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Open AccessArticle
Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief Problem
1
Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
2
Department of Aerospace Science and Technology, Space Engineering University, Beijing 101416, China
3
Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Engineering Research Center of Digital Community, Ministry of Education, Beijing Artificial Intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection, Beijing University of Technology, Beijing 100124, China
4
Technology Center, Vanderlande Industries Logistics Automated Systems Shanghai Co., Ltd., Shanghai 200131, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(17), 7468; https://doi.org/10.3390/app14177468 (registering DOI)
Submission received: 9 July 2024
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Revised: 18 August 2024
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Accepted: 22 August 2024
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Published: 23 August 2024
Abstract
In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.
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MDPI and ACS Style
Xiang, Y.; Guo, J.; Jiang, C.; Ma, H.; Liu, M.
Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief Problem. Appl. Sci. 2024, 14, 7468.
https://doi.org/10.3390/app14177468
AMA Style
Xiang Y, Guo J, Jiang C, Ma H, Liu M.
Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief Problem. Applied Sciences. 2024; 14(17):7468.
https://doi.org/10.3390/app14177468
Chicago/Turabian Style
Xiang, Yue, Jingjing Guo, Chao Jiang, Haibao Ma, and Mandan Liu.
2024. "Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief Problem" Applied Sciences 14, no. 17: 7468.
https://doi.org/10.3390/app14177468
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