A generality analysis of multiobjective hyper-heuristics
Information Sciences, ISSN: 0020-0255, Vol: 627, Page: 34-51
2023
- 7Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Selection hyper-heuristics have emerged as high level general-purpose search methodologies that mix and control a set of low-level (meta) heuristics. Previous empirical studies over a range of single objective optimisation problems have shown that the number and type of low-level (meta) heuristics used are influential to the performance of selection hyper-heuristics. In addition, move acceptance strategies play an important role and can significantly affect the overall performance of a hyper-heuristic. In this paper, we introduce an adapted variant of an existing learning automata based multiobjective hyper-heuristic from the literature. We investigate the performance and generality level of the proposed method, and another learning automata based selection hyper-heuristic, operating over a search space of multiobjective evolutionary algorithms (MOEAs) across two well-known multiobjective optimisation benchmarks. The experimental results demonstrate that, regardless of the number and type of low-level metaheuristics available, the learning automata based hyper-heuristics outperform each constituent MOEA individually, and an online learning and random choice selection hyper-heuristic from the literature. This performance and generality is shown to be consistent across a number of different move acceptance strategies.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0020025523000476; http://dx.doi.org/10.1016/j.ins.2023.01.047; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85146674492&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0020025523000476; https://dx.doi.org/10.1016/j.ins.2023.01.047
Elsevier BV
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