Implementation of K-Means and crossover ant colony optimization algorithm on multiple traveling salesman problem

Kusumahardhini, N; Hertono, Gatot Fatwanto; Handari, Bevina D.

doi:10.1088/1742-6596/1442/1/012035

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Therefore, some clusters were combined and resulted in 8 clusters remaining with each unique characteristics per clusters. These findings leveraged the advantage of using ant clustering algorithm and the simplicity of K-means, so the initial cluster points can be determined optimally [15], [28]. While these two algorithms worked perfectly, the company's policy regarding how these generated clusters should be treated is the important thing.…”

Section: Resultsmentioning

confidence: 99%

Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

Palupi

Fakhruzzaman

2022

IJECE

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<span lang="EN-US">We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters’ customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem. </span>

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Section: Resultsmentioning

confidence: 99%

Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

Palupi

Fakhruzzaman

2022

IJECE

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“…Researchers worked on K-Means and Cross Ant Colony Optimization methods to solve the multiple traveling salesman problem in 2020 [20]. They used the K-Means algorithm to determine the ant colony to determine the tour to find the area that each seller would visit.…”

Section: Related Workmentioning

confidence: 99%

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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“…In [20], the use of the K-means with crossover ACO is proposed to solve the MTSP. The main idea was to divide cities into many clusters, and then apply crossover ACO for each cluster to find the best route for each salesman.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Solving the Multiple Traveling Salesman Problem Using Memetic Algorithm

Taani

Al-Afifi

2022

Artificial Intelligence Evolution

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The Multiple Traveling Salesman Problem (MTSP) is considered as an NP-complete problem due to the difficulty of finding the shortest tour between different cities with a set of constraints such as visiting each city once by one salesman. The solution tour represents the sum of all tours' costs performed by n salesmen. In this research, we propose a novel approach to find different solutions for various instances of MTSP based on a memetic algorithm using Genetic Algorithm (GA) and Hill-Climbing (HC) algorithm. The experimental results proved that the memetic approach achieved promising results i.e., minimum total tour compared to other existing approaches.

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Implementation of K-Means and crossover ant colony optimization algorithm on multiple traveling salesman problem

Cited by 8 publications

References 9 publications

Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

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

Solving the Multiple Traveling Salesman Problem Using Memetic Algorithm

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