ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING
Peeraya THAPATSUWAN
Department of Computational Science and Digital Technology, Faculty of Liberal Arts and Science, Kasetsart University Kamphaeng Saen Campus (Thailand)
https://orcid.org/0009-0007-4187-124X
Warattapop THAPATSUWAN
warattapop.t@ku.thDepartment of Computational Science and Digital Technology, Faculty of Liberal Arts and Science, Kasetsart University Kamphaeng Saen Campus (Thailand)
https://orcid.org/0000-0001-7740-727X
Chaichana KULWORATIT
Department of Computer Science, School of Science, King Mongkut’s Institute of Technology Ladkrabang (Thailand)
https://orcid.org/0000-0001-9959-4264
Abstract
This research investigates the enhancement of Artificial Immune Systems (AIS) for solving the Traveling Salesman Problem (TSP) through hybridization with Neighborhood Improvement (NI) and parameter fine-tuning. Two main experiments were conducted: Experiment A identified the optimal integration points for NI within AIS, revealing that position 2 (AIS+NIpos2) improved solution quality by an average of 27.78% compared to other positions. Experiment B benchmarked AIS performance with various enhancement techniques. Using symmetric and asymmetric TSP datasets, the results showed that integrating NI at strategic points and fine-tuning parameters boosted AIS performance by up to 46.27% in some cases. The hybrid and fine-tuned version of AIS (AIS-th) consistently provided the best solution quality, with up to a 50.36% improvement, though it required more computational time. These findings emphasize the importance of strategic combinations and fine-tuning for creating effective optimization algorithms.
Keywords:
Artificial Immune Systems, Fine-Tuning, Hybridization, Traveling Salesman ProblemReferences
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Authors
Peeraya THAPATSUWANDepartment of Computational Science and Digital Technology, Faculty of Liberal Arts and Science, Kasetsart University Kamphaeng Saen Campus Thailand
https://orcid.org/0009-0007-4187-124X
Authors
Warattapop THAPATSUWANwarattapop.t@ku.th
Department of Computational Science and Digital Technology, Faculty of Liberal Arts and Science, Kasetsart University Kamphaeng Saen Campus Thailand
https://orcid.org/0000-0001-7740-727X
Authors
Chaichana KULWORATITDepartment of Computer Science, School of Science, King Mongkut’s Institute of Technology Ladkrabang Thailand
https://orcid.org/0000-0001-9959-4264
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