Adaptive binary artificial bee colony for multi-dimensional knapsack problem

Durgut, Rafet; Ayd?n, Mehmet Emin

Abstract

Purpose: The purpose of the study is to investigate how to solve for multi-dimensional knapsack problems better with higher robustness using binary artificial bee colony algorithms. Theory and Methods: The efficiency and effectiveness of metaheuristic optimization algorithms is managed with diverse search and fast approximation in the solution space. A balanced "exploration" and "exploitation" capability is required to achieve by the neighborhood operators towards the aimed efficiency. The majority of metaheuristic algorithms use either single operator or limited to genetic operators, which impose serious boundaries upon performance. In order to avoid this limitation, multiple neighborhood operators can be used within the search process orchestrated by a selection scheme. In this study, an adaptive operator selection scheme is studied with multiple binary operators embedded within artificial bee colony algorithm to solve the multidimensional backpack problem. Results: The performance gained with proposed artificial bee colony algorithm is compared with four different state-of-art metaheuristics approaches worked in the same circumstances. Three different benchmarking datasets are used for detailed comparisons. The statistical results including rank and Wilcoxon signed rank test values has been presented. Conclusion: Statistical analysis demonstrated that the proposed algorithm, adaptive binary artificial bee colony, has outperformed the state-of-art approaches with significant results over three benchmarking datasets. It has also been observed that the proposed algorithm produces more robust results too.

Citation

Durgut, R., & Aydın, M. E. (2021). Adaptive binary artificial bee colony for multi-dimensional knapsack problem. Journal of Gazi University Faculty of Engineering and Architecture, 36(4), 2333-2348. https://doi.org/10.17341/gazimmfd.804858

• Journal Article Type: Article
• Acceptance Date: May 1, 2021
• Online Publication Date: Sep 2, 2021
• Publication Date: Sep 2, 2021
• Deposit Date: Dec 10, 2021
• Journal: Journal of the Faculty of Engineering and Architecture of Gazi University
• Print ISSN: 1300-1884
• Electronic ISSN: 1304-4915
• Publisher: Gazi Üniversitesi, Diş Hekimliği Fakültesi
• Peer Reviewed: Peer Reviewed
• Volume: 36
• Issue: 4
• Pages: 2333-2348
• DOI: https://doi.org/10.17341/gazimmfd.804858
• Public URL: https://uwe-repository.worktribe.com/output/8050291