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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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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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Water Level Monitoring and Evacuation Guideline Using Ant Colony Optimization on Mobile Application
(2020-08-01)
Kimpan, Warangkhana
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Kasetvetin, Sirawich
;
Kimpan, Chom
The most natural disasters that have happened in Thailand are storm and flood problems. The people who live near water sources have no warning about the overflowing of water nearby, so they cannot evacuate or get help in time. Thus, there is always a high risk of losing properties or lives. In order to alleviate the losses, this paper proposes water level monitoring on Android application from Internet of Things devices and the guideline for evacuation by applying Ant Colony Optimization which is inspired by the real ant colony. Internet of Things devices are used to monitor the water levels in community for the user who lives near the water sources or near the places which have high risk of flooding. The Hydrostatic level sensors are placed in the water basin near the community to measure the height of the water which can also be observed in real time from mobile application. When the height of the water reaches the critical value that was set in the application, it sends notifications to the user. Moreover, Line bot is used to let the user knows the potential risks from rising water levels. At the critical level, the user needs to evacuate to a safe place located nearby. The application will guide the user to follow the direction to the most safety destination. In case of many people are already evacuated in one place and it reached the maximum amount of limitation, the application will change the recommendation direction to other places nearby using Ant Colony Optimization algorithm for making decisions.
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Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine
(2019-06-01)
Sreng, Syna
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Maneerat, Noppadol
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Hamamoto, Kazuhiko
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Panjaphongse, Ronakorn
Diabetic retinopathy is the major issue of diabetes-induced blindness worldwide but is curable if detected in time. Cotton wool spots (CWSs) are the critical lesions of diabetic retinopathy, which indicate not only advanced nonproliferative but also preproliferative diabetic retinopathy. It is crucial to detect CWSs for grading the severity of diabetic retinopathy. By grading the severity of diabetic retinopathy accurately, the eye specialist can make an effective treatment plan to protect the patient's vision against blindness. CWSs detection remains challenging because of their uneven appearance, in which some CWSs are not clearly visible and some resemble hard exudates. This paper proposed an automatic CWS detection method based on adaptive thresholding and ant colony optimization (ACO) coupled with support vector machine (SVM). One-hundred and sixty-two features from five feature sets, namely morphologies, first-order statistics, gray-level co-occurrence matrix, gray-level run length matrix, and lacunarity, are extracted, and then four feature selection methods,namely genetic algorithm, particle swarm optimization, stepwise method, and ACO, are coupled with SVM classifiers. The evaluation results of the proposed methods on local, standard diabetic retinopathy database calibration level 1, and high-resolution fundus image database datasets containing 319 images indicate that ACO coupling cubic SVM performs better than the other pairs with sensitivity 90.16%, specificity 97.92%, accuracy 96.96%, and area under receiver operating characteristic curve 97.19%. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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Enhanced Ant Colony Optimization with Local Search
(2018-09-14)
Oonsrikaw, Yindee
;
Thammano, Arit
The ant colony optimization (ACO) algorithm frequently gets trapped around local optimum solutions and does not approach the global optimum solution of vehicle routing problem. This work attempts to remedy this drawback by modifying the ant system (AS) algorithm, an instance of ACO. The modification includes a new method of route construction, new weight for improving the pheromone density of each route, and introduction of SA to improve the quality of the solutions from local search. It is called an enhanced ant colony optimization with local search or EACOL. A performance test was performed on EACOL on 10 standard datasets comparing it to those of the ant system algorithm and elitist ant system, and the results show that the proposed algorithm performs better than these two algorithms on these datasets.
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Modified ant colony optimization with updating pheromone by leader and re-initialization pheromone for travelling salesman problem
(2018-08-13)
Ratanavilisagul, Chiabwoot
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Pasaya, Bundit
Ant Colony Optimization (ACO) algorithm is a stochastic algorithm. It is used for solving combinational optimization problem. The ant colony walks along density of pheromone from ant's nest to feeding sources. It leads to create shortest path from ant's nest to feeding sources. Normally, ACO encounters the problem of trapping in local optimum. To improve solutions, 2-Opt algorithm is applied with ACO. However, 2-Opt algorithm cannot solve trapping in local optimum of ACO and cannot improve searching performance of ACO. This paper proposed improving ACO algorithm by the results from searching of 2-Opt algorithm are applied with pheromone of ants. Moreover, when ant colony occur trapping in local optimum, the pheromone of ants is re-initialized to solve trapping in local optimum problem. The proposed technique is tested on twenty-three maps from the Traveling Salesman Problem Library (TSPLIB) and gives more satisfied search results in comparison with ACOs.
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An early exploratory method to avoid local minima in Ant Colony System
(2015-08-17)
Satukitchai, Thanet
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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.

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