Enhanced Moth Search Algorithm for the Set-Union Knapsack Problems

Yanhong Feng; Jiao-Hong Yi; Gai-Ge Wang

doi:10.1109/ACCESS.2019.2956839

IEEE Access (Jan 2019)

Enhanced Moth Search Algorithm for the Set-Union Knapsack Problems

Yanhong Feng,
Jiao-Hong Yi,
Gai-Ge Wang

Affiliations

Yanhong Feng: ORCiD; School of Information Engineering, Hebei GEO University, Shijiazhuang, China
Jiao-Hong Yi: ORCiD; School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
Gai-Ge Wang: ORCiD; College of Information Science and Engineering, Ocean University of China, Qingdao, China

DOI: https://doi.org/10.1109/ACCESS.2019.2956839
Journal volume & issue: Vol. 7
pp. 173774 – 173785

Abstract

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As an important and novel model with multitudinous practical applications, the set-union knapsack problem (SUKP) is a challenging issue in combinatorial optimization. In this paper, we present an enhanced moth search algorithm (EMS) for solving SUKP, which introduces an enhanced interaction operator (EIO) by integrating differential mutation into the global harmony search and then Lévy flight is replaced by EIO. Comparative experimental results, which were conducted on three types of 30 popular SUKP benchmark instances, demonstrate that EMS algorithm is superior to or competitive with the other state-of-the-art metaheuristic algorithm. In particular, EMS reaches the best-known solutions for the great majority of test instances and improves the best-known solutions for six instances. Two critical ingredients of EIO is investigated to confirm their impact on the performance of EMS. The results show that both components have an important role in improving the performance of EMS.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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