A large population island framework for the unconstrained binary quadratic problem

The unconstrained binary quadratic problem is an NP-hard problem and has applications in many fields. Recently, the problem has attracted much interest in the field of quantum optimization, as it is directly related to the Ising problem in physics and the development of quantum computers. However, e...

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Vydáno v:Computers & operations research Ročník 168; s. 106684
Hlavní autoři: Goudet, Olivier, Goëffon, Adrien, Hao, Jin-Kao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.08.2024
Elsevier
Témata:
Combinatorial optimization
Computer Science
Evolutionary search
Heuristics
Island model
Parallel search
Quadratic unconstrained binary optimization
Unconstrained binary quadratic problem
Unconstrained binary quadratic problem
Evolutionary search
Island model
Heuristics
Parallel search
Combinatorial optimization
Quadratic unconstrained binary optimization
ISSN:0305-0548, 1873-765X
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Shrnutí:The unconstrained binary quadratic problem is an NP-hard problem and has applications in many fields. Recently, the problem has attracted much interest in the field of quantum optimization, as it is directly related to the Ising problem in physics and the development of quantum computers. However, effectively solving large instances of this problem remains a major challenge for existing solution methods. To advance the state of the art in solving the problem on a large scale, we propose an evolutionary algorithm with a very large population organized in different islands and integrating a new pairing and recombination method to produce promising offspring in each generation. Numerous experiments are conducted to evaluate the effects of different pairing strategies, crossovers, and migration topologies. This research has led to the discovery of new bounds for difficult instances of the maximum cut problem, which has been transformed using the binary quadratic formulation. •The unconstrained binary quadratic problem has numerous applications.•We present a large population island framework for solving the problem.•We show an implementation of the framework using GPU-based parallel computing.•We report new best known results for 6 challenging maximum cut instances.•The approach can help to better solve related problems.
ISSN:0305-0548
1873-765X
DOI:10.1016/j.cor.2024.106684