optimization problems simultaneously: the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm

Ban¹,

Pham²

2023

PeerJ Computer Science

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We studied two problems called the Traveling Repairman Problem (TRPTW) and Traveling Salesman Problem (TSPTW) with time windows. The TRPTW wants to minimize the sum of travel durations between a depot and customer locations, while the TSPTW aims to minimize the total time to visit all customers. In these two problems, the deliveries are made during a specific time window given by the customers. The difference between the TRPTW and TSPTW is that the TRPTW takes a customer-oriented view, whereas the TSPTW is server-oriented. Existing algorithms have been developed for solving two problems independently in the literature. However, the literature does not have an algorithm that simultaneously solves two problems. Multifactorial Evolutionary Algorithm (MFEA) is a variant of the Evolutionary Algorithm (EA), aiming to solve multiple factorial tasks simultaneously. The main advantage of the approach is to allow transferrable knowledge between tasks. Therefore, it can improve the solution quality for multitasks. This article presents an efficient algorithm that combines the MFEA framework and Randomized Variable Neighborhood Search (RVNS) to solve two problems simultaneously. The proposed algorithm has transferrable knowledge between tasks from the MFEA and the ability to exploit good solution space from RVNS. The proposed algorithm is compared directly to the state-of-the-art MFEA on numerous datasets. Experimental results show the proposed algorithm outperforms the state-of-the-art MFEA in many cases. In addition, it finds several new best-known solutions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%