文献类型：期刊文献

英文题名：Five-Element Cycle Optimization Algorithm Based on an Integrated Mutation Operator for the Traveling Thief Problem

作者：Xiang, Yue[1];Guo, Jingjing[2];Mao, Zhengyan[3];Jiang, Chao[4,5];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]Shanghai Dev Ctr Comp Software Technol, Shanghai Key Lab Comp Software Testing & Evaluatin, Shanghai 201112, Peoples R China;[4]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Engn Res Ctr Digital Community, Minist Educ,Fac Informat Technol,Beijing Key Lab C, Beijing 100124, Peoples R China;[5]Beijing Univ Technol, Beijing Lab Intelligent Environm Protect, Beijing 100124, Peoples R China

年份：2024

卷号：16

期号：9

外文期刊名：SYMMETRY-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001323505700001)】；

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：combinatorial optimization; five-element cycle optimization; integrated mutation operator; heuristic operator; traveling thief problem

摘要：This paper presents a novel algorithm named Five-element Cycle Integrated Mutation Optimization (FECOIMO) for solving the Traveling Thief Problem (TTP). The algorithm introduces a five-element cycle structure that integrates various mutation operations to enhance both global exploration and local exploitation capabilities. In experiments, FECOIMO was extensively tested on 39 TTP instances of varying scales and compared with five common metaheuristic algorithms: Enhanced Simulated Annealing (ESA), Improved Grey Wolf Optimization Algorithm (IGWO), Improved Whale Optimization Algorithm (IWOA), Genetic Algorithm (GA), and Profit-Guided Coordination Heuristic (PGCH). The experimental results demonstrate that FECOIMO outperforms the other algorithms across all instances, particularly excelling in large-scale instances. The results of the Friedman test show that FECOIMO significantly outperforms other algorithms in terms of average solution, maximum solution, and solution standard deviation. Additionally, although FECOIMO has a longer execution time, its complexity is comparable to that of other algorithms, and the additional computational overhead in solving complex optimization problems translates into better solutions. Therefore, FECOIMO has proven its effectiveness and robustness in handling complex combinatorial optimization problems.