Use of Ant Colony Optimization Algorithm for Determining Traveling Salesman Problem Routes

Silalahi, Bib Paruhum; Fathiah, Nurul; Supriyo, Prapto Tri

doi:10.15642/mantik.2019.5.2.100-111

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…Optimization [10], [11], Artificial Bee Colony [12], Ant Colony [13], Crow Search Optimization [14], Cat Swarm Optimization [15], Grey Wolf Optimization [16], Whale Optimization [17]. The advantages/disadvantages of these methods are all well documented in literature.…”

Section: Other Methods Available Include Particle Swarmmentioning

confidence: 99%

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Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Jiménez-Gutiérrez¹,

Sánchez‐Partida²,

Martínez‐Flores³

et al. 2020

Adv. sci. technol. eng. syst. j.

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A distribution company in Mexico covers the travel expenses for 21 sales representatives. Currently, the routes they follow are not established clearly, which can lead to high costs in this subject. A reduction of such cost is sought after, by optimizing the routes for each one of them. The following research finds an improvement on the routes for the sales representative of a distribution company in Mexico. It was done by using the Traveling Salesman Problem with Hotel Selection or Base selections via a Simulated Annealing algorithm. The results show an improvement in a reasonable timeframe by using the Simulated Annealing. It also shows that the maximum process time was of 156.63 minutes, and the least amount of improvement was 24.44% over the current route selection. Applying this model will be beneficial for the company as the company is trying to reduce costs related to the sales representatives such as; fuel cost, hotel cost, and travel expenses.

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Section: Other Methods Available Include Particle Swarmmentioning

confidence: 99%

“…The advantages/disadvantages of these methods are all well documented in literature. Some of these methods have successfully been used to solve TSP [12], [13], [16]. And even some have been applied in combination with Simulated Annealing [11].…”

Section: Other Methods Available Include Particle Swarmmentioning

confidence: 99%

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Jiménez-Gutiérrez¹,

Sánchez‐Partida²,

Martínez‐Flores³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…ACO algorithm is an intelligent optimization algorithm (Silalahi et al , 2019), which has the characteristics of self-organization, heuristic search and strong robustness. ACO algorithms seek the optimal path by updating the Pheromone concentration.…”

Section: Dual Robot Collaborative Work Planningmentioning

confidence: 99%

Research on dual robot collaboration method based on improved double ant colony algorithm

Jia,

Tang,

Zhang

et al. 2024

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Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

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“…Traveling salesman problem (TSP) adalah suatu masalah yang dikaitkan pada penemuan jalur terpendek dari kemungkinan jalur -jalur yang ada pada suatu titik (node). TSP adalah permasalahan optimisasi untuk menemukan jalur terpendek dari satu node ke node lainnya [6]. Masingmasing node minimal dikunjungi sebanyak 1 kali untuk menemukan jalur atau rute terpendek.…”

Section: Iunclassified

Implementasi Traveling Salesman Problem pada Pemilihan Jalur ATM Locator Menggunakan Ant Colony Optimization

Utomo

Kurniawan

Ningrum

2021

JPIT

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Permasalahan utama Traveling Salesman Problem adalah penentuan solusi optimal dari beberapa jalur yang ditentukan. Pemilihan jalur ATM locator memiliki permasalahan yang sama dalam pemilihan jalur yang optimal. Penempatan ATM di berbagai titik lokasi dapat menyebabkan banyaknya kemungkinan pencarian lokasi ATM tersebut. Pengguna harus menelurusi jalur – jalur yang mungkin menjadi jalur terdekat dalam pencarian ATM locator. Berdasarkan permasalahan tersebut, penelitian ini mengusulkan Ant Colony Optimization (ACO) dalam menemukan solusi optimal pemilihan jalur optimal ATM locator. ACO adalah algoritma swarm intelligence yang mampu menyelesaikan masalah kombinasi tur pada TSP. pada tahap eksperimen, representasi titik lokasi ATM locator menggunakan koordinat latitude dan longitude. Penghitungan jarak antar titik lokasi menggunakan formula Haversine. Parameter τ0 yang digunakan adalah 0.5, 0.7 dan 0.9. parameter pheromone untuk menguji pemilihan jalur terdekat dan kebutuhan biaya yang sedikit. Eksperimen membuktikan bahwa ACO menemukan solusi optimal jalur terdekat dan biaya yang sedikit dengan pengaturan parameter a = 1, b = 2, ρ = 0.5, dan τ0 = 0.5.Level pheromone 0.5 mampu menemukan jalur terpendek 34.30 dibandingkan level pheromone 0.7 dan 0.9 dengan hasil 38.96 dan 39.05. level pheromone 0.5 juga menemukan kebutuhan biaya yang sedikit dibandingkan 0.7 dan 0.9.

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Use of Ant Colony Optimization Algorithm for Determining Traveling Salesman Problem Routes

Cited by 7 publications

References 16 publications

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Research on dual robot collaboration method based on improved double ant colony algorithm

Implementasi Traveling Salesman Problem pada Pemilihan Jalur ATM Locator Menggunakan Ant Colony Optimization

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