文献类型：期刊文献

英文题名：Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief Problem

作者：Xiang, Yue[1];Guo, Jingjing[2];Jiang, Chao[3];Ma, Haibao[4];Liu, Mandan[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Space Engn Univ, Dept Aerosp Sci & Technol, Beijing 101416, Peoples R China;[3]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol,Minist Educ,Key Lab Computat, Engn Res Ctr Digital Community,Beijing Key Lab Com, Beijing 100124, Peoples R China; Beijing Univ Technol, Beijing Lab Intelligent Environm Protect, Beijing 100124, Peoples R China;[4]Vanderlande Ind Logist Automated Syst Shanghai Co, Technol Ctr, Shanghai 200131, Peoples R China

年份：2024

卷号：14

期号：17

外文期刊名：APPLIED SCIENCES-BASEL

收录：;EI(收录号：20243817044708);WOS:【SCI-EXPANDED(收录号:WOS:001311276000001)】；

基金：This research was funded by the Fundamental Research Funds for the Central Universities, Grant No. 222201917006, under the affiliation of the Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology), Ministry of Education.

语种：英文

外文关键词：bi-objective traveling thief problem; exploration and exploitation; five-element cycle model; multi-objective evolutionary optimization

摘要：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.