f-Flip strategies for unconstrained binary quadratic programming

Unconstrained binary quadratic programming (UBQP) provides a unifying modeling and solution framework for solving a remarkable range of binary optimization problems, including many accompanied by constraints. Current methods for solving UBQP problems customarily rely on neighborhoods consisting of f...

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Bibliographic Details
Published in:Annals of operations research Vol. 238; no. 1-2; pp. 651 - 657
Main Authors: Glover, Fred, Hao, Jin-Kao
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2016
Springer
Springer Nature B.V
Springer Verlag
Subjects:
Algorithms
Business and Management
CAD
Combinatorics
Computer aided design
Computer Science
Mathematical research
Operations research
Operations Research/Decision Theory
Optimization
Quadratic programming
Studies
Theory of Computation
Variables
Multi-start algorithms
Binary quadratic programming
Computational efficiency
Metaheuristics
0–1 Optimization
metaheuristics
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:Unconstrained binary quadratic programming (UBQP) provides a unifying modeling and solution framework for solving a remarkable range of binary optimization problems, including many accompanied by constraints. Current methods for solving UBQP problems customarily rely on neighborhoods consisting of flip moves that select one or more binary variables and “flip” their values to the complementary value (from 1 to 0 or from 0 to 1). We introduce a class of approaches called f-flip strategies that include a fractional value f as one of those available to the binary variables during intermediate stages of solution. A variety of different f-flip strategies, particularly within the context of multi-start algorithms, are proposed for pursuing intensification and diversification goals in metaheuristic algorithms, accompanied by special rules for evaluating and executing f-flips efficiently.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-015-2076-1