Two-layered ant colony system to improve engraving robot’s efficiency based on a large-scale TSP model

Zhou, Wu; Jun-jun, WU; Zhao, Mingbo; Liang, Feng; Liu, Kai

doi:10.1007/s00521-020-05468-4

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…Although the notion of using a clustering method to solve large-scale TSPs has developed and grown, the work mentioned above does not verify the algorithms' efficacy for TSPs with more than 10 5 nodes. Wu et al [7] investigated large-scale laser engraving in 2020, which is a widely used technology in modern production and can be represented as a TSP. They suggested a new two-layered ant colony system algorithm (TLACS) based on k-means, in which the ACS optimizes the visited order of clusters, and the start point and the end point for each cluster are determined.…”

Section: Of 33mentioning

confidence: 99%

“…In detail, if the algorithm is single-threaded, execute the instance on 20 cores simultaneously; if the algorithm is multi-threaded, run them one by one. The sources of GA, ACS [23], IGA, two-level genetic algorithm (TLGA) [47], TLACS [7], and ALC_IGA are published on GitHub 1 , and the instances involved are also on this repository.…”

Section: Experimental Settingmentioning

confidence: 99%

“…The population size of GA was set to 0.8 times the size of the instance and the mutation number was always set at three individuals. The parameters setting of ACS is as same as the literature [7]. Finally, the termination condition for the three compared algorithms is when there has been no improvement in the population for X iterations.…”

Section: Performance Comparison Of Iga Ga and Acsmentioning

confidence: 99%

“…This problem is equivalent to finding a Hamilton circuit with the minimum distance. The TSP, and its variants, such as asymmetric TSPs (ATSPs) [2], clustered TSPs (CTSPs) [3], dynamic TSPs (DTSPs) [4], multiple TSPs (MTSPs) [5], wandering salesman problems (WSPs) [6], have wide applications in laser engraving [7], integrated circuit design [8], transportation [9], energy saving [10], logistics problem [11], communication engineering [12], and medical waste transportation, which is closely related to COVID-19 pandemic [13]. The TSP was first considered in mathematical format in 1930 to solve a school bus routing problem, and then spread by researchers of Rand corporation.…”

Section: Introductionmentioning

confidence: 99%

“…The other one is to determine the start and end points for each small group after clustering firstly, and then solve the fixed start and end points TSPs, which is also called WSPs, finally combine all the groups [50]. These algorithms are much faster than algorithms without clustering and can solve 180K size TSP within a few hours [7].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Xu¹,

Lan²

2023

Preprint

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Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

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Section: Of 33mentioning

confidence: 99%