Adaptive Ant Colony Optimization with node clustering applied to the Travelling Salesman Problem

Stodola, Petr; Otřísal, Pavel; Hasilová, Kamila

doi:10.1016/j.swevo.2022.101056

Cited by 39 publications

(15 citation statements)

References 41 publications

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“…30 benchmark data samples from well-known TSPLIB benchmarks were used to evaluate the performance of the proposed method and different comparisons with experimental results were performed. The proposed algorithm outperformed these competing methods available in the literature in most cases [31].…”

Section: Related Workmentioning

confidence: 90%

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Solution for TSP/mTSP with an improved parallel clustering and elitist ACO

Baydogmus

2023

ComSIS

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Many problems that were considered complex and unsolvable have started to solve and new technologies have emerged through to the development of GPU technology. Solutions have established for NP-Complete and NP-Hard problems with the acceleration of studies in the field of artificial intelligence, which are very interesting for both mathematicians and computer scientists. The most striking one among such problems is the Traveling Salesman Problem in recent years. This problem has solved by artificial intelligence?s metaheuristic algorithms such as Genetic algorithm and Ant Colony optimization. However, researchers are always looking for a better solution. In this study, it is aimed to design a low-cost and optimized algorithm for Traveling Salesman Problem by using GPU parallelization, Machine Learning, and Artificial Intelligence approaches. In this manner, the proposed algorithm consists of three stages; Cluster the points in the given dataset with K-means clustering, find the shortest path with Ant Colony in each of the clusters, and connect each cluster at the closest point to the other. These three stages were carried out by parallel programming. The most obvious difference of the study from those found in the literature is that it performs all calculations on the GPU by using Elitist Ant Colony Optimization. For the experimental results, examinations were carried out on a wide variety of datasets in TSPLIB and it was seen that the proposed parallel KMeans-Elitist Ant Colony approach increased the performance by 30% compared to its counterparts.

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Section: Related Workmentioning

confidence: 90%

“…In 2022, researchers designed an algorithm that takes advantage of ACO to solve, improve overall performance, and shorten solution time in TSP [31]. For this, clustering on ACO parameters, dynamic pheromone evaporation and diversity of solutions in the population were used.…”

Section: Related Workmentioning

confidence: 99%

Solution for TSP/mTSP with an improved parallel clustering and elitist ACO

Baydogmus

2023

ComSIS

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“…Several constraints must be satisfied. Constraint (2) ensures that all customers must be served, constraint (3) forces each vehicle to start and end its route at the depot, and constraint (4) ensures that each customer is served just once. {𝑟 1 1 ; … ; 𝑟 𝑛 1 1 ; 𝑟 1 2 ; … ; 𝑟 𝑛 2 2 ; 𝑟…”

Section: A Problem Formulationmentioning

confidence: 99%

“…This algorithm has been extended by three novel techniques which further improve its performance. These techniques, originally developed by the authors for the TSP in [4], have been modified and adapted for the MDVRP, and integrated into the algorithm. They are as follows:…”

Section: B Contributionsmentioning

confidence: 99%

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Stodola

Nohel

2023

IEEE Trans. Evol. Computat.

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This article deals with the novel metaheuristic algorithm based on the Ant Colony Optimization (ACO) principle. It implements several novel mechanisms that improve its overall performance, lower the optimization time, and reduce the negative behavior which is typically connected with ACO-based algorithms (such as prematurely falling into local optima, or the impact of setting of control parameters on the convergence for different problem configurations). The most significant novel techniques, implemented for the first time to solve the Multi-Depot Vehicle Routing Problem (MDVRP), are as follows: (a) node clustering where transition vertices are organized into a set of candidate lists called clusters; (b) adaptive pheromone evaporation which is adapted during optimization according to the diversity of the population of ant solutions (measured by information entropy). Moreover, a new termination condition, based also on the population diversity, is formulated. The effectiveness of the proposed algorithm for the MDVRP is evaluated via a set of experiments on 23 well-known benchmark instances. Performance is compared with several state-of-the-art metaheuristic methods; the results show that the proposed algorithm outperforms these methods in most cases. Furthermore, the novel mechanisms are analyzed and discussed from points of view of performance, optimization time, and convergence. The findings achieved in this article bring new contributions to the very popular ACO-based algorithms; they can be applied to solve not only the MDVRP, but also, if adapted, to related complex NP-hard problems.

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“…is mechanism led to more focused search process and greatly improved the search efficiency. Stodola et al [49] presented an adaptive ACO model based on the node clustering method applied to the TSP. e proposed model implemented three new techniques (contains node clustering, adaptive pheromone volatilization, and termination conditions) to improve the overall performance and reduce the execution time and negative impacts.…”

Section: Optimization Algorithms To Travel Route Planningmentioning

confidence: 99%

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

Sun

Chen

Jin

et al. 2022

Journal of Advanced Transportation

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With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists’ travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists’ travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from the travel pattern sequences by using a frequent pattern mining method. Finally, an improved ant colony optimization (ACO) algorithm with a novel extensible heuristic factor approach is adopted to search the multi-objective optimal travel routes according to the popularity of attractions and travel time of tourists. The experimental results indicate that the proposed framework is efficient in recommending multi-objective optimal travel routes considering tourists’ travel time and attractions’ popularity while ensuring that the recommended travel route is suitable.

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Adaptive Ant Colony Optimization with node clustering applied to the Travelling Salesman Problem

Cited by 39 publications

References 41 publications

Solution for TSP/mTSP with an improved parallel clustering and elitist ACO

Solution for TSP/mTSP with an improved parallel clustering and elitist ACO

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

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