{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:58:08Z","timestamp":1782835088372,"version":"3.54.5"},"reference-count":7,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,30]]},"abstract":"<jats:p>The traveling thief problem (TTP) is a typical combinatorial optimization problem that integrates the computational complexity of the traveling salesman problem (TSP) and the knapsack problem (KP). The interdependent and mutually restrictive relationship between these two sub-problems brings new challenges to the heuristic optimization algorithm for solving the TTP problem. This paper first analyzes the performance of three sub-component combined iterative algorithms: Memetic Algorithm with the Two-stage Local Search (MATLS), S5, and CS2SA algorithms, which all employ the Chained Lin-ighan (CLK) algorithm to generate the circumnavigation path. To investigate the influence of different traveling routes on the performance of TTP solving algorithms, we propose a combinatorial iterative TTP solving algorithm based on the Ant Colony Optimization (ACO) and MAX-MIN Ant System (MMAS). Finally, the experimental investigations suggest that the traveling route generation method dramatically impacts the performance of TTP solving algorithms. The sub-component combined iterative algorithms based on the MMAS algorithm to generate the circumnavigation path has the best practical effect.<\/jats:p>","DOI":"10.3233\/jifs-221032","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T11:59:54Z","timestamp":1666353594000},"page":"1943-1956","source":"Crossref","is-referenced-by-count":2,"title":["Influence of subproblem solutions on the quality of traveling thief problem solutions"],"prefix":"10.1177","volume":"44","author":[{"given":"Junfeng","family":"Chen","sequence":"first","affiliation":[{"name":"College of Internet of Things Engineering, Hohai University, Changzhou, China"},{"name":"Jiangsu Key Laboratory of Power Transmission and Distribution Equipment, Hohai University, Changzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaijun","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Internet of Things Engineering, Hohai University, Changzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingwu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Internet of Things Engineering, Hohai University, Changzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Altangerel","family":"Ayush","sequence":"additional","affiliation":[{"name":"School of ICT, Mongolian University of Science and Technology, Ulaanbaatar, Mongolia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-221032_ref2","doi-asserted-by":"crossref","unstructured":"Chiong R. , Weise T. Variants of evolutionary algorithms for real-world applications[M]. 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