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Subject: search.filters.subject.traveling salesman problem

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    Ant Colony Optimization for Solving Electric Vehicle Traveling Salesman Problems
    (2026-01-01)
    Janjarassuk, Udom
    Electric Vehicles (EVs) have become very popular nowadays due to their affordability, lower operating cost and maintenance cost, and environmental benefits. In this paper, we study an electric vehicle variant of the traveling salesman problem (EV-TSP) by using a modified ant colony optimization (ACO) method. Unlike the traditional traveling salesman problem (TSP), the electric vehicle variant of the TSP is more complex due to the limited battery range and the requirement of recharging along the trip. To tackle these difficulties, our proposed algorithm incorporates the battery level constraint as a heuristic information for choosing the next visiting node. We use a sigmoid function to capture the probability of node selection in addition to the pheromone value and visibility of ant. The type of nodes is also considered during the node selection. When the remaining battery level of the vehicle is low, the charging station nodes will have higher probability of being selected compared to the customer nodes, and vice versa. The algorithm was implemented in C++ and tested by using a number of benchmark instances from TSPLIB. The problem instances were modified by adding vehicle range and charging stations with their associated locations. Computational results were reported to show the effectiveness of the algorithm.
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    ENHANCEMENT OF ARTIFICIAL IMMUNE SYSTEMS FOR THE TRAVELING SALESMAN PROBLEM THROUGH HYBRIDIZATION WITH NEIGHBORHOOD IMPROVEMENT AND PARAMETER FINE-TUNING
    (2024-01-01)
    Thapatsuwan, Peeraya
    ;
    Thapatsuwan, Warattapop
    ;
    Kulworatit, Chaichana
    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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    Hybrid Ant Colony Optimization Method for the Traveling Salesman Problem
    (2024-01-01)
    Janjarassuk, Udom
    The traveling salesman problem (TSP) is a classic problem in computer science and operations research which involves finding the shortest possible route that visits a given set of cities. In this paper, we propose a hybrid algorithm for solving such problem. The algorithm combines the ant colony optimization (ACO) method with the 2-opt heuristic to improve the efficiency for solving the TSP. Instances from the TSPLIB were used to test the algorithm. The results showed that the hybrid ACO algorithm was more effective in solving the TSP as compared to the traditional ACO or the 2-opt heuristic methods.
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    Improved Ant Colony Optimization with Local Search for Traveling Salesman Problem
    (2019-07-01)
    Thammano, Arit
    ;
    Oonsrikaw, Yindee
    Improved Ant Colony Optimization (IACO) with a local search algorithm is proposed for better finding near optimal solution of Traveling Salesman Problem (TSP). In this work, IACO was combined with each of four local search operators in order to enhance IACO exploration capacity. The performances of IACO and the four local search were comparatively tested on a set of benchmark symmetric TSP instances. The experimental results show that IACO.
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    An early exploratory method to avoid local minima in Ant Colony System
    (2015-08-17)
    Satukitchai, Thanet
    ;
    Jearanaitanakij, Kietikul
    Ant colony optimization (ACO) is a famous technique for solving the traveling salesman problem (TSP). However, one of its disadvantages is that it can be easily trapped into local optima. Although there is an attempt by Ant Colony System (ACS) to improve the local optima by introducing local pheromone updating rule, the chance of being trapped into local optima still persists. This paper presents an extension of ACS algorithm by modifying the construction solution phase of the algorithm, the phase that ants move and build their tours, for reducing the duplication of tours produced by ants. This modification forces ants to select unique path which has never visited by other ants in the current iteration. As a result, the modified ACS can explore more search space than the conventional ACS. The experimental results on five standard data sets from TSPLIB show improvements on both the quality and the number of optimal solutions founded.