2021
DOI: 10.1016/j.ejor.2020.10.045
|View full text |Cite
|
Sign up to set email alerts
|

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Abstract: Research on metaheuristics has focused almost exclusively on (novel) algorithmic development and on competitive testing, both of which have been frequently argued to yield very little generalizable knowledge. The main goal of this paper is to promote meta-analysis-a systematic statistical examination that combines the results of several independent studies-as a more suitable way to obtain problem-and implementation-independent insights on metaheuristics. Meta-analysis is widely used in several scienti c domain… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
10
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
5
3
1
1

Relationship

1
9

Authors

Journals

citations
Cited by 49 publications
(10 citation statements)
references
References 104 publications
0
10
0
Order By: Relevance
“…In the tabu search [80], simulated annealing [296], memetic algorithm [301] and an auxiliary algorithm [89], for instance, it serves as the initialization technique. In large neighborhood search [285], it is employed as a recreate operation.…”
Section: Nearest Neighbor Methodsmentioning
confidence: 99%
“…In the tabu search [80], simulated annealing [296], memetic algorithm [301] and an auxiliary algorithm [89], for instance, it serves as the initialization technique. In large neighborhood search [285], it is employed as a recreate operation.…”
Section: Nearest Neighbor Methodsmentioning
confidence: 99%
“…LNS has been successfully applied to many variants of vehicle routing problems [Pisinger and Ropke, 2019] and the literature is abundant. Recently, Turkeš et al [2019] compiled many papers using an LNS heuristic in a meta-analysis that concludes that the adaptive component proposed by Ropke and Pisinger [2006a] has, at best, a small impact.…”
Section: Solution Methodsmentioning
confidence: 99%
“…However, when it is close to the optimal solution, it is not easy to find the optimal solution because of the large search step of the enhance disturbance strategy. In the solution process, if the search step can be changed adaptively, it can not only improve the convergence speed of the algorithm, but also find the most suitable solution more accurately when it is close to the global optimal solution [28,31]. In view of this situation, we design an adaptive strength disturbance strategy, which can adaptively switch between asynchronous and long disturbance operators according to the historical iterative evolution information.…”
Section: Adaptive Strength Perturbation and Random Strength Perturbat...mentioning
confidence: 99%