Multi-wave tabu search for the boolean quadratic programming problem with generalized upper bound constraints

The boolean quadratic programming problem with generalized upper bound constraints (BQP-GUB) is an NP-hard problem with many practical applications. In this study, we propose an effective multi-wave tabu search algorithm for solving BQP-GUB. The algorithm performs a sequence of search waves, where e...

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Vydáno v:Computers & operations research Ročník 150; s. 106077
Hlavní autoři: Shang, Zhen, Hao, Jin-Kao, Zhao, Songzheng, Wang, Yang, Ma, Fei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2023
Elsevier
Témata:
Binary quadratic programming
Computer Science
Graph theory
Hybrid metaheuristic
Tabu search
Graph theory
Binary quadratic programming
Tabu search
Hybrid metaheuristic
ISSN:0305-0548, 1873-765X
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Shrnutí:The boolean quadratic programming problem with generalized upper bound constraints (BQP-GUB) is an NP-hard problem with many practical applications. In this study, we propose an effective multi-wave tabu search algorithm for solving BQP-GUB. The algorithm performs a sequence of search waves, where each wave alternates between the forward and reverse phases, and the transition between two adjacent waves depends on a hybrid perturbation phase. The forward phase employs tabu search to reach a critical solution and the reverse phase follows to reverse previously performed moves and perform an equal number of moves by referring to the search information gathered from the latest search process. The hybrid perturbation phase randomly chooses a directed strategy, a frequency guided strategy and a recency guided strategy to achieve search diversification. Experimental results on 78 standard instances indicate that the proposed algorithm is able to improve the lower bounds for 6 instances and match the best solutions in the literature for most instances within competitive time. •Propose a multi-wave tabu search algorithm for BQP-GUB.•Design a tabu search based forward phase for intensification.•Design an information guided reverse phase for adjustments.•Employ memory based strategies to guide search process.•Find new lower bounds for 6 instances.
ISSN:0305-0548
1873-765X
DOI:10.1016/j.cor.2022.106077