Solving Traveling Salesman Problem using Firefly algorithm and K-means Clustering

Jaradat, Ameera; Matalkeh, Bararah; Diabat, Waed

doi:10.1109/jeeit.2019.8717463

Cited by 28 publications

(12 citation statements)

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“…In [28], a new clusterized technique was introduced based on K-means to solve TSP by using the firefly algorithm which we extended that by using the WOA as a new studying. In TSP, with the view to minimize the cost function and hence improve the efficiency, the WOA is combined with the K-means.…”

Section: Issn: 2252-8938mentioning

confidence: 99%

Solving a traveling salesman problem using meta-heuristics

Nejad

Fazekas

2022

IJ-AI

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In this article, we have introduced an advanced new method of solving a traveling salesman problem (TSP) with the whale optimization algorithm (WOA), and K-means which is a partitioning-based algorithm used in clustering. The whale optimization algorithm first was introduced in 2016 and later used to solve a TSP problem. In the TSP problem, finding the best path, which is the path with the lowest value in the fitness function, has always been difficult and time-consuming. In our algorithm, we want to find the best tour by combining it with K-means which is a clustering method. In other words, we want to divide our problem into smaller parts called clusters, and then we join the clusters based on their distances. To do this, the WOA algorithm, TSP, and K-means must be combined. Separately, the WOA-TSP algorithm which is an unclustered algorithm is also implemented to be compared with the proposed algorithm. The results are shown through some figures and tables, which prove the effectiveness of this new method.

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Section: Issn: 2252-8938mentioning

confidence: 99%

Solving a traveling salesman problem using meta-heuristics

Nejad

Fazekas

2022

IJ-AI

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“…A light absorption coefficient γ (Gamma) with the value = 1 is used. The cost (distance) between two cities is represented by r. The cost (distance) between two cities is derived from the Equation (2) [15].…”

Section: A Initializationmentioning

confidence: 99%

“…The firefly updates its location using the current location. The less bright firefly i move towards brighter firefly j using following equation 3 [15].…”

Section: Movementmentioning

confidence: 99%

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Solving Random Travelling Salesman Problem using Firefly Algorithm

Sureja*

2020

IJITEE

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The firefly algorithm is a recently developed optimization algorithm, which is suitable for solving any kind of discrete optimization problems. This is an algorithm inspired from the nature. In this paper, a firefly algorithm is proposed to solve random traveling salesman problem. The solution to this problem is already proposed by the algorithms like simulated annealing, genetic algorithms and ant colony algorithms. This algorithm is developed to deal with the issue of accuracy and convergence rate in the solutions provided by those algorithms. A comparison of the results produced by proposed algorithm with the results of simulated annealing, genetic algorithms and ant colony algorithm is given. Finally, the effectiveness of the proposed algorithm is discussed.

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“…Sobre el particular, no se conoce algoritmos que tenga la posibilidad de buscar el número óptimo de multiagentes a partir de conocer el tiempo máximo tolerable en recorrer todos los nodos, más aún cuando los nodos se encuentran dispersos y agrupados en pequeños grupos; por lo que es necesario hacer agrupamientos antes de encontrar el recorrido mínimo en cada agrupamiento. En ese sentido (Jaradat et al, 2019), propone una solución para TSP utilizando Algoritmo de Firefly (FA) y agrupamiento de k-means. El enfoque propuesto comprende tres pasos principales: agrupar los nodos, encontrar la ruta óptima en cada grupo y volver a conectar los grupos.…”

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Optimización de las rutas para la intervención de pozos de petróleo

et al. 2020

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El objetivo es proponer un programa óptimo de intervención de pozos petroleros mediante la reasignación de rutas de los equipos SWAB en empresas dedicadas a estas operaciones. La primera fase hace uso del método de agrupamiento utilizando k-medias con el propósito de obtener un número de agrupamientos. La segunda fase utiliza algoritmos genéticos (AG) para encontrar el recorrido óptimo en cada agrupamiento de la primera fase y optimizar el número de agrupamientos. Los datos de tiempo de intervención, las coordenadas geográficas y las distancias de recorrido se obtuvieron visitando cada uno de los pozos. Los resultados muestran que el algoritmo propone un programa óptimo para el caso en estudio, obteniendo un ahorro de S/ 664 994.06 por año en estas operaciones. En conclusión, la implementación del algoritmo en dos fases (clústering y AG) es una solución para problemas de planificación de rutas en transporte con las características del proceso swab.

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Solving Traveling Salesman Problem using Firefly algorithm and K-means Clustering

Cited by 28 publications

References 4 publications

Solving a traveling salesman problem using meta-heuristics

Solving a traveling salesman problem using meta-heuristics

Solving Random Travelling Salesman Problem using Firefly Algorithm

Optimización de las rutas para la intervención de pozos de petróleo

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