ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING
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 poin...
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Format: | Article |
Language: | English |
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Polish Association for Knowledge Promotion
2024-12-01
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Series: | Applied Computer Science |
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Online Access: | https://ph.pollub.pl/index.php/acs/article/view/6552 |
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author | Peeraya THAPATSUWAN Warattapop THAPATSUWAN Chaichana KULWORATIT |
author_facet | Peeraya THAPATSUWAN Warattapop THAPATSUWAN Chaichana KULWORATIT |
author_sort | Peeraya THAPATSUWAN |
collection | DOAJ |
description | 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.
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format | Article |
id | doaj-art-84c92d378b8f4957a77ba52b4907b35e |
institution | Kabale University |
issn | 2353-6977 |
language | English |
publishDate | 2024-12-01 |
publisher | Polish Association for Knowledge Promotion |
record_format | Article |
series | Applied Computer Science |
spelling | doaj-art-84c92d378b8f4957a77ba52b4907b35e2025-01-09T12:44:46ZengPolish Association for Knowledge PromotionApplied Computer Science2353-69772024-12-0120410.35784/acs-2024-43ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNINGPeeraya THAPATSUWAN0https://orcid.org/0009-0007-4187-124XWarattapop THAPATSUWAN1https://orcid.org/0000-0001-7740-727XChaichana KULWORATIT2https://orcid.org/0000-0001-9959-4264Department of Computational Science and Digital Technology, Faculty of Liberal Arts and Science, Kasetsart University Kamphaeng Saen CampusDepartment of Computational Science and Digital Technology, Faculty of Liberal Arts and Science, Kasetsart University Kamphaeng Saen CampusDepartment of Computer Science, School of Science, King Mongkut’s Institute of Technology LadkrabangThis 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. https://ph.pollub.pl/index.php/acs/article/view/6552Artificial Immune SystemsFine-TuningHybridizationTraveling Salesman Problem |
spellingShingle | Peeraya THAPATSUWAN Warattapop THAPATSUWAN Chaichana KULWORATIT ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING Applied Computer Science Artificial Immune Systems Fine-Tuning Hybridization Traveling Salesman Problem |
title | ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING |
title_full | ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING |
title_fullStr | ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING |
title_full_unstemmed | ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING |
title_short | ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING |
title_sort | enhancement of artificial immune systems for the traveling salesman problem through hybridization with neighborhood improvement and parameter fine tuning |
topic | Artificial Immune Systems Fine-Tuning Hybridization Traveling Salesman Problem |
url | https://ph.pollub.pl/index.php/acs/article/view/6552 |
work_keys_str_mv | AT peerayathapatsuwan enhancementofartificialimmunesystemsforthetravelingsalesmanproblemthroughhybridizationwithneighborhoodimprovementandparameterfinetuning AT warattapopthapatsuwan enhancementofartificialimmunesystemsforthetravelingsalesmanproblemthroughhybridizationwithneighborhoodimprovementandparameterfinetuning AT chaichanakulworatit enhancementofartificialimmunesystemsforthetravelingsalesmanproblemthroughhybridizationwithneighborhoodimprovementandparameterfinetuning |