A novel binary artificial bee colony algorithm for the set-union knapsack problem

This article investigates how to employ artificial bee colony algorithm to solve Set-Union Knapsack Problem (SUKP). A mathematical model of SUKP, which is to be easily solved by evolutionary algorithms, is developed. A novel binary artificial bee colony algorithm (BABC) is also proposed by adopting...

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Vydáno v:Future generation computer systems Ročník 78; s. 77 - 86
Hlavní autoři: He, Yichao, Xie, Haoran, Wong, Tak-Lam, Wang, Xizhao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.01.2018
Témata:
Artificial bee colony
Infeasible solution
Repairing and optimization
Set-union knapsack problem
Repairing and optimization
Set-union knapsack problem
Artificial bee colony
Infeasible solution
ISSN:0167-739X, 1872-7115
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Shrnutí:This article investigates how to employ artificial bee colony algorithm to solve Set-Union Knapsack Problem (SUKP). A mathematical model of SUKP, which is to be easily solved by evolutionary algorithms, is developed. A novel binary artificial bee colony algorithm (BABC) is also proposed by adopting a mapping function. Furthermore, a greedy repairing and optimization algorithm (S-GROA) for handling infeasible solutions by employing evolutionary technique to solve SUKP is proposed. The consolidation of S-GROA and BABC brings about a new approach to solving SUKP. Extensive experiments are conducted upon benchmark datasets for evaluating the performance of our proposed models. The results verify that the proposed approach is significantly superior to the baseline evolutionary algorithms for solving SUKP such as A-SUKP, ABCbin and binDE in terms of both time complexity and solution performance. •A novel bee colony method based on the full mapping function is proposed.•Infeasible solutions are addressed by using a greedy strategy for Knapsack problems.•The method has better results than extant approximation algorithms to solve SUKP.•The proposed generic model can be integrated with other evolutionary algorithms.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2017.05.044