ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING - Peeraya THAPATSUWAN; Warattapop THAPATSUWAN; Chaichana KULWORATIT (Applied Computer Science)
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.