Metaheuristic Algorithms

Yang Wang () and Jin-Kao Hao ()
Additional contact information
Yang Wang: Northwestern Polytechnical University
Jin-Kao Hao: Universitë d’Angers

Chapter Chapter 9 in The Quadratic Unconstrained Binary Optimization Problem, 2022, pp 241-259 from Springer

Abstract: Abstract Metaheuristic algorithms are practically used to produce approximate solutions to large QUBO instances that cannot be solved exactly due to the high computational complexity. This chapter is dedicated to a review on the general metaheuristic approach for solving the QUBO. First, we present some basic components of local search that are widely used in the design of state-of-the-art metaheuristic algorithms for the problem. Then we overview the metaheuristic algorithms in the literature by groups of fast solving heuristics, local search based methods and population based search methods. Finally, we review some of the most popular and effective metaheuristic algorithms and present experimental results on different sets of instances.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-04520-2_9

Ordering information: This item can be ordered from
http://www.springer.com/9783031045202

DOI: 10.1007/978-3-031-04520-2_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().