Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem
The unconstrained binary quadratic programming problem (BQP) is known to be NP-hard and has many practical applications. This paper presents a simulated annealing (SA)-based heuristic for the BQP. The new SA heuristic for the BQP is based on a simple (1-opt) local search heuristic and designed with...
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| Published in: | European journal of operational research Vol. 134; no. 1; pp. 103 - 119 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Amsterdam
Elsevier B.V
01.10.2001
Elsevier Elsevier Sequoia S.A |
| Series: | European Journal of Operational Research |
| Subjects: | |
| ISSN: | 0377-2217, 1872-6860 |
| Online Access: | Get full text |
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| Abstract | The unconstrained binary quadratic programming problem (BQP) is known to be NP-hard and has many practical applications. This paper presents a simulated annealing (SA)-based heuristic for the BQP. The new SA heuristic for the BQP is based on a simple (1-opt) local search heuristic and designed with a simple cooling schedule, but the multiple annealing processes are adopted. To show practical performances of the SA, we test on publicly available benchmark instances of large size ranging from 500 to 2500 variables and compare them with other heuristics such as multi-start local search, the previous SA, tabu search, and genetic algorithm incorporating the 1-opt local search. Computational results indicate that our SA leads to high-quality solutions with short times and is more effective than the competitors particularly for the largest benchmark set. Furthermore, the values of new best-known solutions found by the SA for several large instances are also reported. |
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| AbstractList | The unconstrained binary quadratic programming problem (BQP) is known to be NP-hard and has many practical applications. This paper presents a simulated annealing (SA)-based heuristic for the BQP. The new SA heuristic for the BQP is based on a simple (1-opt) local search heuristic and designed with a simple cooling schedule, but the multiple annealing processes are adopted. To show practical performances of the SA, we test on publicly available benchmark instances of large size ranging from 500 to 2500 variables and compare them with other heuristics such as multi-start local search, the previous SA, tabu search, and genetic algorithm incorporating the 1-opt local search. Computational results indicate that our SA leads to high-quality solutions with short times and is more effective than the competitors particularly for the largest benchmark set. Furthermore, the values of new best-known solutions found by the SA for several large instances are also reported. |
| Author | Narihisa, Hiroyuki Katayama, Kengo |
| Author_xml | – sequence: 1 givenname: Kengo surname: Katayama fullname: Katayama, Kengo email: katayama@ice.ous.ac.jp – sequence: 2 givenname: Hiroyuki surname: Narihisa fullname: Narihisa, Hiroyuki |
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| Keywords | Local search Unconstrained binary quadratic programming Heuristics Large-size problems Simulated annealing |
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| Snippet | The unconstrained binary quadratic programming problem (BQP) is known to be NP-hard and has many practical applications. This paper presents a simulated... |
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| SubjectTerms | Algorithms Heuristic Heuristics Large-size problems Local search Mathematical programming Quadratic programming Searches Simulated annealing Simulation Studies Unconstrained binary quadratic programming |
| Title | Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem |
| URI | https://dx.doi.org/10.1016/S0377-2217(00)00242-3 http://econpapers.repec.org/article/eeeejores/v_3a134_3ay_3a2001_3ai_3a1_3ap_3a103-119.htm https://www.proquest.com/docview/204140913 |
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