A Biased Random Key Genetic Algorithm for Solving the α-Neighbor p-Center Problem
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14753 LNCS, Page: 9-14
2024
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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.
Conference Paper Description
In this paper, a Biased Random Key Genetic Algorithm is proposed to solve the α-neighbor p-center problem. A decoder and a local search procedure are developed obtaining competitive solutions for the problem. The objective of the ANPC is to locate p facilities serving demand points and assign a number α of facilities to each demand point. The objective function is evaluated as the maximum distance to the farthest facility assigned to each client, and the goal is to minimize this maximum distance. The proposed algorithm is compared with the best method found in the literature. The performance of the algorithm is evaluated over a large set of instances showing the robustness of the proposal.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85198035277&origin=inward; http://dx.doi.org/10.1007/978-3-031-62912-9_2; https://link.springer.com/10.1007/978-3-031-62912-9_2; https://dx.doi.org/10.1007/978-3-031-62912-9_2; https://link.springer.com/chapter/10.1007/978-3-031-62912-9_2
Springer Science and Business Media LLC
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